Statistical code
Topographic volume-standardization atlas of the human brain
Akeret et al. 2020
Setup
knitr::opts_chunk$set(message = FALSE, warning = FALSE, error = FALSE)
library(readxl)
library(biostatUZH)
library(dplyr)
library(tableone)
library(knitr)
library(captioner)
library(beeswarm)
library(readr)
library(ggplot2)
library(reshape2)
library(tidyr)
library(ggpubr)
library(graphics)
library(ggbeeswarm)
library(jcolors)
library(pals)
library(ggsci)
library(Hmisc)
library(stringr)
kableone <- function(x, ...) {
capture.output(x <- print(x))
knitr::kable(x, ...)
}
All.Volumes <- read_csv("Encephalic structures_volumes.csv")Parsed with column specification:
cols(
.default = col_double(),
Gender = [31mcol_character()[39m,
Handedness = [31mcol_character()[39m
)
See spec(...) for full column specifications.All.Volumes$ID <- as.factor(All.Volumes$ID)
All.Volumes$Gender <- as.factor(All.Volumes$Gender)
All.Volumes$Handedness <- as.factor(All.Volumes$Handedness)
Baseline Data
Baseline.data <- CreateTableOne(vars = c("Age (years)", "Handedness"),
data = All.Volumes, strat = c("Gender"))
Baseline.data <- print(Baseline.data) Stratified by Gender
f m p test
n 14 16
Age (years) (mean (SD)) 37.79 (13.04) 38.31 (16.91) 0.925
Handedness = right (%) 14 (100.0) 15 (93.8) 1.000
kable(Baseline.data)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Age (years) (mean (SD)) | 37.79 (13.04) | 38.31 (16.91) | 0.925 | |
| Handedness = right (%) | 14 (100.0) | 15 (93.8) | 1.000 |
NATotal encephalic volume (w/o ventricles)
summary(All.Volumes$`Total encephalic volume (without ventricles)`) Min. 1st Qu. Median Mean 3rd Qu. Max.
921292 999649 1108766 1093437 1187487 1274242 Total.encephalic.volume <- CreateTableOne(vars = c("Total encephalic volume (without ventricles)"),
data = All.Volumes)
Total.encephalic.volume.stratified.gender <- CreateTableOne(vars = c("Total encephalic volume (without ventricles)"),
strata = c("Gender"), data = All.Volumes)
Total.encephalic.volume <- print(Total.encephalic.volume)
Overall
n 30
Total encephalic volume (without ventricles) (mean (SD)) 1093437.27 (111353.18)Total.encephalic.volume.stratified.gender <- print(Total.encephalic.volume.stratified.gender) Stratified by Gender
f m p test
n 14 16
Total encephalic volume (without ventricles) (mean (SD)) 1024921.71 (89784.56) 1153388.38 (93652.70) 0.001
Total.encephalic.volume.RSD <- round((111353.18/1093437.27)*100,1)
Total.encephalic.volume.stratified.gender.female.RDS <- round((89784.56/1024921.71)*100,1)
Total.encephalic.volume.stratified.gender.male.RSD <- round((93652.70/1153388.38)*100,1)
kable(Total.encephalic.volume)
| Overall | |
|---|---|
| n | 30 |
| Total encephalic volume (without ventricles) (mean (SD)) | 1093437.27 (111353.18) |
kable(Total.encephalic.volume.RSD)
| x |
|---|
| 10.2 |
kable(Total.encephalic.volume.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Total encephalic volume (without ventricles) (mean (SD)) | 1024921.71 (89784.56) | 1153388.38 (93652.70) | 0.001 |
kable(Total.encephalic.volume.stratified.gender.female.RDS)
| x |
|---|
| 8.8 |
kable(Total.encephalic.volume.stratified.gender.male.RSD)
| x |
|---|
| 8.1 |
NA
Total.encephalic.volume.plot <- ggplot(All.Volumes, aes(x= Gender, y = `Total encephalic volume (without ventricles)`)) +
geom_quasirandom(aes(color = `Age (years)`), alpha = 1, size = 2, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, size = 0.3, width = 0.35, outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
xlab("Gender") + ylab("") +
theme_minimal() +
coord_flip() +
ggtitle("TOTAL ENCEPHALIC VOLUME (mm3)") +
theme(plot.title = element_text(hjust = 0.5))
Total.encephalic.volume.plot
ggsave("Total.encephalic.volume.plot.pdf", plot = Total.encephalic.volume.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.encephalic.volume.Age.plot <- ggplot(All.Volumes, aes(y=`Total encephalic volume (without ventricles)`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 0.8, shape = 16) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender, group = Gender), method='lm', se = F, alpha = 0.2,linetype = "longdash", size = 0.3, weight = 0.3) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
stat_cor(method = "pearson", label.y = 1250000, label.x = 65, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME encephalon") +
theme(plot.title = element_text(hjust = 0.5))
Total.encephalic.volume.Age.plot
ggsave("Total.encephalic.volume.Age.plot.pdf", plot = Total.encephalic.volume.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Topographic overview
Absolute Volumes
Frontal.lobe <- All.Volumes$`Total volume frontal pole`+
All.Volumes$`Total volume F1`+
All.Volumes$`Total volume F2`+
All.Volumes$`Total volume F3 orbital`+
All.Volumes$`Total volume F3 triangular`+
All.Volumes$`Total volume F3 opercular`+
All.Volumes$`Total volume anterior orbital`+
All.Volumes$`Total volume posterior orbital`+
All.Volumes$`Total volume medial orbital`+
All.Volumes$`Total volume lateral orbital`+
All.Volumes$`Total volume rectus`+
All.Volumes$`Total volume rostral`
Central.lobe <- (All.Volumes$`Total volume PreC`+
All.Volumes$`Total volume PostC`+
All.Volumes$`Total volume ParaC lobule`+
All.Volumes$`Total volume SubC gyrus`)
Parietal.lobe <- (All.Volumes$`Total volume SPL`+
All.Volumes$`Total volume SMG`+
All.Volumes$`Total volume ANG`+
All.Volumes$`Total volume Precuneus`)
Occipital.lobe <- (All.Volumes$`Total volume Cuneus`+
All.Volumes$`Total volume O1`+
All.Volumes$`Total volume O2`+
All.Volumes$`Total volume O3`+
All.Volumes$`Total volume occipital pole`+
All.Volumes$`Total volume lingual`)
Temporal.lobe <- (All.Volumes$`Total volume fusiform`+
All.Volumes$`Total volume T1`+
All.Volumes$`Total volume T2`+
All.Volumes$`Total volume T3`+
All.Volumes$`Total volume Planum temporale`+
All.Volumes$`Total volume Planum polare`+
All.Volumes$`Total volume temporal pole`)
Insular.lobe <- (All.Volumes$`Total volume short insular gyri`+
All.Volumes$`Total volume long insular gyri`)
Limbic.lobe <- (All.Volumes$`Total volume SCA`+
All.Volumes$`Total volume PHG`+
All.Volumes$`Total volume ant cingulate`+
All.Volumes$`Total volume mid cingulate`+
All.Volumes$`Total volume post cingulate`+
All.Volumes$`Total volume hippocampus` +
All.Volumes$`Total volume amygdala`)
Basal.ganglia <- (All.Volumes$`Total volume caudate`+
All.Volumes$`Total volume putamen`+
All.Volumes$`Total volume pallidum`)
Diencephalon <- (All.Volumes$`Total volume hypothalamus`+
All.Volumes$`Total volume thalamus`)
Brainstem <- (All.Volumes$`Total volume brainstem`)
Cerebellum <- (All.Volumes$`Total volume cerebellum`)
Topographic.overview.absolute <- as.data.frame(cbind(Frontal.lobe,
Central.lobe,
Parietal.lobe,
Occipital.lobe,
Temporal.lobe,
Insular.lobe,
Limbic.lobe,
Basal.ganglia,
Diencephalon,
Brainstem,
Cerebellum))
Topographic.overview.absolute$Gender <- All.Volumes$Gender
Topographic.overview.absolute$Age <- All.Volumes$`Age (years)`
Table.topographic.overview.absolute <- CreateTableOne(vars = c("Frontal.lobe","Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe","Limbic.lobe","Basal.ganglia", "Diencephalon","Brainstem","Cerebellum"),
data = Topographic.overview.absolute)
Table.topographic.overview.absolute.stratified.gender <- CreateTableOne(vars = c("Frontal.lobe","Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe","Limbic.lobe","Basal.ganglia", "Diencephalon","Brainstem","Cerebellum"),
strata = c("Gender"), data = Topographic.overview.absolute)
Table.topographic.overview.absolute <- print(Table.topographic.overview.absolute, contDigits = 10)
Overall
n 30
Frontal.lobe (mean (SD)) 199479.6000000000 (24675.6628190333)
Central.lobe (mean (SD)) 81517.1333333333 (9207.9403392916)
Parietal.lobe (mean (SD)) 129215.8000000000 (16770.1086592001)
Occipital.lobe (mean (SD)) 75910.6000000000 (8884.5868496884)
Temporal.lobe (mean (SD)) 116476.8666666667 (13867.9503836608)
Insular.lobe (mean (SD)) 24547.4333333333 (3014.6818175256)
Limbic.lobe (mean (SD)) 75816.2000000000 (9372.2558407689)
Basal.ganglia (mean (SD)) 22117.1000000000 (2978.7042930639)
Diencephalon (mean (SD)) 22568.8333333333 (2268.6469016504)
Brainstem (mean (SD)) 28072.4000000000 (3292.5882450929)
Cerebellum (mean (SD)) 116732.9666666667 (12623.3422659915)Table.topographic.overview.absolute.stratified.gender <- print(Table.topographic.overview.absolute.stratified.gender,contDigits = 10) Stratified by Gender
f m p test
n 14 16
Frontal.lobe (mean (SD)) 186347.1428571429 (19261.7710658549) 210970.5000000000 (23533.5837446545) 0.004
Central.lobe (mean (SD)) 76746.0714285714 (8368.0106308239) 85691.8125000000 (7962.2542303776) 0.006
Parietal.lobe (mean (SD)) 120829.0000000000 (15805.5035546288) 136554.2500000000 (14287.2790994880) 0.008
Occipital.lobe (mean (SD)) 70930.2142857143 (5286.0529724429) 80268.4375000000 (9218.7441080207) 0.002
Temporal.lobe (mean (SD)) 109341.7142857143 (9773.9892290993) 122720.1250000000 (14139.7363241564) 0.006
Insular.lobe (mean (SD)) 22746.3571428571 (2320.9156882291) 26123.3750000000 (2688.0316435389) 0.001
Limbic.lobe (mean (SD)) 71384.9285714286 (8462.9316293904) 79693.5625000000 (8566.6449439575) 0.013
Basal.ganglia (mean (SD)) 21022.8571428571 (2275.7467703234) 23074.5625000000 (3251.1485143715) 0.058
Diencephalon (mean (SD)) 21697.2857142857 (1746.7271121717) 23331.4375000000 (2444.7618280383) 0.047
Brainstem (mean (SD)) 26807.6428571429 (3260.5798117499) 29179.0625000000 (2991.0450229254) 0.047
Cerebellum (mean (SD)) 111616.6428571429 (12960.7820253560) 121209.7500000000 (10801.8819810871) 0.035 write.csv(Table.topographic.overview.absolute, "Table.topographic.overview.absolute.csv")
write.csv(Table.topographic.overview.absolute.stratified.gender, "Table.topographic.overview.absolute.stratified.gender.csv")
Table.topographic.overview.absolute.RSD <- as.data.frame(Table.topographic.overview.absolute)
Table.topographic.overview.absolute.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.topographic.overview.absolute.RSD[-1,]),
' (',fixed=TRUE)))
Table.topographic.overview.absolute.RSD <- data.frame(cbind(str_replace_all(Table.topographic.overview.absolute.RSD$X1, "[ ]", ""),
str_replace_all(Table.topographic.overview.absolute.RSD$X2, "[)]", "")))
Table.topographic.overview.absolute.RSD$X1 <- as.character(Table.topographic.overview.absolute.RSD$X1)
Table.topographic.overview.absolute.RSD$X2 <- as.character(Table.topographic.overview.absolute.RSD$X2)
Table.topographic.overview.absolute.RSD <- as.data.frame(sapply(Table.topographic.overview.absolute.RSD, as.numeric))
Table.topographic.overview.absolute.RSD <- as.data.frame(Table.topographic.overview.absolute.RSD$X2/Table.topographic.overview.absolute.RSD$X1)
Table.topographic.overview.absolute.RSD <- round(Table.topographic.overview.absolute.RSD * 100, 1)
Table.topographic.overview.absolute.stratified.gender.RSD <- as.data.frame(Table.topographic.overview.absolute.stratified.gender)
Table.topographic.overview.absolute.stratified.gender.RSD <- select(Table.topographic.overview.absolute.stratified.gender.RSD, - c(p, test))
Table.topographic.overview.absolute.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.topographic.overview.absolute.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.topographic.overview.absolute.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.topographic.overview.absolute.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.topographic.overview.absolute.stratified.gender.RSD.female$X2, "[)]", "")))
Table.topographic.overview.absolute.stratified.gender.RSD.female$X1 <- as.character(Table.topographic.overview.absolute.stratified.gender.RSD.female$X1)
Table.topographic.overview.absolute.stratified.gender.RSD.female$X2 <- as.character(Table.topographic.overview.absolute.stratified.gender.RSD.female$X2)
Table.topographic.overview.absolute.stratified.gender.RSD.female <- as.data.frame(sapply(Table.topographic.overview.absolute.stratified.gender.RSD.female, as.numeric))
Table.topographic.overview.absolute.stratified.gender.RSD.female <- as.data.frame(Table.topographic.overview.absolute.stratified.gender.RSD.female$X2/Table.topographic.overview.absolute.stratified.gender.RSD.female$X1)
Table.topographic.overview.absolute.stratified.gender.RSD.female <- round(Table.topographic.overview.absolute.stratified.gender.RSD.female * 100, 1)
Table.topographic.overview.absolute.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.topographic.overview.absolute.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.topographic.overview.absolute.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.topographic.overview.absolute.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.topographic.overview.absolute.stratified.gender.RSD.male$X2, "[)]", "")))
Table.topographic.overview.absolute.stratified.gender.RSD.male$X1 <- as.character(Table.topographic.overview.absolute.stratified.gender.RSD.male$X1)
Table.topographic.overview.absolute.stratified.gender.RSD.male$X2 <- as.character(Table.topographic.overview.absolute.stratified.gender.RSD.male$X2)
Table.topographic.overview.absolute.stratified.gender.RSD.male <- as.data.frame(sapply(Table.topographic.overview.absolute.stratified.gender.RSD.male, as.numeric))
Table.topographic.overview.absolute.stratified.gender.RSD.male <- as.data.frame(Table.topographic.overview.absolute.stratified.gender.RSD.male$X2/Table.topographic.overview.absolute.stratified.gender.RSD.male$X1)
Table.topographic.overview.absolute.stratified.gender.RSD.male <- round(Table.topographic.overview.absolute.stratified.gender.RSD.male * 100, 1)
kable(Table.topographic.overview.absolute)
| Overall | |
|---|---|
| n | 30 |
| Frontal.lobe (mean (SD)) | 199479.6000000000 (24675.6628190333) |
| Central.lobe (mean (SD)) | 81517.1333333333 (9207.9403392916) |
| Parietal.lobe (mean (SD)) | 129215.8000000000 (16770.1086592001) |
| Occipital.lobe (mean (SD)) | 75910.6000000000 (8884.5868496884) |
| Temporal.lobe (mean (SD)) | 116476.8666666667 (13867.9503836608) |
| Insular.lobe (mean (SD)) | 24547.4333333333 (3014.6818175256) |
| Limbic.lobe (mean (SD)) | 75816.2000000000 (9372.2558407689) |
| Basal.ganglia (mean (SD)) | 22117.1000000000 (2978.7042930639) |
| Diencephalon (mean (SD)) | 22568.8333333333 (2268.6469016504) |
| Brainstem (mean (SD)) | 28072.4000000000 (3292.5882450929) |
| Cerebellum (mean (SD)) | 116732.9666666667 (12623.3422659915) |
kable(Table.topographic.overview.absolute.RSD)
| Table.topographic.overview.absolute.RSDX2/Table.topographic.overview.absolute.RSDX1 |
|---|
| 12.4 |
| 11.3 |
| 13.0 |
| 11.7 |
| 11.9 |
| 12.3 |
| 12.4 |
| 13.5 |
| 10.1 |
| 11.7 |
| 10.8 |
kable(Table.topographic.overview.absolute.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Frontal.lobe (mean (SD)) | 186347.1428571429 (19261.7710658549) | 210970.5000000000 (23533.5837446545) | 0.004 | |
| Central.lobe (mean (SD)) | 76746.0714285714 (8368.0106308239) | 85691.8125000000 (7962.2542303776) | 0.006 | |
| Parietal.lobe (mean (SD)) | 120829.0000000000 (15805.5035546288) | 136554.2500000000 (14287.2790994880) | 0.008 | |
| Occipital.lobe (mean (SD)) | 70930.2142857143 (5286.0529724429) | 80268.4375000000 (9218.7441080207) | 0.002 | |
| Temporal.lobe (mean (SD)) | 109341.7142857143 (9773.9892290993) | 122720.1250000000 (14139.7363241564) | 0.006 | |
| Insular.lobe (mean (SD)) | 22746.3571428571 (2320.9156882291) | 26123.3750000000 (2688.0316435389) | 0.001 | |
| Limbic.lobe (mean (SD)) | 71384.9285714286 (8462.9316293904) | 79693.5625000000 (8566.6449439575) | 0.013 | |
| Basal.ganglia (mean (SD)) | 21022.8571428571 (2275.7467703234) | 23074.5625000000 (3251.1485143715) | 0.058 | |
| Diencephalon (mean (SD)) | 21697.2857142857 (1746.7271121717) | 23331.4375000000 (2444.7618280383) | 0.047 | |
| Brainstem (mean (SD)) | 26807.6428571429 (3260.5798117499) | 29179.0625000000 (2991.0450229254) | 0.047 | |
| Cerebellum (mean (SD)) | 111616.6428571429 (12960.7820253560) | 121209.7500000000 (10801.8819810871) | 0.035 |
kable(Table.topographic.overview.absolute.stratified.gender.RSD.female)
| Table.topographic.overview.absolute.stratified.gender.RSD.femaleX2/Table.topographic.overview.absolute.stratified.gender.RSD.femaleX1 |
|---|
| 10.3 |
| 10.9 |
| 13.1 |
| 7.5 |
| 8.9 |
| 10.2 |
| 11.9 |
| 10.8 |
| 8.1 |
| 12.2 |
| 11.6 |
kable(Table.topographic.overview.absolute.stratified.gender.RSD.male)
| Table.topographic.overview.absolute.stratified.gender.RSD.maleX2/Table.topographic.overview.absolute.stratified.gender.RSD.maleX1 |
|---|
| 11.2 |
| 9.3 |
| 10.5 |
| 11.5 |
| 11.5 |
| 10.3 |
| 10.7 |
| 14.1 |
| 10.5 |
| 10.3 |
| 8.9 |
NA
Topographic.overview.absolute <- select(Topographic.overview.absolute, - c(Gender, Age))
Topographic.overview.absolute.plotdata <- gather(Topographic.overview.absolute, "anatomical.structure", "relative.volume")
Topographic.overview.absolute.plotdata$Gender <- All.Volumes$Gender
Topographic.overview.absolute.plotdata$Age <- All.Volumes$`Age (years)`
Topographic.overview.absolute.plotdata$Gender <- factor(Topographic.overview.absolute.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Topographic.overview.absolute.plotdata$anatomical.structure <- factor(Topographic.overview.absolute.plotdata$anatomical.structure, levels = rev(c("Frontal.lobe", "Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe", "Limbic.lobe", "Basal.ganglia", "Diencephalon", "Brainstem", "Cerebellum")), rev(c("Frontal lobe", "Central lobe", "Parietal lobe", "Occipital lobe", "Temporal lobe", "Insular lobe", "Limbic lobe", "Basal ganglia", "Diencephalon", "Brainstem", "Cerebellum")))
Topographic.overview.absolute.plot <- ggplot(Topographic.overview.absolute.plotdata, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Absolute volume (in %)") +
theme_minimal() +
coord_flip() +
ggtitle("TOPOGRAPHIC OVERVIEW") +
theme(plot.title = element_text(hjust = 0.5))
Topographic.overview.absolute.plot
ggsave("Topographic.overview.absolute.plot.pdf", plot = Topographic.overview.absolute.plot, width = 12, height = 6, units = "in", dpi = 600)
Topographic.overview.absolute$Gender <- All.Volumes$Gender
Topographic.overview.absolute$Age <- All.Volumes$`Age (years)`
Total.frontal.lobe.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Frontal.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 230000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME frontal lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.frontal.lobe.Age.plot
ggsave("TO.Total.frontal.lobe.Age.plot.pdf", plot = Total.frontal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.central.lobe.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Central.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 97000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME central lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.central.lobe.Age.plot
ggsave("TO.Total.central.lobe.Age.plot.pdf", plot = Total.central.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.parietal.lobe.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Parietal.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 145000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME parietal lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.parietal.lobe.Age.plot
ggsave("TO.Total.parietal.lobe.Age.plot.pdf", plot = Total.parietal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.occipital.lobe.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Occipital.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 86000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME occipital lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.occipital.lobe.Age.plot
ggsave("TO.Total.occipital.lobe.Age.plot.pdf", plot = Total.occipital.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.temporal.lobe.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Temporal.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 86000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME temporal lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.temporal.lobe.Age.plot
ggsave("TO.Total.temporal.lobe.Age.plot.pdf", plot = Total.temporal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.insular.lobe.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Insular.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 29000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME insular lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.insular.lobe.Age.plot
ggsave("TO.Total.insular.lobe.Age.plot.pdf", plot = Total.insular.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.limbic.lobe.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Limbic.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 82000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME limbic lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.limbic.lobe.Age.plot
ggsave("TO.Total.limbic.lobe.Age.plot.pdf", plot = Total.limbic.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Basal.ganglia.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Basal.ganglia, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 24000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME basal ganglia") +
theme(plot.title = element_text(hjust = 0.5))
Total.Basal.ganglia.Age.plot
ggsave("TO.Total.Basal.ganglia.Age.plot.pdf", plot = Total.Basal.ganglia.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Diencephalon.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Diencephalon, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 25000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME diencephalon") +
theme(plot.title = element_text(hjust = 0.5))
Total.Diencephalon.Age.plot
ggsave("TO.Total.Diencephalon.Age.plot.pdf", plot = Total.Diencephalon.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Brainstem.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Brainstem, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 33000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME brainstem") +
theme(plot.title = element_text(hjust = 0.5))
Total.Brainstem.Age.plot
ggsave("TO.Total.Brainstem.Age.plot.pdf", plot = Total.Brainstem.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Cerebellum.Age.plot <- ggplot(Topographic.overview.absolute, aes(y=Cerebellum, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 122000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME cerebellum") +
theme(plot.title = element_text(hjust = 0.5))
Total.Cerebellum.Age.plot
ggsave("TO.Total.Cerebellum.Age.plot.pdf", plot = Total.Cerebellum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative Volumes
Topographic.overview.absolute <- select(Topographic.overview.absolute, - c(Gender, Age))
Topographic.overview.relative1 <- (100 * (Topographic.overview.absolute/All.Volumes$`Total encephalic volume (without ventricles)`))
Topographic.overview.relative2 <- CreateTableOne(vars = c("Frontal.lobe","Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe","Limbic.lobe","Basal.ganglia", "Diencephalon","Brainstem","Cerebellum"),
data = Topographic.overview.relative1)
Topographic.overview.relative1$Gender <- All.Volumes$Gender
Topographic.overview.relative.stratified.gender <- CreateTableOne(vars = c("Frontal.lobe","Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe","Limbic.lobe","Basal.ganglia", "Diencephalon","Brainstem","Cerebellum"),
strata = c("Gender"), data = Topographic.overview.relative1)
Topographic.overview.relative2 <- print(Topographic.overview.relative2, quote = FALSE, contDigits = 10)
Overall
n 30
Frontal.lobe (mean (SD)) 18.2262527269 (0.9597402604)
Central.lobe (mean (SD)) 7.4652325684 (0.5232606277)
Parietal.lobe (mean (SD)) 11.8153299902 (0.8890245686)
Occipital.lobe (mean (SD)) 6.9466013623 (0.4663487454)
Temporal.lobe (mean (SD)) 10.6576458547 (0.7070314916)
Insular.lobe (mean (SD)) 2.2473539433 (0.1834431574)
Limbic.lobe (mean (SD)) 6.9273443947 (0.3816087377)
Basal.ganglia (mean (SD)) 2.0288505705 (0.2308767019)
Diencephalon (mean (SD)) 2.0690086186 (0.1329050351)
Brainstem (mean (SD)) 2.5737214227 (0.2369790952)
Cerebellum (mean (SD)) 10.7318266204 (1.1655208385)Topographic.overview.relative.stratified.gender <- print(Topographic.overview.relative.stratified.gender, quote = FALSE, contDigits = 10) Stratified by Gender
f m p test
n 14 16
Frontal.lobe (mean (SD)) 18.1736795014 (0.7427106379) 18.2722542992 (1.1392494000) 0.784
Central.lobe (mean (SD)) 7.4860247454 (0.4299043550) 7.4470394134 (0.6069744522) 0.843
Parietal.lobe (mean (SD)) 11.7665136761 (0.7842958817) 11.8580442651 (0.9953713153) 0.784
Occipital.lobe (mean (SD)) 6.9414616026 (0.4870496255) 6.9510986521 (0.4634964636) 0.956
Temporal.lobe (mean (SD)) 10.6703456390 (0.3193451746) 10.6465335435 (0.9369070117) 0.929
Insular.lobe (mean (SD)) 2.2226928694 (0.1710334769) 2.2689323829 (0.1965780744) 0.501
Limbic.lobe (mean (SD)) 6.9547003947 (0.3570504302) 6.9034078946 (0.4120018041) 0.720
Basal.ganglia (mean (SD)) 2.0548417724 (0.1820952611) 2.0061082688 (0.2704344168) 0.573
Diencephalon (mean (SD)) 2.1204998650 (0.1057097871) 2.0239537780 (0.1408028261) 0.045
Brainstem (mean (SD)) 2.6191165363 (0.2600478612) 2.5340006983 (0.2153131486) 0.335
Cerebellum (mean (SD)) 10.9282585511 (1.2213015050) 10.5599486811 (1.1252066685) 0.397
Table.topographic.overview.relative.RSD <- as.data.frame(Topographic.overview.relative2)
Table.topographic.overview.relative.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.topographic.overview.relative.RSD[-1,]),
' (',fixed=TRUE)))
Table.topographic.overview.relative.RSD <- data.frame(cbind(str_replace_all(Table.topographic.overview.relative.RSD$X1, "[ ]", ""),
str_replace_all(Table.topographic.overview.relative.RSD$X2, "[)]", "")))
Table.topographic.overview.relative.RSD$X1 <- as.character(Table.topographic.overview.relative.RSD$X1)
Table.topographic.overview.relative.RSD$X2 <- as.character(Table.topographic.overview.relative.RSD$X2)
Table.topographic.overview.relative.RSD <- as.data.frame(sapply(Table.topographic.overview.relative.RSD, as.numeric))
Table.topographic.overview.relative.RSD <- as.data.frame(Table.topographic.overview.relative.RSD$X2/Table.topographic.overview.relative.RSD$X1)
Table.topographic.overview.relative.RSD <- round(Table.topographic.overview.relative.RSD*100, 1)
Table.topographic.overview.relative.stratified.gender.RSD <- as.data.frame(Topographic.overview.relative.stratified.gender)
Table.topographic.overview.relative.stratified.gender.RSD <- select(Table.topographic.overview.relative.stratified.gender.RSD, - c(p, test))
Table.topographic.overview.relative.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.topographic.overview.relative.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.topographic.overview.relative.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.topographic.overview.relative.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.topographic.overview.relative.stratified.gender.RSD.female$X2, "[)]", "")))
Table.topographic.overview.relative.stratified.gender.RSD.female$X1 <- as.character(Table.topographic.overview.relative.stratified.gender.RSD.female$X1)
Table.topographic.overview.relative.stratified.gender.RSD.female$X2 <- as.character(Table.topographic.overview.relative.stratified.gender.RSD.female$X2)
Table.topographic.overview.relative.stratified.gender.RSD.female <- as.data.frame(sapply(Table.topographic.overview.relative.stratified.gender.RSD.female, as.numeric))
Table.topographic.overview.relative.stratified.gender.RSD.female <- as.data.frame(Table.topographic.overview.relative.stratified.gender.RSD.female$X2/Table.topographic.overview.relative.stratified.gender.RSD.female$X1)
Table.topographic.overview.relative.stratified.gender.RSD.female <- round(Table.topographic.overview.relative.stratified.gender.RSD.female*100, 1)
Table.topographic.overview.relative.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.topographic.overview.relative.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.topographic.overview.relative.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.topographic.overview.relative.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.topographic.overview.relative.stratified.gender.RSD.male$X2, "[)]", "")))
Table.topographic.overview.relative.stratified.gender.RSD.male$X1 <- as.character(Table.topographic.overview.relative.stratified.gender.RSD.male$X1)
Table.topographic.overview.relative.stratified.gender.RSD.male$X2 <- as.character(Table.topographic.overview.relative.stratified.gender.RSD.male$X2)
Table.topographic.overview.relative.stratified.gender.RSD.male <- as.data.frame(sapply(Table.topographic.overview.relative.stratified.gender.RSD.male, as.numeric))
Table.topographic.overview.relative.stratified.gender.RSD.male <- as.data.frame(Table.topographic.overview.relative.stratified.gender.RSD.male$X2/Table.topographic.overview.relative.stratified.gender.RSD.male$X1)
Table.topographic.overview.relative.stratified.gender.RSD.male <- round(Table.topographic.overview.relative.stratified.gender.RSD.male*100, 1)
kable(Topographic.overview.relative2)
| Overall | |
|---|---|
| n | 30 |
| Frontal.lobe (mean (SD)) | 18.2262527269 (0.9597402604) |
| Central.lobe (mean (SD)) | 7.4652325684 (0.5232606277) |
| Parietal.lobe (mean (SD)) | 11.8153299902 (0.8890245686) |
| Occipital.lobe (mean (SD)) | 6.9466013623 (0.4663487454) |
| Temporal.lobe (mean (SD)) | 10.6576458547 (0.7070314916) |
| Insular.lobe (mean (SD)) | 2.2473539433 (0.1834431574) |
| Limbic.lobe (mean (SD)) | 6.9273443947 (0.3816087377) |
| Basal.ganglia (mean (SD)) | 2.0288505705 (0.2308767019) |
| Diencephalon (mean (SD)) | 2.0690086186 (0.1329050351) |
| Brainstem (mean (SD)) | 2.5737214227 (0.2369790952) |
| Cerebellum (mean (SD)) | 10.7318266204 (1.1655208385) |
kable(Table.topographic.overview.relative.RSD)
| Table.topographic.overview.relative.RSDX2/Table.topographic.overview.relative.RSDX1 |
|---|
| 5.3 |
| 7.0 |
| 7.5 |
| 6.7 |
| 6.6 |
| 8.2 |
| 5.5 |
| 11.4 |
| 6.4 |
| 9.2 |
| 10.9 |
kable(Topographic.overview.relative.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Frontal.lobe (mean (SD)) | 18.1736795014 (0.7427106379) | 18.2722542992 (1.1392494000) | 0.784 | |
| Central.lobe (mean (SD)) | 7.4860247454 (0.4299043550) | 7.4470394134 (0.6069744522) | 0.843 | |
| Parietal.lobe (mean (SD)) | 11.7665136761 (0.7842958817) | 11.8580442651 (0.9953713153) | 0.784 | |
| Occipital.lobe (mean (SD)) | 6.9414616026 (0.4870496255) | 6.9510986521 (0.4634964636) | 0.956 | |
| Temporal.lobe (mean (SD)) | 10.6703456390 (0.3193451746) | 10.6465335435 (0.9369070117) | 0.929 | |
| Insular.lobe (mean (SD)) | 2.2226928694 (0.1710334769) | 2.2689323829 (0.1965780744) | 0.501 | |
| Limbic.lobe (mean (SD)) | 6.9547003947 (0.3570504302) | 6.9034078946 (0.4120018041) | 0.720 | |
| Basal.ganglia (mean (SD)) | 2.0548417724 (0.1820952611) | 2.0061082688 (0.2704344168) | 0.573 | |
| Diencephalon (mean (SD)) | 2.1204998650 (0.1057097871) | 2.0239537780 (0.1408028261) | 0.045 | |
| Brainstem (mean (SD)) | 2.6191165363 (0.2600478612) | 2.5340006983 (0.2153131486) | 0.335 | |
| Cerebellum (mean (SD)) | 10.9282585511 (1.2213015050) | 10.5599486811 (1.1252066685) | 0.397 |
kable(Table.topographic.overview.relative.stratified.gender.RSD.female)
| Table.topographic.overview.relative.stratified.gender.RSD.femaleX2/Table.topographic.overview.relative.stratified.gender.RSD.femaleX1 |
|---|
| 4.1 |
| 5.7 |
| 6.7 |
| 7.0 |
| 3.0 |
| 7.7 |
| 5.1 |
| 8.9 |
| 5.0 |
| 9.9 |
| 11.2 |
kable(Table.topographic.overview.relative.stratified.gender.RSD.male)
| Table.topographic.overview.relative.stratified.gender.RSD.maleX2/Table.topographic.overview.relative.stratified.gender.RSD.maleX1 |
|---|
| 6.2 |
| 8.2 |
| 8.4 |
| 6.7 |
| 8.8 |
| 8.7 |
| 6.0 |
| 13.5 |
| 7.0 |
| 8.5 |
| 10.7 |
NA
Topographic.overview.relative1 <- select(Topographic.overview.relative1, - c(Gender))
Topographic.overview.relative.plotdata <- gather(Topographic.overview.relative1, "anatomical.structure", "relative.volume")
Topographic.overview.relative.plotdata$Gender <- All.Volumes$Gender
Topographic.overview.relative.plotdata$Age <- All.Volumes$`Age (years)`
Topographic.overview.relative.plotdata$Gender <- factor(Topographic.overview.relative.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Topographic.overview.relative.plotdata$anatomical.structure <- factor(Topographic.overview.relative.plotdata$anatomical.structure, levels = rev(c("Frontal.lobe", "Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe", "Limbic.lobe", "Basal.ganglia", "Diencephalon", "Brainstem", "Cerebellum")), rev(c("Frontal lobe", "Central lobe", "Parietal lobe", "Occipital lobe", "Temporal lobe", "Insular lobe", "Limbic lobe", "Basal ganglia", "Diencephalon", "Brainstem", "Cerebellum")))
Topographic.overview.relative.plot <- ggplot(Topographic.overview.relative.plotdata, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Relative volume (in %)") +
theme_minimal() +
coord_flip() +
ggtitle("TOPOGRAPHIC OVERVIEW") +
theme(plot.title = element_text(hjust = 0.5))
Topographic.overview.relative.plot
ggsave("Topographic.overview.relative.plot.pdf", plot = Topographic.overview.relative.plot, width = 12, height = 6, units = "in", dpi = 600)
Topographic.overview.relative1$Gender <- All.Volumes$Gender
Topographic.overview.relative1$Age <- All.Volumes$`Age (years)`
Relative.frontal.lobe.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Frontal.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 19.3, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME frontal lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.frontal.lobe.Age.plot
ggsave("TOR.Relative.frontal.lobe.Age.plot.pdf", plot = Relative.frontal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Central.lobe.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Central.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 9.7, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME central lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Central.lobe.Age.plot
ggsave("TOR.Relative.Central.lobe.Age.plot.pdf", plot = Relative.Central.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Parietal.lobe.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Parietal.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 13.2, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME parietal lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Parietal.lobe.Age.plot
ggsave("TOR.Relative.Parietal.lobe.Age.plot.pdf", plot = Relative.Parietal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Occipital.lobe.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Occipital.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 7.6, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME occipital lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Occipital.lobe.Age.plot
ggsave("TOR.Relative.Occipital.lobe.Age.plot.pdf", plot = Relative.Occipital.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Temporal.lobe.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Temporal.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 8.6, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME temporal lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Temporal.lobe.Age.plot
ggsave("TOR.Relative.Temporal.lobe.Age.plot.pdf", plot = Relative.Temporal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Insular.lobe.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Insular.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.05, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME insular lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Insular.lobe.Age.plot
ggsave("TOR.Relative.Insular.lobe.Age.plot.pdf", plot = Relative.Insular.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Limbic.lobe.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Limbic.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 7.7, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME limbic lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Limbic.lobe.Age.plot
ggsave("TOR.Relative.Limbic.lobe.Age.plot.pdf", plot = Relative.Limbic.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Basal.ganglia.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Basal.ganglia, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.25, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME basal ganglia") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Basal.ganglia.Age.plot
ggsave("TOR.Relative.Basal.ganglia.Age.plot.pdf", plot = Relative.Basal.ganglia.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Diencephalon.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Diencephalon, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.25, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME diencephalon") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Diencephalon.Age.plot
ggsave("TOR.Relative.Diencephalon.Age.plot.pdf", plot = Relative.Diencephalon.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Brainstem.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Brainstem, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3.18, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME brainstem") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Brainstem.Age.plot
ggsave("TOR.Relative.Brainstem.Age.plot.pdf", plot = Relative.Brainstem.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Cerebellum.Age.plot <- ggplot(Topographic.overview.relative1, aes(y=Cerebellum, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 12.7, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME cerebellum") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Cerebellum.Age.plot
ggsave("TOR.Relative.Cerebellum.Age.plot.pdf", plot = Relative.Cerebellum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
The Prosencephalon
Absolute Volumes
Frontal.pole <- All.Volumes$`Total volume frontal pole`
F1 <- All.Volumes$`Total volume F1`
F2 <- All.Volumes$`Total volume F2`
F3.orbital <- All.Volumes$`Total volume F3 orbital`
F3.triangular <- All.Volumes$`Total volume F3 triangular`
F3.opercular <- All.Volumes$`Total volume F3 opercular`
Anterior.orbital <- All.Volumes$`Total volume anterior orbital`
Medial.orbital <- All.Volumes$`Total volume medial orbital`
Lateral.orbital <- All.Volumes$`Total volume lateral orbital`
Posterior.orbital <- All.Volumes$`Total volume posterior orbital`
Rectus <- All.Volumes$`Total volume rectus`
Rostral <- All.Volumes$`Total volume rostral`
Precentral <- All.Volumes$`Total volume PreC`
Postcentral <- All.Volumes$`Total volume PostC`
Paracentral.lobule <- All.Volumes$`Total volume ParaC lobule`
Subcentral <- All.Volumes$`Total volume SubC gyrus`
SPL <- All.Volumes$`Total volume SPL`
SMG <- All.Volumes$`Total volume SMG`
ANG <- All.Volumes$`Total volume ANG`
Precuneus <- All.Volumes$`Total volume Precuneus`
Cuneus <- All.Volumes$`Total volume Cuneus`
O1 <- All.Volumes$`Total volume O1`
O2 <- All.Volumes$`Total volume O2`
O3 <- All.Volumes$`Total volume O3`
Occipital.pole <- All.Volumes$`Total volume occipital pole`
Lingual <- All.Volumes$`Total volume lingual`
Fusiform <- All.Volumes$`Total volume fusiform`
Temporal.pole <- All.Volumes$`Total volume T1`
T1 <- All.Volumes$`Total volume T2`
T2 <- All.Volumes$`Total volume T3`
T3 <- All.Volumes$`Total volume Planum temporale`
Planum.temporale <- All.Volumes$`Total volume Planum polare`
Planum.polare <- All.Volumes$`Total volume temporal pole`
Short.insular <- All.Volumes$`Total volume short insular gyri`
Long.insular <- All.Volumes$`Total volume long insular gyri`
SCA <- All.Volumes$`Total volume SCA`
Cingulate.anterior <- All.Volumes$`Total volume ant cingulate`
Cingulate.middle <- All.Volumes$`Total volume mid cingulate`
Cingulate.posterior <- All.Volumes$`Total volume post cingulate`
PHG <- All.Volumes$`Total volume PHG`
Hippocampus <- All.Volumes$`Total volume hippocampus`
Amygdala <- All.Volumes$`Total volume amygdala`
Corpus.callosum <- All.Volumes$`Total volume corpus callosum`
Claustrum <- All.Volumes$`Total volume claustrum`
Putamen <- All.Volumes$`Total volume putamen`
Caudate <- All.Volumes$`Total volume caudate`
Globus.pallidum <- All.Volumes$`Total volume pallidum`
Internal.capsule <- All.Volumes$`Total volume internal capsule`
Innominate.substance <- All.Volumes$`Total volume substantia innominata`
Hypothalamus <- All.Volumes$`Total volume hypothalamus`
Thalamus <- All.Volumes$`Total volume thalamus`
Prosencephalon.absolute <- as.data.frame(cbind(
Frontal.pole,
F1,
F2,
F3.orbital,
F3.triangular,
F3.opercular,
Anterior.orbital,
Medial.orbital,
Lateral.orbital,
Posterior.orbital,
Rectus,
Rostral,
Precentral,
Postcentral,
Paracentral.lobule,
Subcentral,
SPL,
SMG,
ANG,
Precuneus,
Cuneus,
O1,
O2,
O3,
Occipital.pole,
Lingual,
Fusiform,
Temporal.pole,
T1,
T2,
T3,
Planum.temporale,
Planum.polare,
Short.insular,
Long.insular,
SCA,
Cingulate.anterior,
Cingulate.middle,
Cingulate.posterior,
PHG,
Hippocampus,
Amygdala,
Corpus.callosum,
Claustrum,
Putamen,
Caudate,
Globus.pallidum,
Internal.capsule,
Innominate.substance,
Hypothalamus,
Thalamus
))
Prosencephalon.absolute$Gender <- All.Volumes$Gender
Table.Prosencephalon.absolute <- CreateTableOne(
vars = c("Frontal.pole",
"F1",
"F2",
"F3.orbital",
"F3.triangular",
"F3.opercular",
"Anterior.orbital",
"Medial.orbital",
"Lateral.orbital",
"Posterior.orbital",
"Rectus",
"Rostral",
"Precentral",
"Postcentral",
"Paracentral.lobule",
"Subcentral",
"SPL",
"SMG",
"ANG",
"Precuneus",
"Cuneus",
"O1",
"O2",
"O3",
"Occipital.pole",
"Lingual",
"Fusiform",
"Temporal.pole",
"T1",
"T2",
"T3",
"Planum.temporale",
"Planum.polare",
"Short.insular",
"Long.insular",
"SCA",
"Cingulate.anterior",
"Cingulate.middle",
"Cingulate.posterior",
"PHG",
"Hippocampus",
"Amygdala",
"Corpus.callosum",
"Claustrum",
"Putamen",
"Caudate",
"Globus.pallidum",
"Internal.capsule",
"Innominate.substance",
"Hypothalamus",
"Thalamus"),
data = Prosencephalon.absolute)
Table.Prosencephalon.absolute.stratified.gender <- CreateTableOne(
vars = c("Frontal.pole",
"F1",
"F2",
"F3.orbital",
"F3.triangular",
"F3.opercular",
"Anterior.orbital",
"Medial.orbital",
"Lateral.orbital",
"Posterior.orbital",
"Rectus",
"Rostral",
"Precentral",
"Postcentral",
"Paracentral.lobule",
"Subcentral",
"SPL",
"SMG",
"ANG",
"Precuneus",
"Cuneus",
"O1",
"O2",
"O3",
"Occipital.pole",
"Lingual",
"Fusiform",
"Temporal.pole",
"T1",
"T2",
"T3",
"Planum.temporale",
"Planum.polare",
"Short.insular",
"Long.insular",
"SCA",
"Cingulate.anterior",
"Cingulate.middle",
"Cingulate.posterior",
"PHG",
"Hippocampus",
"Amygdala",
"Corpus.callosum",
"Claustrum",
"Putamen",
"Caudate",
"Globus.pallidum",
"Internal.capsule",
"Innominate.substance",
"Hypothalamus",
"Thalamus"),
strata = c("Gender"),
data = Prosencephalon.absolute)
Table.Prosencephalon.absolute <- print(Table.Prosencephalon.absolute, contDigits = 2)
Overall
n 30
Frontal.pole (mean (SD)) 4710.60 (976.19)
F1 (mean (SD)) 68776.43 (9196.02)
F2 (mean (SD)) 58458.30 (7689.55)
F3.orbital (mean (SD)) 3916.57 (613.82)
F3.triangular (mean (SD)) 11688.77 (1898.88)
F3.opercular (mean (SD)) 13949.13 (2022.23)
Anterior.orbital (mean (SD)) 3349.47 (604.18)
Medial.orbital (mean (SD)) 7566.23 (1369.20)
Lateral.orbital (mean (SD)) 6063.57 (1102.27)
Posterior.orbital (mean (SD)) 7420.67 (1341.23)
Rectus (mean (SD)) 9986.63 (4584.25)
Rostral (mean (SD)) 3593.23 (379.75)
Precentral (mean (SD)) 39114.37 (4348.35)
Postcentral (mean (SD)) 22307.97 (3280.88)
Paracentral.lobule (mean (SD)) 14036.93 (1769.85)
Subcentral (mean (SD)) 6057.87 (1027.51)
SPL (mean (SD)) 33315.33 (4452.27)
SMG (mean (SD)) 30707.70 (4785.83)
ANG (mean (SD)) 34333.43 (4760.76)
Precuneus (mean (SD)) 30859.33 (4302.66)
Cuneus (mean (SD)) 10063.17 (1461.83)
O1 (mean (SD)) 9149.23 (1312.27)
O2 (mean (SD)) 15786.67 (2399.63)
O3 (mean (SD)) 9263.87 (1668.79)
Occipital.pole (mean (SD)) 11367.23 (1511.90)
Lingual (mean (SD)) 20280.43 (2733.84)
Fusiform (mean (SD)) 22196.20 (3298.55)
Temporal.pole (mean (SD)) 25353.77 (3578.13)
T1 (mean (SD)) 27247.23 (3925.33)
T2 (mean (SD)) 23693.10 (3139.43)
T3 (mean (SD)) 5010.53 (871.47)
Planum.temporale (mean (SD)) 3078.07 (770.86)
Planum.polare (mean (SD)) 9897.97 (1847.85)
Short.insular (mean (SD)) 15405.47 (2108.68)
Long.insular (mean (SD)) 9141.97 (1881.34)
SCA (mean (SD)) 2394.33 (662.65)
Cingulate.anterior (mean (SD)) 14419.77 (2135.23)
Cingulate.middle (mean (SD)) 17135.30 (2613.59)
Cingulate.posterior (mean (SD)) 20174.30 (2957.84)
PHG (mean (SD)) 10443.13 (1652.70)
Hippocampus (mean (SD)) 8127.77 (952.70)
Amygdala (mean (SD)) 3121.60 (469.63)
Corpus.callosum (mean (SD)) 3311.90 (552.40)
Claustrum (mean (SD)) 1344.90 (417.64)
Putamen (mean (SD)) 11264.80 (1661.91)
Caudate (mean (SD)) 7779.73 (1318.33)
Globus.pallidum (mean (SD)) 3072.57 (592.30)
Internal.capsule (mean (SD)) 10616.47 (1548.67)
Innominate.substance (mean (SD)) 2687.93 (322.72)
Hypothalamus (mean (SD)) 7962.50 (917.36)
Thalamus (mean (SD)) 14606.33 (1462.98)Table.Prosencephalon.absolute.stratified.gender <- print(Table.Prosencephalon.absolute.stratified.gender, contDigits = 2) Stratified by Gender
f m p test
n 14 16
Frontal.pole (mean (SD)) 4369.21 (705.73) 5009.31 (1098.53) 0.072
F1 (mean (SD)) 64718.00 (8455.20) 72327.56 (8527.23) 0.021
F2 (mean (SD)) 55366.50 (7297.68) 61163.62 (7171.64) 0.037
F3.orbital (mean (SD)) 3665.71 (565.37) 4136.06 (584.21) 0.034
F3.triangular (mean (SD)) 10845.50 (1038.66) 12426.62 (2189.00) 0.020
F3.opercular (mean (SD)) 12752.21 (1540.15) 14996.44 (1828.47) 0.001
Anterior.orbital (mean (SD)) 3087.00 (244.87) 3579.12 (730.22) 0.023
Medial.orbital (mean (SD)) 6973.14 (558.72) 8085.19 (1654.78) 0.024
Lateral.orbital (mean (SD)) 5590.57 (456.44) 6477.44 (1333.01) 0.025
Posterior.orbital (mean (SD)) 6845.21 (546.20) 7924.19 (1624.75) 0.025
Rectus (mean (SD)) 8721.00 (747.46) 11094.06 (6110.83) 0.161
Rostral (mean (SD)) 3413.07 (295.50) 3750.88 (382.52) 0.012
Precentral (mean (SD)) 36944.14 (3427.70) 41013.31 (4258.04) 0.008
Postcentral (mean (SD)) 20938.00 (3435.44) 23506.69 (2701.43) 0.030
Paracentral.lobule (mean (SD)) 13361.57 (1785.89) 14627.88 (1579.10) 0.049
Subcentral (mean (SD)) 5502.36 (885.64) 6543.94 (906.29) 0.004
SPL (mean (SD)) 31813.43 (4849.58) 34629.50 (3740.81) 0.084
SMG (mean (SD)) 28640.79 (4365.75) 32516.25 (4504.07) 0.024
ANG (mean (SD)) 31864.79 (4552.43) 36493.50 (3897.76) 0.006
Precuneus (mean (SD)) 28510.00 (3788.70) 32915.00 (3700.31) 0.003
Cuneus (mean (SD)) 9355.07 (1040.46) 10682.75 (1521.76) 0.010
O1 (mean (SD)) 8298.50 (925.51) 9893.62 (1149.08) <0.001
O2 (mean (SD)) 14683.57 (1913.46) 16751.88 (2414.54) 0.016
O3 (mean (SD)) 8261.00 (1177.06) 10141.38 (1556.68) 0.001
Occipital.pole (mean (SD)) 10666.64 (1017.58) 11980.25 (1631.86) 0.015
Lingual (mean (SD)) 19665.43 (2166.02) 20818.56 (3117.94) 0.256
Fusiform (mean (SD)) 20947.07 (2857.52) 23289.19 (3350.86) 0.050
Temporal.pole (mean (SD)) 23479.07 (2923.96) 26994.12 (3345.51) 0.005
T1 (mean (SD)) 24988.57 (2642.33) 29223.56 (3848.44) 0.002
T2 (mean (SD)) 22875.64 (2875.80) 24408.38 (3273.84) 0.187
T3 (mean (SD)) 4531.00 (734.14) 5430.12 (773.80) 0.003
Planum.temporale (mean (SD)) 2714.57 (409.79) 3396.12 (878.68) 0.013
Planum.polare (mean (SD)) 9805.79 (1645.78) 9978.62 (2058.92) 0.803
Short.insular (mean (SD)) 14652.79 (1336.31) 16064.06 (2461.22) 0.066
Long.insular (mean (SD)) 8093.57 (1221.68) 10059.31 (1904.19) 0.003
SCA (mean (SD)) 2108.21 (542.11) 2644.69 (671.54) 0.024
Cingulate.anterior (mean (SD)) 13500.14 (2193.65) 15224.44 (1778.78) 0.025
Cingulate.middle (mean (SD)) 16044.50 (2245.99) 18089.75 (2598.50) 0.030
Cingulate.posterior (mean (SD)) 18885.71 (2679.94) 21301.81 (2790.00) 0.023
PHG (mean (SD)) 10013.00 (1197.43) 10819.50 (1927.25) 0.187
Hippocampus (mean (SD)) 7823.93 (712.09) 8393.62 (1074.12) 0.103
Amygdala (mean (SD)) 3009.43 (469.08) 3219.75 (462.25) 0.227
Corpus.callosum (mean (SD)) 3319.00 (580.49) 3305.69 (545.72) 0.949
Claustrum (mean (SD)) 1189.93 (305.72) 1480.50 (462.80) 0.056
Putamen (mean (SD)) 10793.64 (1243.13) 11677.06 (1900.52) 0.149
Caudate (mean (SD)) 7368.07 (931.75) 8139.94 (1520.25) 0.111
Globus.pallidum (mean (SD)) 2861.14 (330.56) 3257.56 (710.86) 0.066
Internal.capsule (mean (SD)) 10153.14 (1654.93) 11021.88 (1373.90) 0.127
Innominate.substance (mean (SD)) 2639.07 (362.97) 2730.69 (288.08) 0.448
Hypothalamus (mean (SD)) 7507.07 (671.29) 8361.00 (934.61) 0.008
Thalamus (mean (SD)) 14190.21 (1205.08) 14970.44 (1605.09) 0.148 write.csv(Table.Prosencephalon.absolute, "Table.Prosencephalon.absolute.csv")
write.csv(Table.Prosencephalon.absolute.stratified.gender, "Table.Prosencephalon.absolute.stratified.gender.csv")
Table.Prosencephalon.absolute.RSD <- as.data.frame(Table.Prosencephalon.absolute)
Table.Prosencephalon.absolute.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.Prosencephalon.absolute.RSD[-1,]),
' (',fixed=TRUE)))
Table.Prosencephalon.absolute.RSD <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.RSD$X1, "[ ]", ""),
str_replace_all(Table.Prosencephalon.absolute.RSD$X2, "[)]", "")))
Table.Prosencephalon.absolute.RSD$X1 <- as.character(Table.Prosencephalon.absolute.RSD$X1)
Table.Prosencephalon.absolute.RSD$X2 <- as.character(Table.Prosencephalon.absolute.RSD$X2)
Table.Prosencephalon.absolute.RSD <- as.data.frame(sapply(Table.Prosencephalon.absolute.RSD, as.numeric))
Table.Prosencephalon.absolute.RSD <- as.data.frame(Table.Prosencephalon.absolute.RSD$X2/Table.Prosencephalon.absolute.RSD$X1)
Table.Prosencephalon.absolute.RSD <- round(Table.Prosencephalon.absolute.RSD * 100, 1)
Table.Prosencephalon.absolute.stratified.gender.RSD <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender)
Table.Prosencephalon.absolute.stratified.gender.RSD <- select(Table.Prosencephalon.absolute.stratified.gender.RSD, - c(p, test))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.Prosencephalon.absolute.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2)
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Prosencephalon.absolute.stratified.gender.RSD.female, as.numeric))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2/Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- round(Table.Prosencephalon.absolute.stratified.gender.RSD.female * 100, 1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.Prosencephalon.absolute.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Prosencephalon.absolute.stratified.gender.RSD.male, as.numeric))
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2/Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- round(Table.Prosencephalon.absolute.stratified.gender.RSD.male * 100, 1)
kable(Table.Prosencephalon.absolute)
| Overall | |
|---|---|
| n | 30 |
| Frontal.pole (mean (SD)) | 4710.60 (976.19) |
| F1 (mean (SD)) | 68776.43 (9196.02) |
| F2 (mean (SD)) | 58458.30 (7689.55) |
| F3.orbital (mean (SD)) | 3916.57 (613.82) |
| F3.triangular (mean (SD)) | 11688.77 (1898.88) |
| F3.opercular (mean (SD)) | 13949.13 (2022.23) |
| Anterior.orbital (mean (SD)) | 3349.47 (604.18) |
| Medial.orbital (mean (SD)) | 7566.23 (1369.20) |
| Lateral.orbital (mean (SD)) | 6063.57 (1102.27) |
| Posterior.orbital (mean (SD)) | 7420.67 (1341.23) |
| Rectus (mean (SD)) | 9986.63 (4584.25) |
| Rostral (mean (SD)) | 3593.23 (379.75) |
| Precentral (mean (SD)) | 39114.37 (4348.35) |
| Postcentral (mean (SD)) | 22307.97 (3280.88) |
| Paracentral.lobule (mean (SD)) | 14036.93 (1769.85) |
| Subcentral (mean (SD)) | 6057.87 (1027.51) |
| SPL (mean (SD)) | 33315.33 (4452.27) |
| SMG (mean (SD)) | 30707.70 (4785.83) |
| ANG (mean (SD)) | 34333.43 (4760.76) |
| Precuneus (mean (SD)) | 30859.33 (4302.66) |
| Cuneus (mean (SD)) | 10063.17 (1461.83) |
| O1 (mean (SD)) | 9149.23 (1312.27) |
| O2 (mean (SD)) | 15786.67 (2399.63) |
| O3 (mean (SD)) | 9263.87 (1668.79) |
| Occipital.pole (mean (SD)) | 11367.23 (1511.90) |
| Lingual (mean (SD)) | 20280.43 (2733.84) |
| Fusiform (mean (SD)) | 22196.20 (3298.55) |
| Temporal.pole (mean (SD)) | 25353.77 (3578.13) |
| T1 (mean (SD)) | 27247.23 (3925.33) |
| T2 (mean (SD)) | 23693.10 (3139.43) |
| T3 (mean (SD)) | 5010.53 (871.47) |
| Planum.temporale (mean (SD)) | 3078.07 (770.86) |
| Planum.polare (mean (SD)) | 9897.97 (1847.85) |
| Short.insular (mean (SD)) | 15405.47 (2108.68) |
| Long.insular (mean (SD)) | 9141.97 (1881.34) |
| SCA (mean (SD)) | 2394.33 (662.65) |
| Cingulate.anterior (mean (SD)) | 14419.77 (2135.23) |
| Cingulate.middle (mean (SD)) | 17135.30 (2613.59) |
| Cingulate.posterior (mean (SD)) | 20174.30 (2957.84) |
| PHG (mean (SD)) | 10443.13 (1652.70) |
| Hippocampus (mean (SD)) | 8127.77 (952.70) |
| Amygdala (mean (SD)) | 3121.60 (469.63) |
| Corpus.callosum (mean (SD)) | 3311.90 (552.40) |
| Claustrum (mean (SD)) | 1344.90 (417.64) |
| Putamen (mean (SD)) | 11264.80 (1661.91) |
| Caudate (mean (SD)) | 7779.73 (1318.33) |
| Globus.pallidum (mean (SD)) | 3072.57 (592.30) |
| Internal.capsule (mean (SD)) | 10616.47 (1548.67) |
| Innominate.substance (mean (SD)) | 2687.93 (322.72) |
| Hypothalamus (mean (SD)) | 7962.50 (917.36) |
| Thalamus (mean (SD)) | 14606.33 (1462.98) |
kable(Table.Prosencephalon.absolute.RSD)
| Table.Prosencephalon.absolute.RSDX2/Table.Prosencephalon.absolute.RSDX1 |
|---|
| 20.7 |
| 13.4 |
| 13.2 |
| 15.7 |
| 16.2 |
| 14.5 |
| 18.0 |
| 18.1 |
| 18.2 |
| 18.1 |
| 45.9 |
| 10.6 |
| 11.1 |
| 14.7 |
| 12.6 |
| 17.0 |
| 13.4 |
| 15.6 |
| 13.9 |
| 13.9 |
| 14.5 |
| 14.3 |
| 15.2 |
| 18.0 |
| 13.3 |
| 13.5 |
| 14.9 |
| 14.1 |
| 14.4 |
| 13.3 |
| 17.4 |
| 25.0 |
| 18.7 |
| 13.7 |
| 20.6 |
| 27.7 |
| 14.8 |
| 15.3 |
| 14.7 |
| 15.8 |
| 11.7 |
| 15.0 |
| 16.7 |
| 31.1 |
| 14.8 |
| 16.9 |
| 19.3 |
| 14.6 |
| 12.0 |
| 11.5 |
| 10.0 |
kable(Table.Prosencephalon.absolute.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Frontal.pole (mean (SD)) | 4369.21 (705.73) | 5009.31 (1098.53) | 0.072 | |
| F1 (mean (SD)) | 64718.00 (8455.20) | 72327.56 (8527.23) | 0.021 | |
| F2 (mean (SD)) | 55366.50 (7297.68) | 61163.62 (7171.64) | 0.037 | |
| F3.orbital (mean (SD)) | 3665.71 (565.37) | 4136.06 (584.21) | 0.034 | |
| F3.triangular (mean (SD)) | 10845.50 (1038.66) | 12426.62 (2189.00) | 0.020 | |
| F3.opercular (mean (SD)) | 12752.21 (1540.15) | 14996.44 (1828.47) | 0.001 | |
| Anterior.orbital (mean (SD)) | 3087.00 (244.87) | 3579.12 (730.22) | 0.023 | |
| Medial.orbital (mean (SD)) | 6973.14 (558.72) | 8085.19 (1654.78) | 0.024 | |
| Lateral.orbital (mean (SD)) | 5590.57 (456.44) | 6477.44 (1333.01) | 0.025 | |
| Posterior.orbital (mean (SD)) | 6845.21 (546.20) | 7924.19 (1624.75) | 0.025 | |
| Rectus (mean (SD)) | 8721.00 (747.46) | 11094.06 (6110.83) | 0.161 | |
| Rostral (mean (SD)) | 3413.07 (295.50) | 3750.88 (382.52) | 0.012 | |
| Precentral (mean (SD)) | 36944.14 (3427.70) | 41013.31 (4258.04) | 0.008 | |
| Postcentral (mean (SD)) | 20938.00 (3435.44) | 23506.69 (2701.43) | 0.030 | |
| Paracentral.lobule (mean (SD)) | 13361.57 (1785.89) | 14627.88 (1579.10) | 0.049 | |
| Subcentral (mean (SD)) | 5502.36 (885.64) | 6543.94 (906.29) | 0.004 | |
| SPL (mean (SD)) | 31813.43 (4849.58) | 34629.50 (3740.81) | 0.084 | |
| SMG (mean (SD)) | 28640.79 (4365.75) | 32516.25 (4504.07) | 0.024 | |
| ANG (mean (SD)) | 31864.79 (4552.43) | 36493.50 (3897.76) | 0.006 | |
| Precuneus (mean (SD)) | 28510.00 (3788.70) | 32915.00 (3700.31) | 0.003 | |
| Cuneus (mean (SD)) | 9355.07 (1040.46) | 10682.75 (1521.76) | 0.010 | |
| O1 (mean (SD)) | 8298.50 (925.51) | 9893.62 (1149.08) | <0.001 | |
| O2 (mean (SD)) | 14683.57 (1913.46) | 16751.88 (2414.54) | 0.016 | |
| O3 (mean (SD)) | 8261.00 (1177.06) | 10141.38 (1556.68) | 0.001 | |
| Occipital.pole (mean (SD)) | 10666.64 (1017.58) | 11980.25 (1631.86) | 0.015 | |
| Lingual (mean (SD)) | 19665.43 (2166.02) | 20818.56 (3117.94) | 0.256 | |
| Fusiform (mean (SD)) | 20947.07 (2857.52) | 23289.19 (3350.86) | 0.050 | |
| Temporal.pole (mean (SD)) | 23479.07 (2923.96) | 26994.12 (3345.51) | 0.005 | |
| T1 (mean (SD)) | 24988.57 (2642.33) | 29223.56 (3848.44) | 0.002 | |
| T2 (mean (SD)) | 22875.64 (2875.80) | 24408.38 (3273.84) | 0.187 | |
| T3 (mean (SD)) | 4531.00 (734.14) | 5430.12 (773.80) | 0.003 | |
| Planum.temporale (mean (SD)) | 2714.57 (409.79) | 3396.12 (878.68) | 0.013 | |
| Planum.polare (mean (SD)) | 9805.79 (1645.78) | 9978.62 (2058.92) | 0.803 | |
| Short.insular (mean (SD)) | 14652.79 (1336.31) | 16064.06 (2461.22) | 0.066 | |
| Long.insular (mean (SD)) | 8093.57 (1221.68) | 10059.31 (1904.19) | 0.003 | |
| SCA (mean (SD)) | 2108.21 (542.11) | 2644.69 (671.54) | 0.024 | |
| Cingulate.anterior (mean (SD)) | 13500.14 (2193.65) | 15224.44 (1778.78) | 0.025 | |
| Cingulate.middle (mean (SD)) | 16044.50 (2245.99) | 18089.75 (2598.50) | 0.030 | |
| Cingulate.posterior (mean (SD)) | 18885.71 (2679.94) | 21301.81 (2790.00) | 0.023 | |
| PHG (mean (SD)) | 10013.00 (1197.43) | 10819.50 (1927.25) | 0.187 | |
| Hippocampus (mean (SD)) | 7823.93 (712.09) | 8393.62 (1074.12) | 0.103 | |
| Amygdala (mean (SD)) | 3009.43 (469.08) | 3219.75 (462.25) | 0.227 | |
| Corpus.callosum (mean (SD)) | 3319.00 (580.49) | 3305.69 (545.72) | 0.949 | |
| Claustrum (mean (SD)) | 1189.93 (305.72) | 1480.50 (462.80) | 0.056 | |
| Putamen (mean (SD)) | 10793.64 (1243.13) | 11677.06 (1900.52) | 0.149 | |
| Caudate (mean (SD)) | 7368.07 (931.75) | 8139.94 (1520.25) | 0.111 | |
| Globus.pallidum (mean (SD)) | 2861.14 (330.56) | 3257.56 (710.86) | 0.066 | |
| Internal.capsule (mean (SD)) | 10153.14 (1654.93) | 11021.88 (1373.90) | 0.127 | |
| Innominate.substance (mean (SD)) | 2639.07 (362.97) | 2730.69 (288.08) | 0.448 | |
| Hypothalamus (mean (SD)) | 7507.07 (671.29) | 8361.00 (934.61) | 0.008 | |
| Thalamus (mean (SD)) | 14190.21 (1205.08) | 14970.44 (1605.09) | 0.148 |
kable(Table.Prosencephalon.absolute.stratified.gender.RSD.female)
| Table.Prosencephalon.absolute.stratified.gender.RSD.femaleX2/Table.Prosencephalon.absolute.stratified.gender.RSD.femaleX1 |
|---|
| 16.2 |
| 13.1 |
| 13.2 |
| 15.4 |
| 9.6 |
| 12.1 |
| 7.9 |
| 8.0 |
| 8.2 |
| 8.0 |
| 8.6 |
| 8.7 |
| 9.3 |
| 16.4 |
| 13.4 |
| 16.1 |
| 15.2 |
| 15.2 |
| 14.3 |
| 13.3 |
| 11.1 |
| 11.2 |
| 13.0 |
| 14.2 |
| 9.5 |
| 11.0 |
| 13.6 |
| 12.5 |
| 10.6 |
| 12.6 |
| 16.2 |
| 15.1 |
| 16.8 |
| 9.1 |
| 15.1 |
| 25.7 |
| 16.2 |
| 14.0 |
| 14.2 |
| 12.0 |
| 9.1 |
| 15.6 |
| 17.5 |
| 25.7 |
| 11.5 |
| 12.6 |
| 11.6 |
| 16.3 |
| 13.8 |
| 8.9 |
| 8.5 |
kable(Table.Prosencephalon.absolute.stratified.gender.RSD.male)
| Table.Prosencephalon.absolute.stratified.gender.RSD.maleX2/Table.Prosencephalon.absolute.stratified.gender.RSD.maleX1 |
|---|
| 21.9 |
| 11.8 |
| 11.7 |
| 14.1 |
| 17.6 |
| 12.2 |
| 20.4 |
| 20.5 |
| 20.6 |
| 20.5 |
| 55.1 |
| 10.2 |
| 10.4 |
| 11.5 |
| 10.8 |
| 13.8 |
| 10.8 |
| 13.9 |
| 10.7 |
| 11.2 |
| 14.2 |
| 11.6 |
| 14.4 |
| 15.3 |
| 13.6 |
| 15.0 |
| 14.4 |
| 12.4 |
| 13.2 |
| 13.4 |
| 14.3 |
| 25.9 |
| 20.6 |
| 15.3 |
| 18.9 |
| 25.4 |
| 11.7 |
| 14.4 |
| 13.1 |
| 17.8 |
| 12.8 |
| 14.4 |
| 16.5 |
| 31.3 |
| 16.3 |
| 18.7 |
| 21.8 |
| 12.5 |
| 10.5 |
| 11.2 |
| 10.7 |
NA
Prosencephalon.absolute <- select(Prosencephalon.absolute, - c(Gender))
Prosencephalon.absolute1 <- Prosencephalon.absolute[,-c(41:51)]
Prosencephalon.absolute2 <- Prosencephalon.absolute[, c(41:51)]
names.anatomical.structures.temporary <- c("Frontal.pole",
"F1",
"F2",
"F3.orbital",
"F3.triangular",
"F3.opercular",
"Anterior.orbital",
"Medial.orbital",
"Lateral.orbital",
"Posterior.orbital",
"Rectus",
"Rostral",
"Precentral",
"Postcentral",
"Paracentral.lobule",
"Subcentral",
"SPL",
"SMG",
"ANG",
"Precuneus",
"Cuneus",
"O1",
"O2",
"O3",
"Occipital.pole",
"Lingual",
"Fusiform",
"Temporal.pole",
"T1",
"T2",
"T3",
"Planum.temporale",
"Planum.polare",
"Short.insular",
"Long.insular",
"SCA",
"Cingulate.anterior",
"Cingulate.middle",
"Cingulate.posterior",
"PHG")
names.anatomical.structures.definitive <- c("Frontal pole",
"F1",
"F2",
"F3 orbital",
"F3 triangular",
"F3 opercular",
"Anterior orbital",
"Medial orbital",
"Lateral orbital",
"Posterior orbital",
"Rectus",
"Rostral",
"Precentral",
"Postcentral",
"Paracentral lobule",
"Subcentral",
"SPL",
"SMG",
"ANG",
"Precuneus",
"Cuneus",
"O1",
"O2",
"O3",
"Occipital pole",
"Lingual",
"Fusiform",
"Temporal pole",
"T1",
"T2",
"T3",
"Planum temporale",
"Planum polare",
"Short insular",
"Long insular",
"SCA",
"Cingulate anterior",
"Cingulate middle",
"Cingulate posterior",
"PHG")
Prosencephalon.absolute.plotdata1 <- gather(Prosencephalon.absolute1, "anatomical.structure", "relative.volume")
Prosencephalon.absolute.plotdata1$Gender <- All.Volumes$Gender
Prosencephalon.absolute.plotdata1$Age <- All.Volumes$`Age (years)`
Prosencephalon.absolute.plotdata1$Gender <- factor(Prosencephalon.absolute.plotdata1$Gender, levels = c("f", "m"), c("f", "m"))
Prosencephalon.absolute.plotdata1$anatomical.structure <- factor(Prosencephalon.absolute.plotdata1$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Prosencephalon.absolute.plot1 <- ggplot(Prosencephalon.absolute.plotdata1, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Absolute volume (in mm3)") +
theme_minimal() +
coord_flip() +
ggtitle("CEREBRAL GYRI") +
theme(plot.title = element_text(hjust = 0.5))
Prosencephalon.absolute.plot1
ggsave("Prosencephalon.absolute.plot1.pdf", plot = Prosencephalon.absolute.plot1, width = 14, height = 12, units = "in", dpi = 600)
names.anatomical.structures.temporary <- c(
"Corpus.callosum",
"Claustrum",
"Putamen",
"Caudate",
"Globus.pallidum",
"Internal.capsule",
"Innominate.substance",
"Hypothalamus",
"Thalamus",
"Hippocampus",
"Amygdala")
names.anatomical.structures.definitive <- c(
"Corpus callosum",
"Claustrum",
"Putamen",
"Caudate",
"Globus pallidum",
"Internal capsule",
"Innominate substance",
"Hypothalamus",
"Thalamus",
"Hippocampus",
"Amygdala")
Prosencephalon.absolute.plotdata2 <- gather(Prosencephalon.absolute2, "anatomical.structure", "relative.volume")
Prosencephalon.absolute.plotdata2$Gender <- All.Volumes$Gender
Prosencephalon.absolute.plotdata2$Age <- All.Volumes$`Age (years)`
Prosencephalon.absolute.plotdata2$Gender <- factor(Prosencephalon.absolute.plotdata2$Gender, levels = c("f", "m"), c("f", "m"))
Prosencephalon.absolute.plotdata2$anatomical.structure <- factor(Prosencephalon.absolute.plotdata2$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Prosencephalon.absolute.plot2 <- ggplot(Prosencephalon.absolute.plotdata2, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Absolute volume (in mm3)") +
theme_minimal() +
coord_flip() +
ggtitle("CENTRAL PROSENCEPHALON") +
theme(plot.title = element_text(hjust = 0.5))
Prosencephalon.absolute.plot2
ggsave("Prosencephalon.absolute.plot2.pdf", plot = Prosencephalon.absolute.plot2, width = 10, height = 6, units = "in", dpi = 600)
Prosencephalon.absolute$Gender <- All.Volumes$Gender
Prosencephalon.absolute$Age <- All.Volumes$`Age (years)`
Total.Frontal.pole.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Frontal.pole, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 6700, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME frontal pole") +
theme(plot.title = element_text(hjust = 0.5))
Total.Frontal.pole.Age.plot
ggsave("Total.Frontal.pole.Age.plot.pdf", plot = Total.Frontal.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.F1.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=F1, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 77000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME F1") +
theme(plot.title = element_text(hjust = 0.5))
Total.F1.Age.plot
ggsave("Total.F1.Age.plot.pdf", plot = Total.F1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.F2.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=F2, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 72000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME F2") +
theme(plot.title = element_text(hjust = 0.5))
Total.F2.Age.plot
ggsave("Total.F2.Age.plot.pdf", plot = Total.F2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.F3.orbital.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=F3.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4600, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME F3 orbital") +
theme(plot.title = element_text(hjust = 0.5))
Total.F3.orbital.Age.plot
ggsave("Total.F3.orbital.Age.plot.pdf", plot = Total.F3.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.F3.triangular.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=F3.triangular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 13300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME F3 triangular") +
theme(plot.title = element_text(hjust = 0.5))
Total.F3.triangular.Age.plot
ggsave("Total.F3.triangular.Age.plot.pdf", plot = Total.F3.triangular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.F3.opercular.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=F3.opercular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 17300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME F3 opercular") +
theme(plot.title = element_text(hjust = 0.5))
Total.F3.opercular.Age.plot
ggsave("Total.F3.opercular.Age.plot.pdf", plot = Total.F3.opercular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Anterior.orbital.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Anterior.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 5200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME anterior orbital") +
theme(plot.title = element_text(hjust = 0.5))
Total.Anterior.orbital.Age.plot
ggsave("Total.Anterior.orbital.Age.plot.pdf", plot = Total.Anterior.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Medial.orbital.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Medial.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 10500, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME medial orbital") +
theme(plot.title = element_text(hjust = 0.5))
Total.Medial.orbital.Age.plot
ggsave("Total.Medial.orbital.Age.plot.pdf", plot = Total.Medial.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Lateral.orbital.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Lateral.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 8200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME lateral orbital") +
theme(plot.title = element_text(hjust = 0.5))
Total.Lateral.orbital.Age.plot
ggsave("Total.Lateral.orbital.Age.plot.pdf", plot = Total.Lateral.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Posterior.orbital.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Posterior.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 10200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME posterior orbital") +
theme(plot.title = element_text(hjust = 0.5))
Total.Posterior.orbital.Age.plot
ggsave("Total.Posterior.orbital.Age.plot.pdf", plot = Total.Posterior.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Rectus.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Rectus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 22000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME rectus") +
theme(plot.title = element_text(hjust = 0.5))
Total.Rectus.Age.plot
ggsave("Total.Rectus.Age.plot.pdf", plot = Total.Rectus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Rostral.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Rostral, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME rostral") +
theme(plot.title = element_text(hjust = 0.5))
Total.Rostral.Age.plot
ggsave("Total.Rostral.Age.plot.pdf", plot = Total.Rostral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Precentral.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Precentral, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 48000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME precentral") +
theme(plot.title = element_text(hjust = 0.5))
Total.Precentral.Age.plot
ggsave("Total.Precentral.Age.plot.pdf", plot = Total.Precentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Postcentral.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Postcentral, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 26500, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME postcentral") +
theme(plot.title = element_text(hjust = 0.5))
Total.Postcentral.Age.plot
ggsave("Total.Postcentral.Age.plot.pdf", plot = Total.Postcentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Paracentral.lobule.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Paracentral.lobule, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 17200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME paracentral lobule") +
theme(plot.title = element_text(hjust = 0.5))
Total.Paracentral.lobule.Age.plot
ggsave("Total.Paracentral.lobule.Age.plot.pdf", plot = Total.Paracentral.lobule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Subcentral.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Subcentral, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 7200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME subcentral") +
theme(plot.title = element_text(hjust = 0.5))
Total.Subcentral.Age.plot
ggsave("Total.Subcentral.Age.plot.pdf", plot = Total.Subcentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.SPL.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=SPL, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 38000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME superior parietal lobule") +
theme(plot.title = element_text(hjust = 0.5))
Total.SPL.Age.plot
ggsave("Total.SPL.Age.plot.pdf", plot = Total.SPL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.SMG.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=SMG, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 36000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME supramarginal") +
theme(plot.title = element_text(hjust = 0.5))
Total.SMG.Age.plot
ggsave("Total.SMG.Age.plot.pdf", plot = Total.SMG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.ANG.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=ANG, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 38000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME angular") +
theme(plot.title = element_text(hjust = 0.5))
Total.ANG.Age.plot
ggsave("Total.ANG.Age.plot.pdf", plot = Total.ANG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Precuneus.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Precuneus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 33000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME precuneus") +
theme(plot.title = element_text(hjust = 0.5))
Total.Precuneus.Age.plot
ggsave("Total.Precuneus.Age.plot.pdf", plot = Total.Precuneus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Cuneus.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Cuneus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 11700, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME cuneus") +
theme(plot.title = element_text(hjust = 0.5))
Total.Cuneus.Age.plot
ggsave("Total.Cuneus.Age.plot.pdf", plot = Total.Cuneus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.O1.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=O1, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 11200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME O1") +
theme(plot.title = element_text(hjust = 0.5))
Total.O1.Age.plot
ggsave("Total.O1.Age.plot.pdf", plot = Total.O1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.O2.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=O2, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 18000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME O2") +
theme(plot.title = element_text(hjust = 0.5))
Total.O2.Age.plot
ggsave("Total.O2.Age.plot.pdf", plot = Total.O2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.O3.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=O3, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 11500, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME O3") +
theme(plot.title = element_text(hjust = 0.5))
Total.O3.Age.plot
ggsave("Total.O3.Age.plot.pdf", plot = Total.O3.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Occipital.pole.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Occipital.pole, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 13200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME occipital pole") +
theme(plot.title = element_text(hjust = 0.5))
Total.Occipital.pole.Age.plot
ggsave("Total.Occipital.pole.Age.plot.pdf", plot = Total.Occipital.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Lingual.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Lingual, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 25000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME lingual") +
theme(plot.title = element_text(hjust = 0.5))
Total.Lingual.Age.plot
ggsave("Total.Lingual.Age.plot.pdf", plot = Total.Lingual.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Fusiform.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Fusiform, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 26000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME fusiform") +
theme(plot.title = element_text(hjust = 0.5))
Total.Fusiform.Age.plot
ggsave("Total.Fusiform.Age.plot.pdf", plot = Total.Fusiform.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Temporal.pole.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Temporal.pole, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 31000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME temporal pole") +
theme(plot.title = element_text(hjust = 0.5))
Total.Temporal.pole.Age.plot
ggsave("Total.Temporal.pole.Age.plot.pdf", plot = Total.Temporal.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.T1.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=T1, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 31000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME T1") +
theme(plot.title = element_text(hjust = 0.5))
Total.T1.Age.plot
ggsave("Total.T1.Age.plot.pdf", plot = Total.T1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.T2.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=T2, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 29000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME T2") +
theme(plot.title = element_text(hjust = 0.5))
Total.T2.Age.plot
ggsave("Total.T2.Age.plot.pdf", plot = Total.T2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.T3.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=T3, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 6100, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME T3") +
theme(plot.title = element_text(hjust = 0.5))
Total.T3.Age.plot
ggsave("Total.T3.Age.plot.pdf", plot = Total.T3.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Planum.temporale.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Planum.temporale, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4100, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME planum temporale") +
theme(plot.title = element_text(hjust = 0.5))
Total.Planum.temporale.Age.plot
ggsave("Total.Planum.temporale.Age.plot.pdf", plot = Total.Planum.temporale.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Planum.polare.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Planum.polare, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 13300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME planum polare") +
theme(plot.title = element_text(hjust = 0.5))
Total.Planum.polare.Age.plot
ggsave("Total.Planum.polare.Age.plot.pdf", plot = Total.Planum.polare.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Short.insular.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Short.insular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 19000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME short insular") +
theme(plot.title = element_text(hjust = 0.5))
Total.Short.insular.Age.plot
ggsave("Total.Short.insular.Age.plot.pdf", plot = Total.Short.insular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Long.insular.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Long.insular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 12000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME long insular") +
theme(plot.title = element_text(hjust = 0.5))
Total.Long.insular.Age.plot
ggsave("Total.Long.insular.Age.plot.pdf", plot = Total.Long.insular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.SCA.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=SCA, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3800, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME subcallosal area") +
theme(plot.title = element_text(hjust = 0.5))
Total.SCA.Age.plot
ggsave("Total.SCA.Age.plot.pdf", plot = Total.SCA.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Cingulate.anterior.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Cingulate.anterior, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 17300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME cingulate anterior") +
theme(plot.title = element_text(hjust = 0.5))
Total.Cingulate.anterior.Age.plot
ggsave("Total.Cingulate.anterior.Age.plot.pdf", plot = Total.Cingulate.anterior.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Cingulate.middle.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Cingulate.middle, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 20300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME cingulate middle") +
theme(plot.title = element_text(hjust = 0.5))
Total.Cingulate.middle.Age.plot
ggsave("Total.Cingulate.middle.Age.plot.pdf", plot = Total.Cingulate.middle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Cingulate.posterior.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Cingulate.posterior, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 24300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME cingulate posterior") +
theme(plot.title = element_text(hjust = 0.5))
Total.Cingulate.posterior.Age.plot
ggsave("Total.Cingulate.posterior.Age.plot.pdf", plot = Total.Cingulate.posterior.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.PHG.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=PHG, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 14000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME PHG") +
theme(plot.title = element_text(hjust = 0.5))
Total.PHG.Age.plot
ggsave("Total.PHG.Age.plot.pdf", plot = Total.PHG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Hippocampus.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Hippocampus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 9700, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME hippocampus") +
theme(plot.title = element_text(hjust = 0.5))
Total.Hippocampus.Age.plot
ggsave("Total.Hippocampus.Age.plot.pdf", plot = Total.Hippocampus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Amygdala.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Amygdala, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3600, label.x = 60, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME amygdala") +
theme(plot.title = element_text(hjust = 0.5))
Total.Amygdala.Age.plot
ggsave("Total.Amygdala.Age.plot.pdf", plot = Total.Amygdala.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Corpus.callosum.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Corpus.callosum, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4100, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME corpus callosum") +
theme(plot.title = element_text(hjust = 0.5))
Total.Corpus.callosum.Age.plot
ggsave("Total.Corpus.callosum.Age.plot.pdf", plot = Total.Corpus.callosum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Claustrum.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Claustrum, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2100, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME claustrum") +
theme(plot.title = element_text(hjust = 0.5))
Total.Claustrum.Age.plot
ggsave("Total.Claustrum.Age.plot.pdf", plot = Total.Claustrum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Putamen.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Putamen, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 12300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME putamen") +
theme(plot.title = element_text(hjust = 0.5))
Total.Putamen.Age.plot
ggsave("Total.Putamen.Age.plot.pdf", plot = Total.Putamen.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Caudate.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Caudate, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 10200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME caudate") +
theme(plot.title = element_text(hjust = 0.5))
Total.Caudate.Age.plot
ggsave("Total.Caudate.Age.plot.pdf", plot = Total.Caudate.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Globus.pallidum.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Globus.pallidum, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME globus pallidum") +
theme(plot.title = element_text(hjust = 0.5))
Total.Globus.pallidum.Age.plot
ggsave("Total.Globus.pallidum.Age.plot.pdf", plot = Total.Globus.pallidum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Internal.capsule.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Internal.capsule, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 12200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME internal capsule") +
theme(plot.title = element_text(hjust = 0.5))
Total.Internal.capsule.Age.plot
ggsave("Total.Internal.capsule.Age.plot.pdf", plot = Total.Internal.capsule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Innominate.substance.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Innominate.substance, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3180, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME innominate substance") +
theme(plot.title = element_text(hjust = 0.5))
Total.Innominate.substance.Age.plot
ggsave("Total.Innominate.substance.Age.plot.pdf", plot = Total.Innominate.substance.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Hypothalamus.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Hypothalamus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 9200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME hypothalamus") +
theme(plot.title = element_text(hjust = 0.5))
Total.Hypothalamus.Age.plot
ggsave("Total.Hypothalamus.Age.plot.pdf", plot = Total.Hypothalamus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Thalamus.Age.plot <- ggplot(Prosencephalon.absolute, aes(y=Thalamus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 17000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME thalamus") +
theme(plot.title = element_text(hjust = 0.5))
Total.Thalamus.Age.plot
ggsave("Total.Thalamus.Age.plot.pdf", plot = Total.Thalamus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative Volumes
#Prosencephalon.absolute <- select(Prosencephalon.absolute, - c(Gender))
Prosencephalon.relative <- (100 * (Prosencephalon.absolute/All.Volumes$`Total encephalic volume (without ventricles)`))‘/’ not meaningful for factorsProsencephalon.relative1 <- Prosencephalon.relative[,-c(41:51)]
Prosencephalon.relative2 <- Prosencephalon.relative[, c(41:51)]
Table.Prosencephalon.relative <- CreateTableOne(
vars = c("Frontal.pole",
"F1",
"F2",
"F3.orbital",
"F3.triangular",
"F3.opercular",
"Anterior.orbital",
"Medial.orbital",
"Lateral.orbital",
"Posterior.orbital",
"Rectus",
"Rostral",
"Precentral",
"Postcentral",
"Paracentral.lobule",
"Subcentral",
"SPL",
"SMG",
"ANG",
"Precuneus",
"Cuneus",
"O1",
"O2",
"O3",
"Occipital.pole",
"Lingual",
"Fusiform",
"Temporal.pole",
"T1",
"T2",
"T3",
"Planum.temporale",
"Planum.polare",
"Short.insular",
"Long.insular",
"SCA",
"Cingulate.anterior",
"Cingulate.middle",
"Cingulate.posterior",
"PHG",
"Hippocampus",
"Amygdala",
"Corpus.callosum",
"Claustrum",
"Putamen",
"Caudate",
"Globus.pallidum",
"Internal.capsule",
"Innominate.substance",
"Hypothalamus",
"Thalamus"),
data = Prosencephalon.relative)
Prosencephalon.relative$Gender <- All.Volumes$Gender
Table.Prosencephalon.relative.stratified.gender <- CreateTableOne(
vars = c("Frontal.pole",
"F1",
"F2",
"F3.orbital",
"F3.triangular",
"F3.opercular",
"Anterior.orbital",
"Medial.orbital",
"Lateral.orbital",
"Posterior.orbital",
"Rectus",
"Rostral",
"Precentral",
"Postcentral",
"Paracentral.lobule",
"Subcentral",
"SPL",
"SMG",
"ANG",
"Precuneus",
"Cuneus",
"O1",
"O2",
"O3",
"Occipital.pole",
"Lingual",
"Fusiform",
"Temporal.pole",
"T1",
"T2",
"T3",
"Planum.temporale",
"Planum.polare",
"Short.insular",
"Long.insular",
"SCA",
"Cingulate.anterior",
"Cingulate.middle",
"Cingulate.posterior",
"PHG",
"Hippocampus",
"Amygdala",
"Corpus.callosum",
"Claustrum",
"Putamen",
"Caudate",
"Globus.pallidum",
"Internal.capsule",
"Innominate.substance",
"Hypothalamus",
"Thalamus"),
strata = c("Gender"),
data = Prosencephalon.relative)
Table.Prosencephalon.relative <- print(Table.Prosencephalon.relative, contDigits = 10)
Overall
n 30
Frontal.pole (mean (SD)) 0.4308161280 (0.0735790922)
F1 (mean (SD)) 6.2797984283 (0.3863873818)
F2 (mean (SD)) 5.3376497465 (0.3212511986)
F3.orbital (mean (SD)) 0.3580350033 (0.0400757488)
F3.triangular (mean (SD)) 1.0712810271 (0.1454267486)
F3.opercular (mean (SD)) 1.2780793248 (0.1499873117)
Anterior.orbital (mean (SD)) 0.3058239406 (0.0373903676)
Medial.orbital (mean (SD)) 0.6908155982 (0.0847676556)
Lateral.orbital (mean (SD)) 0.5536005086 (0.0684027573)
Posterior.orbital (mean (SD)) 0.6775656621 (0.0832782152)
Rectus (mean (SD)) 0.9134375442 (0.4012168421)
Rostral (mean (SD)) 0.3293498153 (0.0250770666)
Precentral (mean (SD)) 3.5870207286 (0.3013356393)
Postcentral (mean (SD)) 2.0375742862 (0.1796109149)
Paracentral.lobule (mean (SD)) 1.2872489540 (0.1404521846)
Subcentral (mean (SD)) 0.5533885997 (0.0701240033)
SPL (mean (SD)) 3.0512299837 (0.2995764614)
SMG (mean (SD)) 2.8054702345 (0.2963151197)
ANG (mean (SD)) 3.1401508432 (0.2974069556)
Precuneus (mean (SD)) 2.8184789289 (0.2195354334)
Cuneus (mean (SD)) 0.9218075932 (0.1105395289)
O1 (mean (SD)) 0.8363347713 (0.0794530354)
O2 (mean (SD)) 1.4412036853 (0.1395067116)
O3 (mean (SD)) 0.8459452532 (0.1135967778)
Occipital.pole (mean (SD)) 1.0401371468 (0.0957630116)
Lingual (mean (SD)) 1.8611729124 (0.2307977883)
Fusiform (mean (SD)) 2.0313915705 (0.2297491283)
Temporal.pole (mean (SD)) 2.3163307043 (0.1969989384)
T1 (mean (SD)) 2.4899711225 (0.2314929390)
T2 (mean (SD)) 2.1710340083 (0.2222231589)
T3 (mean (SD)) 0.4573538448 (0.0569894934)
Planum.temporale (mean (SD)) 0.2805395089 (0.0570436553)
Planum.polare (mean (SD)) 0.9110250954 (0.1711155137)
Short.insular (mean (SD)) 1.4149000524 (0.1765065390)
Long.insular (mean (SD)) 0.8324538909 (0.1196168331)
SCA (mean (SD)) 0.2170797247 (0.0471724812)
Cingulate.anterior (mean (SD)) 1.3176948779 (0.1348973794)
Cingulate.middle (mean (SD)) 1.5637867547 (0.1479091119)
Cingulate.posterior (mean (SD)) 1.8413310529 (0.1454271384)
PHG (mean (SD)) 0.9563118242 (0.1171393527)
Hippocampus (mean (SD)) 0.7446874971 (0.0621830878)
Amygdala (mean (SD)) 0.2864526632 (0.0401036938)
Corpus.callosum (mean (SD)) 0.3039193139 (0.0468123806)
Claustrum (mean (SD)) 0.1228498024 (0.0358816349)
Putamen (mean (SD)) 1.0344949954 (0.1388220008)
Caudate (mean (SD)) 0.7138494977 (0.1087637962)
Globus.pallidum (mean (SD)) 0.2805060774 (0.0391642507)
Internal.capsule (mean (SD)) 0.9724042811 (0.1172447094)
Innominate.substance (mean (SD)) 0.2477217623 (0.0361218477)
Hypothalamus (mean (SD)) 0.7289368930 (0.0482037353)
Thalamus (mean (SD)) 1.3400717256 (0.1017134094)Table.Prosencephalon.relative.stratified.gender <- print(Table.Prosencephalon.relative.stratified.gender, contDigits = 10) Stratified by Gender
f m p test
n 14 16
Frontal.pole (mean (SD)) 0.4282337280 (0.0713377035) 0.4330757280 (0.0777473097) 0.861
F1 (mean (SD)) 6.3006329062 (0.3639376668) 6.2615682601 (0.4160375339) 0.788
F2 (mean (SD)) 5.3883848936 (0.3077446428) 5.2932564928 (0.3360665361) 0.428
F3.orbital (mean (SD)) 0.3571578969 (0.0385677622) 0.3588024714 (0.0425977851) 0.913
F3.triangular (mean (SD)) 1.0636322212 (0.1197364194) 1.0779737323 (0.1684052590) 0.793
F3.opercular (mean (SD)) 1.2495682579 (0.1574229154) 1.3030265083 (0.1435004821) 0.339
Anterior.orbital (mean (SD)) 0.3017582832 (0.0164770413) 0.3093813908 (0.0493827496) 0.586
Medial.orbital (mean (SD)) 0.6815918090 (0.0371181605) 0.6988864137 (0.1120228996) 0.586
Lateral.orbital (mean (SD)) 0.5464232617 (0.0304519052) 0.5598805996 (0.0902891005) 0.600
Posterior.orbital (mean (SD)) 0.6691168234 (0.0367863500) 0.6849583960 (0.1100474777) 0.612
Rectus (mean (SD)) 0.8532393638 (0.0662940918) 0.9661109520 (0.5486963089) 0.452
Rostral (mean (SD)) 0.3339400566 (0.0264801278) 0.3253333540 (0.0239001532) 0.357
Precentral (mean (SD)) 3.6104693496 (0.2468897213) 3.5665031852 (0.3489460002) 0.697
Postcentral (mean (SD)) 2.0354425348 (0.1884559911) 2.0394395687 (0.1777112761) 0.953
Paracentral.lobule (mean (SD)) 1.3045162856 (0.1438324556) 1.2721400388 (0.1403115272) 0.538
Subcentral (mean (SD)) 0.5355965755 (0.0569946874) 0.5689566208 (0.0783433358) 0.199
SPL (mean (SD)) 3.0950976636 (0.2771645426) 3.0128457637 (0.3218134493) 0.463
SMG (mean (SD)) 2.7870612121 (0.2560750682) 2.8215781290 (0.3351530207) 0.756
ANG (mean (SD)) 3.1074492113 (0.3335758333) 3.1687647712 (0.2696241186) 0.582
Precuneus (mean (SD)) 2.7769055892 (0.2074598065) 2.8548556011 (0.2298977990) 0.341
Cuneus (mean (SD)) 0.9162725007 (0.1085944316) 0.9266507992 (0.1155395028) 0.803
O1 (mean (SD)) 0.8107747580 (0.0725777711) 0.8586997830 (0.0805991452) 0.100
O2 (mean (SD)) 1.4338278528 (0.1550060300) 1.4476575387 (0.1292605063) 0.792
O3 (mean (SD)) 0.8091809468 (0.1170210398) 0.8781140214 (0.1035122305) 0.098
Occipital.pole (mean (SD)) 1.0453411723 (0.1129383712) 1.0355836245 (0.0814121997) 0.786
Lingual (mean (SD)) 1.9260643720 (0.2287149262) 1.8043928852 (0.2242302480) 0.153
Fusiform (mean (SD)) 2.0431163714 (0.2142611581) 2.0211323697 (0.2490441211) 0.799
Temporal.pole (mean (SD)) 2.2887424248 (0.1717974683) 2.3404704489 (0.2193601055) 0.483
T1 (mean (SD)) 2.4388032912 (0.1640313381) 2.5347429750 (0.2751451068) 0.265
T2 (mean (SD)) 2.2309720524 (0.1865495819) 2.1185882198 (0.2429535202) 0.171
T3 (mean (SD)) 0.4422376645 (0.0597357531) 0.4705805025 (0.0527885654) 0.179
Planum.temporale (mean (SD)) 0.2652397597 (0.0370145950) 0.2939267894 (0.0685126147) 0.174
Planum.polare (mean (SD)) 0.9612340750 (0.1660219988) 0.8670922381 (0.1682532577) 0.135
Short.insular (mean (SD)) 1.4338625682 (0.1204101258) 1.3983078510 (0.2168811526) 0.591
Long.insular (mean (SD)) 0.7888303012 (0.0888448680) 0.8706245319 (0.1322545191) 0.060
SCA (mean (SD)) 0.2044625144 (0.0441130883) 0.2281197836 (0.0483429862) 0.175
Cingulate.anterior (mean (SD)) 1.3135641868 (0.1439613708) 1.3213092326 (0.1311107677) 0.879
Cingulate.middle (mean (SD)) 1.5619775543 (0.1253223665) 1.5653698051 (0.1693469993) 0.951
Cingulate.posterior (mean (SD)) 1.8380656367 (0.1343469675) 1.8441882921 (0.1588299348) 0.911
PHG (mean (SD)) 0.9762747018 (0.0728687435) 0.9388443063 (0.1457024489) 0.392
Hippocampus (mean (SD)) 0.7655342036 (0.0635206138) 0.7264466289 (0.0567298899) 0.086
Amygdala (mean (SD)) 0.2948215970 (0.0466054982) 0.2791298460 (0.0332319267) 0.293
Corpus.callosum (mean (SD)) 0.3231162484 (0.0427646174) 0.2871219961 (0.0447975428) 0.033
Claustrum (mean (SD)) 0.1157108693 (0.0257804133) 0.1290963689 (0.0427077348) 0.316
Putamen (mean (SD)) 1.0552049804 (0.1037510009) 1.0163737585 (0.1648596697) 0.454
Caudate (mean (SD)) 0.7197152028 (0.0745225273) 0.7087170058 (0.1341534632) 0.788
Globus.pallidum (mean (SD)) 0.2799215892 (0.0302657494) 0.2810175045 (0.0465933809) 0.941
Internal.capsule (mean (SD)) 0.9899260573 (0.1268862057) 0.9570727269 (0.1099341721) 0.454
Innominate.substance (mean (SD)) 0.2587625040 (0.0391948981) 0.2380611133 (0.0312707598) 0.119
Hypothalamus (mean (SD)) 0.7337200336 (0.0472119392) 0.7247516451 (0.0502044289) 0.620
Thalamus (mean (SD)) 1.3867798315 (0.0776479089) 1.2992021329 (0.1046820913) 0.016
Table.Prosencephalon.relative.RSD <- as.data.frame(Table.Prosencephalon.relative)
Table.Prosencephalon.relative.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.Prosencephalon.relative.RSD[-1,]),
' (',fixed=TRUE)))
Table.Prosencephalon.relative.RSD <- data.frame(cbind(str_replace_all(Table.Prosencephalon.relative.RSD$X1, "[ ]", ""),
str_replace_all(Table.Prosencephalon.relative.RSD$X2, "[)]", "")))
Table.Prosencephalon.relative.RSD$X1 <- as.character(Table.Prosencephalon.relative.RSD$X1)
Table.Prosencephalon.relative.RSD$X2 <- as.character(Table.Prosencephalon.relative.RSD$X2)
Table.Prosencephalon.relative.RSD <- as.data.frame(sapply(Table.Prosencephalon.relative.RSD, as.numeric))
Table.Prosencephalon.relative.RSD <- as.data.frame(Table.Prosencephalon.relative.RSD$X2/Table.Prosencephalon.relative.RSD$X1)
Table.Prosencephalon.relative.RSD <- round(Table.Prosencephalon.relative.RSD * 100, 1)
Table.Prosencephalon.relative.stratified.gender.RSD <- as.data.frame(Table.Prosencephalon.relative.stratified.gender)
Table.Prosencephalon.relative.stratified.gender.RSD <- select(Table.Prosencephalon.relative.stratified.gender.RSD, - c(p, test))
Table.Prosencephalon.relative.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.Prosencephalon.relative.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.Prosencephalon.relative.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Prosencephalon.relative.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.Prosencephalon.relative.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Prosencephalon.relative.stratified.gender.RSD.female$X1 <- as.character(Table.Prosencephalon.relative.stratified.gender.RSD.female$X1)
Table.Prosencephalon.relative.stratified.gender.RSD.female$X2 <- as.character(Table.Prosencephalon.relative.stratified.gender.RSD.female$X2)
Table.Prosencephalon.relative.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Prosencephalon.relative.stratified.gender.RSD.female, as.numeric))
Table.Prosencephalon.relative.stratified.gender.RSD.female <- as.data.frame(Table.Prosencephalon.relative.stratified.gender.RSD.female$X2/Table.Prosencephalon.relative.stratified.gender.RSD.female$X1)
Table.Prosencephalon.relative.stratified.gender.RSD.female <- round(Table.Prosencephalon.relative.stratified.gender.RSD.female * 100, 1)
Table.Prosencephalon.relative.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.Prosencephalon.relative.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.Prosencephalon.relative.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Prosencephalon.relative.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.Prosencephalon.relative.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Prosencephalon.relative.stratified.gender.RSD.male$X1 <- as.character(Table.Prosencephalon.relative.stratified.gender.RSD.male$X1)
Table.Prosencephalon.relative.stratified.gender.RSD.male$X2 <- as.character(Table.Prosencephalon.relative.stratified.gender.RSD.male$X2)
Table.Prosencephalon.relative.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Prosencephalon.relative.stratified.gender.RSD.male, as.numeric))
Table.Prosencephalon.relative.stratified.gender.RSD.male <- as.data.frame(Table.Prosencephalon.relative.stratified.gender.RSD.male$X2/Table.Prosencephalon.relative.stratified.gender.RSD.male$X1)
Table.Prosencephalon.relative.stratified.gender.RSD.male <- round(Table.Prosencephalon.relative.stratified.gender.RSD.male * 100, 1)
kable(Table.Prosencephalon.relative)
| Overall | |
|---|---|
| n | 30 |
| Frontal.pole (mean (SD)) | 0.4308161280 (0.0735790922) |
| F1 (mean (SD)) | 6.2797984283 (0.3863873818) |
| F2 (mean (SD)) | 5.3376497465 (0.3212511986) |
| F3.orbital (mean (SD)) | 0.3580350033 (0.0400757488) |
| F3.triangular (mean (SD)) | 1.0712810271 (0.1454267486) |
| F3.opercular (mean (SD)) | 1.2780793248 (0.1499873117) |
| Anterior.orbital (mean (SD)) | 0.3058239406 (0.0373903676) |
| Medial.orbital (mean (SD)) | 0.6908155982 (0.0847676556) |
| Lateral.orbital (mean (SD)) | 0.5536005086 (0.0684027573) |
| Posterior.orbital (mean (SD)) | 0.6775656621 (0.0832782152) |
| Rectus (mean (SD)) | 0.9134375442 (0.4012168421) |
| Rostral (mean (SD)) | 0.3293498153 (0.0250770666) |
| Precentral (mean (SD)) | 3.5870207286 (0.3013356393) |
| Postcentral (mean (SD)) | 2.0375742862 (0.1796109149) |
| Paracentral.lobule (mean (SD)) | 1.2872489540 (0.1404521846) |
| Subcentral (mean (SD)) | 0.5533885997 (0.0701240033) |
| SPL (mean (SD)) | 3.0512299837 (0.2995764614) |
| SMG (mean (SD)) | 2.8054702345 (0.2963151197) |
| ANG (mean (SD)) | 3.1401508432 (0.2974069556) |
| Precuneus (mean (SD)) | 2.8184789289 (0.2195354334) |
| Cuneus (mean (SD)) | 0.9218075932 (0.1105395289) |
| O1 (mean (SD)) | 0.8363347713 (0.0794530354) |
| O2 (mean (SD)) | 1.4412036853 (0.1395067116) |
| O3 (mean (SD)) | 0.8459452532 (0.1135967778) |
| Occipital.pole (mean (SD)) | 1.0401371468 (0.0957630116) |
| Lingual (mean (SD)) | 1.8611729124 (0.2307977883) |
| Fusiform (mean (SD)) | 2.0313915705 (0.2297491283) |
| Temporal.pole (mean (SD)) | 2.3163307043 (0.1969989384) |
| T1 (mean (SD)) | 2.4899711225 (0.2314929390) |
| T2 (mean (SD)) | 2.1710340083 (0.2222231589) |
| T3 (mean (SD)) | 0.4573538448 (0.0569894934) |
| Planum.temporale (mean (SD)) | 0.2805395089 (0.0570436553) |
| Planum.polare (mean (SD)) | 0.9110250954 (0.1711155137) |
| Short.insular (mean (SD)) | 1.4149000524 (0.1765065390) |
| Long.insular (mean (SD)) | 0.8324538909 (0.1196168331) |
| SCA (mean (SD)) | 0.2170797247 (0.0471724812) |
| Cingulate.anterior (mean (SD)) | 1.3176948779 (0.1348973794) |
| Cingulate.middle (mean (SD)) | 1.5637867547 (0.1479091119) |
| Cingulate.posterior (mean (SD)) | 1.8413310529 (0.1454271384) |
| PHG (mean (SD)) | 0.9563118242 (0.1171393527) |
| Hippocampus (mean (SD)) | 0.7446874971 (0.0621830878) |
| Amygdala (mean (SD)) | 0.2864526632 (0.0401036938) |
| Corpus.callosum (mean (SD)) | 0.3039193139 (0.0468123806) |
| Claustrum (mean (SD)) | 0.1228498024 (0.0358816349) |
| Putamen (mean (SD)) | 1.0344949954 (0.1388220008) |
| Caudate (mean (SD)) | 0.7138494977 (0.1087637962) |
| Globus.pallidum (mean (SD)) | 0.2805060774 (0.0391642507) |
| Internal.capsule (mean (SD)) | 0.9724042811 (0.1172447094) |
| Innominate.substance (mean (SD)) | 0.2477217623 (0.0361218477) |
| Hypothalamus (mean (SD)) | 0.7289368930 (0.0482037353) |
| Thalamus (mean (SD)) | 1.3400717256 (0.1017134094) |
kable(Table.Prosencephalon.relative.RSD)
| Table.Prosencephalon.relative.RSDX2/Table.Prosencephalon.relative.RSDX1 |
|---|
| 17.1 |
| 6.2 |
| 6.0 |
| 11.2 |
| 13.6 |
| 11.7 |
| 12.2 |
| 12.3 |
| 12.4 |
| 12.3 |
| 43.9 |
| 7.6 |
| 8.4 |
| 8.8 |
| 10.9 |
| 12.7 |
| 9.8 |
| 10.6 |
| 9.5 |
| 7.8 |
| 12.0 |
| 9.5 |
| 9.7 |
| 13.4 |
| 9.2 |
| 12.4 |
| 11.3 |
| 8.5 |
| 9.3 |
| 10.2 |
| 12.5 |
| 20.3 |
| 18.8 |
| 12.5 |
| 14.4 |
| 21.7 |
| 10.2 |
| 9.5 |
| 7.9 |
| 12.2 |
| 8.4 |
| 14.0 |
| 15.4 |
| 29.2 |
| 13.4 |
| 15.2 |
| 14.0 |
| 12.1 |
| 14.6 |
| 6.6 |
| 7.6 |
kable(Table.Prosencephalon.relative.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Frontal.pole (mean (SD)) | 0.4282337280 (0.0713377035) | 0.4330757280 (0.0777473097) | 0.861 | |
| F1 (mean (SD)) | 6.3006329062 (0.3639376668) | 6.2615682601 (0.4160375339) | 0.788 | |
| F2 (mean (SD)) | 5.3883848936 (0.3077446428) | 5.2932564928 (0.3360665361) | 0.428 | |
| F3.orbital (mean (SD)) | 0.3571578969 (0.0385677622) | 0.3588024714 (0.0425977851) | 0.913 | |
| F3.triangular (mean (SD)) | 1.0636322212 (0.1197364194) | 1.0779737323 (0.1684052590) | 0.793 | |
| F3.opercular (mean (SD)) | 1.2495682579 (0.1574229154) | 1.3030265083 (0.1435004821) | 0.339 | |
| Anterior.orbital (mean (SD)) | 0.3017582832 (0.0164770413) | 0.3093813908 (0.0493827496) | 0.586 | |
| Medial.orbital (mean (SD)) | 0.6815918090 (0.0371181605) | 0.6988864137 (0.1120228996) | 0.586 | |
| Lateral.orbital (mean (SD)) | 0.5464232617 (0.0304519052) | 0.5598805996 (0.0902891005) | 0.600 | |
| Posterior.orbital (mean (SD)) | 0.6691168234 (0.0367863500) | 0.6849583960 (0.1100474777) | 0.612 | |
| Rectus (mean (SD)) | 0.8532393638 (0.0662940918) | 0.9661109520 (0.5486963089) | 0.452 | |
| Rostral (mean (SD)) | 0.3339400566 (0.0264801278) | 0.3253333540 (0.0239001532) | 0.357 | |
| Precentral (mean (SD)) | 3.6104693496 (0.2468897213) | 3.5665031852 (0.3489460002) | 0.697 | |
| Postcentral (mean (SD)) | 2.0354425348 (0.1884559911) | 2.0394395687 (0.1777112761) | 0.953 | |
| Paracentral.lobule (mean (SD)) | 1.3045162856 (0.1438324556) | 1.2721400388 (0.1403115272) | 0.538 | |
| Subcentral (mean (SD)) | 0.5355965755 (0.0569946874) | 0.5689566208 (0.0783433358) | 0.199 | |
| SPL (mean (SD)) | 3.0950976636 (0.2771645426) | 3.0128457637 (0.3218134493) | 0.463 | |
| SMG (mean (SD)) | 2.7870612121 (0.2560750682) | 2.8215781290 (0.3351530207) | 0.756 | |
| ANG (mean (SD)) | 3.1074492113 (0.3335758333) | 3.1687647712 (0.2696241186) | 0.582 | |
| Precuneus (mean (SD)) | 2.7769055892 (0.2074598065) | 2.8548556011 (0.2298977990) | 0.341 | |
| Cuneus (mean (SD)) | 0.9162725007 (0.1085944316) | 0.9266507992 (0.1155395028) | 0.803 | |
| O1 (mean (SD)) | 0.8107747580 (0.0725777711) | 0.8586997830 (0.0805991452) | 0.100 | |
| O2 (mean (SD)) | 1.4338278528 (0.1550060300) | 1.4476575387 (0.1292605063) | 0.792 | |
| O3 (mean (SD)) | 0.8091809468 (0.1170210398) | 0.8781140214 (0.1035122305) | 0.098 | |
| Occipital.pole (mean (SD)) | 1.0453411723 (0.1129383712) | 1.0355836245 (0.0814121997) | 0.786 | |
| Lingual (mean (SD)) | 1.9260643720 (0.2287149262) | 1.8043928852 (0.2242302480) | 0.153 | |
| Fusiform (mean (SD)) | 2.0431163714 (0.2142611581) | 2.0211323697 (0.2490441211) | 0.799 | |
| Temporal.pole (mean (SD)) | 2.2887424248 (0.1717974683) | 2.3404704489 (0.2193601055) | 0.483 | |
| T1 (mean (SD)) | 2.4388032912 (0.1640313381) | 2.5347429750 (0.2751451068) | 0.265 | |
| T2 (mean (SD)) | 2.2309720524 (0.1865495819) | 2.1185882198 (0.2429535202) | 0.171 | |
| T3 (mean (SD)) | 0.4422376645 (0.0597357531) | 0.4705805025 (0.0527885654) | 0.179 | |
| Planum.temporale (mean (SD)) | 0.2652397597 (0.0370145950) | 0.2939267894 (0.0685126147) | 0.174 | |
| Planum.polare (mean (SD)) | 0.9612340750 (0.1660219988) | 0.8670922381 (0.1682532577) | 0.135 | |
| Short.insular (mean (SD)) | 1.4338625682 (0.1204101258) | 1.3983078510 (0.2168811526) | 0.591 | |
| Long.insular (mean (SD)) | 0.7888303012 (0.0888448680) | 0.8706245319 (0.1322545191) | 0.060 | |
| SCA (mean (SD)) | 0.2044625144 (0.0441130883) | 0.2281197836 (0.0483429862) | 0.175 | |
| Cingulate.anterior (mean (SD)) | 1.3135641868 (0.1439613708) | 1.3213092326 (0.1311107677) | 0.879 | |
| Cingulate.middle (mean (SD)) | 1.5619775543 (0.1253223665) | 1.5653698051 (0.1693469993) | 0.951 | |
| Cingulate.posterior (mean (SD)) | 1.8380656367 (0.1343469675) | 1.8441882921 (0.1588299348) | 0.911 | |
| PHG (mean (SD)) | 0.9762747018 (0.0728687435) | 0.9388443063 (0.1457024489) | 0.392 | |
| Hippocampus (mean (SD)) | 0.7655342036 (0.0635206138) | 0.7264466289 (0.0567298899) | 0.086 | |
| Amygdala (mean (SD)) | 0.2948215970 (0.0466054982) | 0.2791298460 (0.0332319267) | 0.293 | |
| Corpus.callosum (mean (SD)) | 0.3231162484 (0.0427646174) | 0.2871219961 (0.0447975428) | 0.033 | |
| Claustrum (mean (SD)) | 0.1157108693 (0.0257804133) | 0.1290963689 (0.0427077348) | 0.316 | |
| Putamen (mean (SD)) | 1.0552049804 (0.1037510009) | 1.0163737585 (0.1648596697) | 0.454 | |
| Caudate (mean (SD)) | 0.7197152028 (0.0745225273) | 0.7087170058 (0.1341534632) | 0.788 | |
| Globus.pallidum (mean (SD)) | 0.2799215892 (0.0302657494) | 0.2810175045 (0.0465933809) | 0.941 | |
| Internal.capsule (mean (SD)) | 0.9899260573 (0.1268862057) | 0.9570727269 (0.1099341721) | 0.454 | |
| Innominate.substance (mean (SD)) | 0.2587625040 (0.0391948981) | 0.2380611133 (0.0312707598) | 0.119 | |
| Hypothalamus (mean (SD)) | 0.7337200336 (0.0472119392) | 0.7247516451 (0.0502044289) | 0.620 | |
| Thalamus (mean (SD)) | 1.3867798315 (0.0776479089) | 1.2992021329 (0.1046820913) | 0.016 |
kable(Table.Prosencephalon.relative.stratified.gender.RSD.female)
| Table.Prosencephalon.relative.stratified.gender.RSD.femaleX2/Table.Prosencephalon.relative.stratified.gender.RSD.femaleX1 |
|---|
| 16.7 |
| 5.8 |
| 5.7 |
| 10.8 |
| 11.3 |
| 12.6 |
| 5.5 |
| 5.4 |
| 5.6 |
| 5.5 |
| 7.8 |
| 7.9 |
| 6.8 |
| 9.3 |
| 11.0 |
| 10.6 |
| 9.0 |
| 9.2 |
| 10.7 |
| 7.5 |
| 11.9 |
| 9.0 |
| 10.8 |
| 14.5 |
| 10.8 |
| 11.9 |
| 10.5 |
| 7.5 |
| 6.7 |
| 8.4 |
| 13.5 |
| 14.0 |
| 17.3 |
| 8.4 |
| 11.3 |
| 21.6 |
| 11.0 |
| 8.0 |
| 7.3 |
| 7.5 |
| 8.3 |
| 15.8 |
| 13.2 |
| 22.3 |
| 9.8 |
| 10.4 |
| 10.8 |
| 12.8 |
| 15.1 |
| 6.4 |
| 5.6 |
kable(Table.Prosencephalon.relative.stratified.gender.RSD.male)
| Table.Prosencephalon.relative.stratified.gender.RSD.maleX2/Table.Prosencephalon.relative.stratified.gender.RSD.maleX1 |
|---|
| 18.0 |
| 6.6 |
| 6.3 |
| 11.9 |
| 15.6 |
| 11.0 |
| 16.0 |
| 16.0 |
| 16.1 |
| 16.1 |
| 56.8 |
| 7.3 |
| 9.8 |
| 8.7 |
| 11.0 |
| 13.8 |
| 10.7 |
| 11.9 |
| 8.5 |
| 8.1 |
| 12.5 |
| 9.4 |
| 8.9 |
| 11.8 |
| 7.9 |
| 12.4 |
| 12.3 |
| 9.4 |
| 10.9 |
| 11.5 |
| 11.2 |
| 23.3 |
| 19.4 |
| 15.5 |
| 15.2 |
| 21.2 |
| 9.9 |
| 10.8 |
| 8.6 |
| 15.5 |
| 7.8 |
| 11.9 |
| 15.6 |
| 33.1 |
| 16.2 |
| 18.9 |
| 16.6 |
| 11.5 |
| 13.1 |
| 6.9 |
| 8.1 |
NA
Prosencephalon.relative1 <- select(Prosencephalon.relative1, - c(Age))
names.anatomical.structures.temporary <- c("Frontal.pole",
"F1",
"F2",
"F3.orbital",
"F3.triangular",
"F3.opercular",
"Anterior.orbital",
"Medial.orbital",
"Lateral.orbital",
"Posterior.orbital",
"Rectus",
"Rostral",
"Precentral",
"Postcentral",
"Paracentral.lobule",
"Subcentral",
"SPL",
"SMG",
"ANG",
"Precuneus",
"Cuneus",
"O1",
"O2",
"O3",
"Occipital.pole",
"Lingual",
"Fusiform",
"Temporal.pole",
"T1",
"T2",
"T3",
"Planum.temporale",
"Planum.polare",
"Short.insular",
"Long.insular",
"SCA",
"Cingulate.anterior",
"Cingulate.middle",
"Cingulate.posterior",
"PHG")
names.anatomical.structures.definitive <- c("Frontal pole",
"F1",
"F2",
"F3 orbital",
"F3 triangular",
"F3 opercular",
"Anterior orbital",
"Medial orbital",
"Lateral orbital",
"Posterior orbital",
"Rectus",
"Rostral",
"Precentral",
"Postcentral",
"Paracentral lobule",
"Subcentral",
"SPL",
"SMG",
"ANG",
"Precuneus",
"Cuneus",
"O1",
"O2",
"O3",
"Occipital pole",
"Lingual",
"Fusiform",
"Temporal pole",
"T1",
"T2",
"T3",
"Planum temporale",
"Planum polare",
"Short insular",
"Long insular",
"SCA",
"Cingulate anterior",
"Cingulate middle",
"Cingulate posterior",
"PHG")
Prosencephalon.relative.plotdata1 <- gather(Prosencephalon.relative1, "anatomical.structure", "relative.volume")
Prosencephalon.relative.plotdata1$Gender <- All.Volumes$Gender
Prosencephalon.relative.plotdata1$Age <- All.Volumes$`Age (years)`
Prosencephalon.relative.plotdata1$Gender <- factor(Prosencephalon.relative.plotdata1$Gender, levels = c("f", "m"), c("f", "m"))
Prosencephalon.relative.plotdata1$anatomical.structure <- factor(Prosencephalon.relative.plotdata1$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Prosencephalon.relative.plot1 <- ggplot(Prosencephalon.relative.plotdata1, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Relative volume (in %)") +
theme_minimal() +
coord_flip() +
ggtitle("CEREBRAL GYRI") +
theme(plot.title = element_text(hjust = 0.5))
Prosencephalon.relative.plot1
ggsave("Prosencephalon.relative.plot1.pdf", plot = Prosencephalon.relative.plot1, width = 14, height = 12, units = "in", dpi = 600)
names.anatomical.structures.temporary <- c(
"Corpus.callosum",
"Claustrum",
"Putamen",
"Caudate",
"Globus.pallidum",
"Internal.capsule",
"Innominate.substance",
"Hypothalamus",
"Thalamus",
"Hippocampus",
"Amygdala")
names.anatomical.structures.definitive <- c(
"Corpus callosum",
"Claustrum",
"Putamen",
"Caudate",
"Globus pallidum",
"Internal capsule",
"Innominate substance",
"Hypothalamus",
"Thalamus",
"Hippocampus",
"Amygdala")
Prosencephalon.relative.plotdata2 <- gather(Prosencephalon.relative2, "anatomical.structure", "relative.volume")
Prosencephalon.relative.plotdata2$Gender <- All.Volumes$Gender
Prosencephalon.relative.plotdata2$Age <- All.Volumes$`Age (years)`
Prosencephalon.relative.plotdata2$Gender <- factor(Prosencephalon.relative.plotdata2$Gender, levels = c("f", "m"), c("f", "m"))
Prosencephalon.relative.plotdata2$anatomical.structure <- factor(Prosencephalon.relative.plotdata2$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Prosencephalon.relative.plot2 <- ggplot(Prosencephalon.relative.plotdata2, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Relative volume (in %)") +
theme_minimal() +
coord_flip() +
ggtitle("CENTRAL PROSENCEPHALON") +
theme(plot.title = element_text(hjust = 0.5))
Prosencephalon.relative.plot2
ggsave("Prosencephalon.relative.plot2.pdf", plot = Prosencephalon.relative.plot2, width = 10, height = 6, units = "in", dpi = 600)
#Prosencephalon.relative$Gender <- All.Volumes$Gender
Prosencephalon.relative$Age <- All.Volumes$`Age (years)`
Relative.Frontal.pole.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Frontal.pole, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.57, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME frontal pole") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Frontal.pole.Age.plot
ggsave("Relative.Frontal.pole.Age.plot.pdf", plot = Relative.Frontal.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.F1.Age.plot <- ggplot(Prosencephalon.relative, aes(y=F1, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 6.6, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME F1") +
theme(plot.title = element_text(hjust = 0.5))
Relative.F1.Age.plot
ggsave("Relative.F1.Age.plot.pdf", plot = Relative.F1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.F2.Age.plot <- ggplot(Prosencephalon.relative, aes(y=F2, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 5.95, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME F2") +
theme(plot.title = element_text(hjust = 0.5))
Relative.F2.Age.plot
ggsave("Relative.F2.Age.plot.pdf", plot = Relative.F2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.F3.orbital.Age.plot <- ggplot(Prosencephalon.relative, aes(y=F3.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.41, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME F3 orbital") +
theme(plot.title = element_text(hjust = 0.5))
Relative.F3.orbital.Age.plot
ggsave("Relative.F3.orbital.Age.plot.pdf", plot = Relative.F3.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.F3.triangular.Age.plot <- ggplot(Prosencephalon.relative, aes(y=F3.triangular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.22, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME F3 triangular") +
theme(plot.title = element_text(hjust = 0.5))
Relative.F3.triangular.Age.plot
ggsave("Relative.F3.triangular.Age.plot.pdf", plot = Relative.F3.triangular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.F3.opercular.Age.plot <- ggplot(Prosencephalon.relative, aes(y=F3.opercular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.65, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME F3 opercular") +
theme(plot.title = element_text(hjust = 0.5))
Relative.F3.opercular.Age.plot
ggsave("Relative.F3.opercular.Age.plot.pdf", plot = Relative.F3.opercular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Anterior.orbital.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Anterior.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.385, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME anterior orbital") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Anterior.orbital.Age.plot
ggsave("Relative.Anterior.orbital.Age.plot.pdf", plot = Relative.Anterior.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Medial.orbital.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Medial.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.87, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME medial orbital") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Medial.orbital.Age.plot
ggsave("Relative.Medial.orbital.Age.plot.pdf", plot = Relative.Medial.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Lateral.orbital.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Lateral.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.67, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME lateral orbital") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Lateral.orbital.Age.plot
ggsave("Relative.Lateral.orbital.Age.plot.pdf", plot = Relative.Lateral.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Posterior.orbital.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Posterior.orbital, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.82, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME posterior orbital") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Posterior.orbital.Age.plot
ggsave("Relative.Posterior.orbital.Age.plot.pdf", plot = Relative.Posterior.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Rectus.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Rectus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.8, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME rectus") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Rectus.Age.plot
ggsave("Relative.Rectus.Age.plot.pdf", plot = Relative.Rectus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Rostral.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Rostral, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.41, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME rostral") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Rostral.Age.plot
ggsave("Relative.Rostral.Age.plot.pdf", plot = Relative.Rostral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Precentral.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Precentral, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4.6, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME precentral") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Precentral.Age.plot
ggsave("Relative.Precentral.Age.plot.pdf", plot = Relative.Precentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Postcentral.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Postcentral, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.45, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME postcentral") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Postcentral.Age.plot
ggsave("Relative.Postcentral.Age.plot.pdf", plot = Relative.Postcentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Paracentral.lobule.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Paracentral.lobule, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.62, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME paracentral lobule") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Paracentral.lobule.Age.plot
ggsave("Relative.Paracentral.lobule.Age.plot.pdf", plot = Relative.Paracentral.lobule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Subcentral.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Subcentral, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.66, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME subcentral") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Subcentral.Age.plot
ggsave("Relative.Subcentral.Age.plot.pdf", plot = Relative.Subcentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.SPL.Age.plot <- ggplot(Prosencephalon.relative, aes(y=SPL, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3.5, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME SPL") +
theme(plot.title = element_text(hjust = 0.5))
Relative.SPL.Age.plot
ggsave("Relative.SPL.Age.plot.pdf", plot = Relative.SPL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.SMG.Age.plot <- ggplot(Prosencephalon.relative, aes(y=SMG, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3.3, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME supramarginal") +
theme(plot.title = element_text(hjust = 0.5))
Relative.SMG.Age.plot
ggsave("Relative.SMG.Age.plot.pdf", plot = Relative.SMG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.ANG.Age.plot <- ggplot(Prosencephalon.relative, aes(y=ANG, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3.4, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME angular") +
theme(plot.title = element_text(hjust = 0.5))
Relative.ANG.Age.plot
ggsave("Relative.ANG.Age.plot.pdf", plot = Relative.ANG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Precuneus.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Precuneus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3.05, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME precuneus") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Precuneus.Age.plot
ggsave("Relative.Precuneus.Age.plot.pdf", plot = Relative.Precuneus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Cuneus.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Cuneus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.07, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME cuneus") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Cuneus.Age.plot
ggsave("Relative.Cuneus.Age.plot.pdf", plot = Relative.Cuneus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.O1.Age.plot <- ggplot(Prosencephalon.relative, aes(y=O1, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.97, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME O1") +
theme(plot.title = element_text(hjust = 0.5))
Relative.O1.Age.plot
ggsave("Relative.O1.Age.plot.pdf", plot = Relative.O1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.O2.Age.plot <- ggplot(Prosencephalon.relative, aes(y=O2, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.62, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME O2") +
theme(plot.title = element_text(hjust = 0.5))
Relative.O2.Age.plot
ggsave("Relative.O2.Age.plot.pdf", plot = Relative.O2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.O3.Age.plot <- ggplot(Prosencephalon.relative, aes(y=O3, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.02, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME O3") +
theme(plot.title = element_text(hjust = 0.5))
Relative.O3.Age.plot
ggsave("Relative.O3.Age.plot.pdf", plot = Relative.O3.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Occipital.pole.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Occipital.pole, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.17, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME occipital pole") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Occipital.pole.Age.plot
ggsave("Relative.Occipital.pole.Age.plot.pdf", plot = Relative.Occipital.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Lingual.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Lingual, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.3, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME lingual") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Lingual.Age.plot
ggsave("Relative.Lingual.Age.plot.pdf", plot = Relative.Lingual.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Fusiform.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Fusiform, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.28, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME fusiform") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Fusiform.Age.plot
ggsave("Relative.Fusiform.Age.plot.pdf", plot = Relative.Fusiform.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Temporal.pole.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Temporal.pole, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.7, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME temporal pole") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Temporal.pole.Age.plot
ggsave("Relative.Temporal.pole.Age.plot.pdf", plot = Relative.Temporal.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.T1.Age.plot <- ggplot(Prosencephalon.relative, aes(y=T1, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.75, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME T1") +
theme(plot.title = element_text(hjust = 0.5))
Relative.T1.Age.plot
ggsave("Relative.T1.Age.plot.pdf", plot = Relative.T1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.T2.Age.plot <- ggplot(Prosencephalon.relative, aes(y=T2, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.43, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME T2") +
theme(plot.title = element_text(hjust = 0.5))
Relative.T2.Age.plot
ggsave("Relative.T2.Age.plot.pdf", plot = Relative.T2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.T3.Age.plot <- ggplot(Prosencephalon.relative, aes(y=T3, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.51, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME T3") +
theme(plot.title = element_text(hjust = 0.5))
Relative.T3.Age.plot
ggsave("Relative.T3.Age.plot.pdf", plot = Relative.T3.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Planum.temporale.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Planum.temporale, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.335, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME planum temporale") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Planum.temporale.Age.plot
ggsave("Relative.Planum.temporale.Age.plot.pdf", plot = Relative.Planum.temporale.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Planum.polare.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Planum.polare, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.18, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME planum polare") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Planum.polare.Age.plot
ggsave("Relative.Planum.polare.Age.plot.pdf", plot = Relative.Planum.polare.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Short.insular.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Short.insular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.85, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME short insular") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Short.insular.Age.plot
ggsave("Relative.Short.insular.Age.plot.pdf", plot = Relative.Short.insular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Long.insular.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Long.insular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.97, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME long insular") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Long.insular.Age.plot
ggsave("Relative.Long.insular.Age.plot.pdf", plot = Relative.Long.insular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.SCA.Age.plot <- ggplot(Prosencephalon.relative, aes(y=SCA, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.33, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME subcallosal area") +
theme(plot.title = element_text(hjust = 0.5))
Relative.SCA.Age.plot
ggsave("Relative.SCA.Age.plot.pdf", plot = Relative.SCA.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Cingulate.anterior.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Cingulate.anterior, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.57, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME cingulate anterior") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Cingulate.anterior.Age.plot
ggsave("Relative.Cingulate.anterior.Age.plot.pdf", plot = Relative.Cingulate.anterior.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Cingulate.middle.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Cingulate.middle, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.75, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME cingulate middle") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Cingulate.middle.Age.plot
ggsave("Relative.Cingulate.middle.Age.plot.pdf", plot = Relative.Cingulate.middle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Cingulate.posterior.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Cingulate.posterior, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.05, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME cingulate posterior") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Cingulate.posterior.Age.plot
ggsave("Relative.Cingulate.posterior.Age.plot.pdf", plot = Relative.Cingulate.posterior.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.PHG.Age.plot <- ggplot(Prosencephalon.relative, aes(y=PHG, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.15, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME PHG") +
theme(plot.title = element_text(hjust = 0.5))
Relative.PHG.Age.plot
ggsave("Relative.PHG.Age.plot.pdf", plot = Relative.PHG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Hippocampus.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Hippocampus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.77, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME hippocampus") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Hippocampus.Age.plot
ggsave("Relative.Hippocampus.Age.plot.pdf", plot = Relative.Hippocampus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Amygdala.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Amygdala, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.335, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME amygdala") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Amygdala.Age.plot
ggsave("Relative.Amygdala.Age.plot.pdf", plot = Relative.Amygdala.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Corpus.callosum.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Corpus.callosum, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.36, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME corpus callosum") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Corpus.callosum.Age.plot
ggsave("Relative.Corpus.callosum.Age.plot.pdf", plot = Relative.Corpus.callosum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Claustrum.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Claustrum, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.18, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME claustrum") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Claustrum.Age.plot
ggsave("Relative.Claustrum.Age.plot.pdf", plot = Relative.Claustrum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Putamen.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Putamen, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.15, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME putamen") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Putamen.Age.plot
ggsave("Relative.Putamen.Age.plot.pdf", plot = Relative.Putamen.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Caudate.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Caudate, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.87, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME caudate") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Caudate.Age.plot
ggsave("Relative.Caudate.Age.plot.pdf", plot = Relative.Caudate.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Globus.pallidum.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Globus.pallidum, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.335, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME globus pallidum") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Globus.pallidum.Age.plot
ggsave("Relative.Globus.pallidum.Age.plot.pdf", plot = Relative.Globus.pallidum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Internal.capsule.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Internal.capsule, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.125, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME internal capsule") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Internal.capsule.Age.plot
ggsave("Relative.Internal.capsule.Age.plot.pdf", plot = Relative.Internal.capsule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Innominate.substance.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Innominate.substance, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.31, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME innominate substance") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Innominate.substance.Age.plot
ggsave("Relative.Innominate.substance.Age.plot.pdf", plot = Relative.Innominate.substance.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Hypothalamus.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Hypothalamus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.78, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME hypothalamus") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Hypothalamus.Age.plot
ggsave("Relative.Hypothalamus.Age.plot.pdf", plot = Relative.Hypothalamus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Thalamus.Age.plot <- ggplot(Prosencephalon.relative, aes(y=Thalamus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.47, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME thalamus") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Thalamus.Age.plot
ggsave("Relative.Thalamus.Age.plot.pdf", plot = Relative.Thalamus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Brainstem and Cerebellum
Absolute Volumes
Mesencephalon <- All.Volumes$Mesencephalon
Pons <- All.Volumes$Pons
Medulla.oblongata <- All.Volumes$`Medulla oblongata`
Cerebellar.peduncles <- All.Volumes$`Total volume cerebellar peduncles`
Vermis <- All.Volumes$`Total volume vermis`
Hemisphere <- All.Volumes$`Total volume cerebellar hemisphere`
Anterior.lobe <- (All.Volumes$Central+All.Volumes$Culmen+All.Volumes$`Total volume ala lobuli centralis`+All.Volumes$`Total volume AQL`)
Medial.lobe <- (All.Volumes$Declive+All.Volumes$Folium+All.Volumes$`Total volume PQL`+All.Volumes$`Total volume SSL`)
Posterior.lobe <- (All.Volumes$Tuber+All.Volumes$Pyramid+All.Volumes$Uvula+All.Volumes$`Total volume inferior semilunar /gracile`+All.Volumes$`Total volume biventer`+All.Volumes$`Total volume tonsilla`)
Flocculonodular.lobe <- (All.Volumes$Nodule+All.Volumes$`Total volume flocculus`)
Central <- All.Volumes$Central
Culmen <- All.Volumes$Culmen
Declive <- All.Volumes$Declive
Folium <- All.Volumes$Folium
Tuber <- All.Volumes$Tuber
Pyramid <- All.Volumes$Pyramid
Uvula <- All.Volumes$Uvula
Nodule <- All.Volumes$Nodule
Ala.lobuli.centralis <- All.Volumes$`Total volume ala lobuli centralis`
AQL <- All.Volumes$`Total volume AQL`
PQL <- All.Volumes$`Total volume PQL`
SSL <- All.Volumes$`Total volume SSL`
ISL.gracile <- All.Volumes$`Total volume inferior semilunar /gracile`
Biventer <- All.Volumes$`Total volume biventer`
Tonsilla <- All.Volumes$`Total volume tonsilla`
Flocculus <- All.Volumes$`Total volume flocculus`
Brainstem.Cerebellum.absolute <- as.data.frame(cbind(
Mesencephalon,
Pons,
Medulla.oblongata,
Cerebellar.peduncles,
Vermis,
Hemisphere,
Anterior.lobe,
Medial.lobe,
Posterior.lobe,
Flocculonodular.lobe,
Central,
Culmen,
Declive,
Folium,
Tuber,
Pyramid,
Uvula,
Nodule,
Ala.lobuli.centralis,
AQL,
PQL,
SSL,
ISL.gracile,
Biventer,
Tonsilla,
Flocculus
))
Brainstem.Cerebellum.absolute$Gender <- All.Volumes$Gender
Table.Brainstem.Cerebellum.absolute <- CreateTableOne(
vars = c("Mesencephalon",
"Pons",
"Medulla.oblongata",
"Cerebellar.peduncles",
"Vermis",
"Hemisphere",
"Anterior.lobe",
"Medial.lobe",
"Posterior.lobe",
"Flocculonodular.lobe",
"Central",
"Culmen",
"Declive",
"Folium",
"Tuber",
"Pyramid",
"Uvula",
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile",
"Biventer",
"Tonsilla",
"Flocculus"),
data = Brainstem.Cerebellum.absolute)
Table.Brainstem.Cerebellum.absolute.stratified.gender <- CreateTableOne(
vars = c("Mesencephalon",
"Pons",
"Medulla.oblongata",
"Cerebellar.peduncles",
"Vermis",
"Hemisphere",
"Anterior.lobe",
"Medial.lobe",
"Posterior.lobe",
"Flocculonodular.lobe",
"Central",
"Culmen",
"Declive",
"Folium",
"Tuber",
"Pyramid",
"Uvula",
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile",
"Biventer",
"Tonsilla",
"Flocculus"),
strata = c("Gender"),
data = Brainstem.Cerebellum.absolute)
Table.Brainstem.Cerebellum.absolute <- print(Table.Brainstem.Cerebellum.absolute, contDigits = 10)
Overall
n 30
Mesencephalon (mean (SD)) 10045.5666666667 (1460.5845472071)
Pons (mean (SD)) 15286.1333333333 (2216.2026505953)
Medulla.oblongata (mean (SD)) 2740.8333333333 (367.7611746898)
Cerebellar.peduncles (mean (SD)) 6780.7000000000 (962.4036413925)
Vermis (mean (SD)) 5946.8333333333 (804.8236938367)
Hemisphere (mean (SD)) 110786.1333333333 (12111.5249865098)
Anterior.lobe (mean (SD)) 32232.8666666667 (4775.6684076923)
Medial.lobe (mean (SD)) 28791.7000000000 (4414.8219407996)
Posterior.lobe (mean (SD)) 54644.6666666667 (7773.0127844438)
Flocculonodular.lobe (mean (SD)) 1063.7000000000 (150.3644193973)
Central (mean (SD)) 481.9666666667 (125.1868901733)
Culmen (mean (SD)) 2183.6000000000 (359.9041634888)
Declive (mean (SD)) 1030.5333333333 (188.4987392866)
Folium (mean (SD)) 440.1000000000 (120.4701565563)
Tuber (mean (SD)) 387.0333333333 (97.0319831145)
Pyramid (mean (SD)) 426.6333333333 (201.7137752521)
Uvula (mean (SD)) 784.6666666667 (129.0771844294)
Nodule (mean (SD)) 212.5000000000 (35.1290723495)
Ala.lobuli.centralis (mean (SD)) 11993.7666666667 (2880.0725647388)
AQL (mean (SD)) 17573.5333333333 (3822.1072607455)
PQL (mean (SD)) 13124.4333333333 (2598.2643767070)
SSL (mean (SD)) 14196.6333333333 (2513.7768875200)
ISL.gracile (mean (SD)) 34930.4333333333 (6197.6310695872)
Biventer (mean (SD)) 12163.3000000000 (3786.6815073268)
Tonsilla (mean (SD)) 5952.6000000000 (1337.6721571446)
Flocculus (mean (SD)) 851.2000000000 (139.2406500297) Table.Brainstem.Cerebellum.absolute.stratified.gender <- print(Table.Brainstem.Cerebellum.absolute.stratified.gender, contDigits = 10) Stratified by Gender
f m p test
n 14 16
Mesencephalon (mean (SD)) 9763.7857142857 (1207.3040645983) 10292.1250000000 (1649.9104167601) 0.332
Pons (mean (SD)) 14388.7142857143 (2400.8001688082) 16071.3750000000 (1758.1127713166) 0.036
Medulla.oblongata (mean (SD)) 2654.9285714286 (273.5303285914) 2816.0000000000 (428.6288215539) 0.238
Cerebellar.peduncles (mean (SD)) 6442.8571428571 (696.4864411009) 7076.3125000000 (1081.9140889337) 0.071
Vermis (mean (SD)) 5729.2857142857 (698.0237408979) 6137.1875000000 (864.4103361059) 0.170
Hemisphere (mean (SD)) 105887.2142857143 (12510.9148234564) 115072.6875000000 (10293.0357214235) 0.036
Anterior.lobe (mean (SD)) 30952.2142857143 (5066.7219440425) 33353.4375000000 (4355.9895617988) 0.174
Medial.lobe (mean (SD)) 28501.7142857143 (4694.7263282010) 29045.4375000000 (4293.3716660103) 0.743
Posterior.lobe (mean (SD)) 51123.5000000000 (7944.6905807009) 57725.6875000000 (6357.0069447684) 0.017
Flocculonodular.lobe (mean (SD)) 1039.6428571429 (171.7814385705) 1084.7500000000 (130.8604855052) 0.422
Central (mean (SD)) 494.1428571429 (138.8357397696) 471.3125000000 (115.4727781774) 0.627
Culmen (mean (SD)) 2090.3571428571 (300.4288235539) 2265.1875000000 (396.2178220373) 0.189
Declive (mean (SD)) 1009.5000000000 (222.2752105629) 1048.9375000000 (158.4354626759) 0.577
Folium (mean (SD)) 429.9285714286 (104.0461911370) 449.0000000000 (135.9975489975) 0.673
Tuber (mean (SD)) 359.0714285714 (74.5442194898) 411.5000000000 (109.6290715702) 0.143
Pyramid (mean (SD)) 368.1428571429 (137.8856031660) 477.8125000000 (237.0651721222) 0.140
Uvula (mean (SD)) 768.3571428571 (121.3416075148) 798.9375000000 (137.7860509389) 0.527
Nodule (mean (SD)) 210.4285714286 (32.6937168410) 214.3125000000 (38.1046257034) 0.768
Ala.lobuli.centralis (mean (SD)) 11410.7857142857 (3163.7587717672) 12503.8750000000 (2601.3639210999) 0.308
AQL (mean (SD)) 16956.9285714286 (3679.3877096037) 18113.0625000000 (3980.5670026392) 0.418
PQL (mean (SD)) 12835.1428571429 (3258.6597022311) 13377.5625000000 (1924.1673513064) 0.577
SSL (mean (SD)) 14227.1428571429 (2163.5692325821) 14169.9375000000 (2856.2831901792) 0.952
ISL.gracile (mean (SD)) 33388.7857142857 (6276.2341866707) 36279.3750000000 (5996.8659134029) 0.208
Biventer (mean (SD)) 10529.6428571429 (3581.7178217924) 13592.7500000000 (3454.4626113671) 0.024
Tonsilla (mean (SD)) 5709.5000000000 (1079.5718346427) 6165.3125000000 (1531.6471620992) 0.361
Flocculus (mean (SD)) 829.2142857143 (158.8619035376) 870.4375000000 (121.5126708071) 0.428 write.csv(Table.Brainstem.Cerebellum.absolute, "Table.Brainstem.Cerebellum.absolute.csv")
write.csv(Table.Brainstem.Cerebellum.absolute.stratified.gender, "Table.Brainstem.Cerebellum.absolute.stratified.gender.csv")
Table.Brainstem.Cerebellum.absolute.RSD <- as.data.frame(Table.Brainstem.Cerebellum.absolute)
Table.Brainstem.Cerebellum.absolute.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.Brainstem.Cerebellum.absolute.RSD[-1,]),' (',fixed=TRUE)))
Table.Brainstem.Cerebellum.absolute.RSD <- data.frame(cbind(str_replace_all(Table.Brainstem.Cerebellum.absolute.RSD$X1, "[ ]", ""),
str_replace_all(Table.Brainstem.Cerebellum.absolute.RSD$X2, "[)]", "")))
Table.Brainstem.Cerebellum.absolute.RSD$X1 <- as.character(Table.Brainstem.Cerebellum.absolute.RSD$X1)
Table.Brainstem.Cerebellum.absolute.RSD$X2 <- as.character(Table.Brainstem.Cerebellum.absolute.RSD$X2)
Table.Brainstem.Cerebellum.absolute.RSD <- as.data.frame(sapply(Table.Brainstem.Cerebellum.absolute.RSD, as.numeric))
Table.Brainstem.Cerebellum.absolute.RSD <- as.data.frame(Table.Brainstem.Cerebellum.absolute.RSD$X2/Table.Brainstem.Cerebellum.absolute.RSD$X1)
Table.Brainstem.Cerebellum.absolute.RSD <- round(Table.Brainstem.Cerebellum.absolute.RSD * 100, 1)
Table.Prosencephalon.absolute.stratified.gender.RSD <- as.data.frame(Table.Brainstem.Cerebellum.absolute.stratified.gender)
Table.Prosencephalon.absolute.stratified.gender.RSD <- select(Table.Prosencephalon.absolute.stratified.gender.RSD, - c(p, test))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.Prosencephalon.absolute.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2)
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Prosencephalon.absolute.stratified.gender.RSD.female, as.numeric))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2/Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- round(Table.Prosencephalon.absolute.stratified.gender.RSD.female * 100, 1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.Prosencephalon.absolute.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Prosencephalon.absolute.stratified.gender.RSD.male, as.numeric))
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2/Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- round(Table.Prosencephalon.absolute.stratified.gender.RSD.male * 100, 1)
kable(Table.Brainstem.Cerebellum.absolute)
| Overall | |
|---|---|
| n | 30 |
| Mesencephalon (mean (SD)) | 10045.5666666667 (1460.5845472071) |
| Pons (mean (SD)) | 15286.1333333333 (2216.2026505953) |
| Medulla.oblongata (mean (SD)) | 2740.8333333333 (367.7611746898) |
| Cerebellar.peduncles (mean (SD)) | 6780.7000000000 (962.4036413925) |
| Vermis (mean (SD)) | 5946.8333333333 (804.8236938367) |
| Hemisphere (mean (SD)) | 110786.1333333333 (12111.5249865098) |
| Anterior.lobe (mean (SD)) | 32232.8666666667 (4775.6684076923) |
| Medial.lobe (mean (SD)) | 28791.7000000000 (4414.8219407996) |
| Posterior.lobe (mean (SD)) | 54644.6666666667 (7773.0127844438) |
| Flocculonodular.lobe (mean (SD)) | 1063.7000000000 (150.3644193973) |
| Central (mean (SD)) | 481.9666666667 (125.1868901733) |
| Culmen (mean (SD)) | 2183.6000000000 (359.9041634888) |
| Declive (mean (SD)) | 1030.5333333333 (188.4987392866) |
| Folium (mean (SD)) | 440.1000000000 (120.4701565563) |
| Tuber (mean (SD)) | 387.0333333333 (97.0319831145) |
| Pyramid (mean (SD)) | 426.6333333333 (201.7137752521) |
| Uvula (mean (SD)) | 784.6666666667 (129.0771844294) |
| Nodule (mean (SD)) | 212.5000000000 (35.1290723495) |
| Ala.lobuli.centralis (mean (SD)) | 11993.7666666667 (2880.0725647388) |
| AQL (mean (SD)) | 17573.5333333333 (3822.1072607455) |
| PQL (mean (SD)) | 13124.4333333333 (2598.2643767070) |
| SSL (mean (SD)) | 14196.6333333333 (2513.7768875200) |
| ISL.gracile (mean (SD)) | 34930.4333333333 (6197.6310695872) |
| Biventer (mean (SD)) | 12163.3000000000 (3786.6815073268) |
| Tonsilla (mean (SD)) | 5952.6000000000 (1337.6721571446) |
| Flocculus (mean (SD)) | 851.2000000000 (139.2406500297) |
kable(Table.Brainstem.Cerebellum.absolute.RSD)
| Table.Brainstem.Cerebellum.absolute.RSDX2/Table.Brainstem.Cerebellum.absolute.RSDX1 |
|---|
| 14.5 |
| 14.5 |
| 13.4 |
| 14.2 |
| 13.5 |
| 10.9 |
| 14.8 |
| 15.3 |
| 14.2 |
| 14.1 |
| 26.0 |
| 16.5 |
| 18.3 |
| 27.4 |
| 25.1 |
| 47.3 |
| 16.4 |
| 16.5 |
| 24.0 |
| 21.7 |
| 19.8 |
| 17.7 |
| 17.7 |
| 31.1 |
| 22.5 |
| 16.4 |
kable(Table.Brainstem.Cerebellum.absolute.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Mesencephalon (mean (SD)) | 9763.7857142857 (1207.3040645983) | 10292.1250000000 (1649.9104167601) | 0.332 | |
| Pons (mean (SD)) | 14388.7142857143 (2400.8001688082) | 16071.3750000000 (1758.1127713166) | 0.036 | |
| Medulla.oblongata (mean (SD)) | 2654.9285714286 (273.5303285914) | 2816.0000000000 (428.6288215539) | 0.238 | |
| Cerebellar.peduncles (mean (SD)) | 6442.8571428571 (696.4864411009) | 7076.3125000000 (1081.9140889337) | 0.071 | |
| Vermis (mean (SD)) | 5729.2857142857 (698.0237408979) | 6137.1875000000 (864.4103361059) | 0.170 | |
| Hemisphere (mean (SD)) | 105887.2142857143 (12510.9148234564) | 115072.6875000000 (10293.0357214235) | 0.036 | |
| Anterior.lobe (mean (SD)) | 30952.2142857143 (5066.7219440425) | 33353.4375000000 (4355.9895617988) | 0.174 | |
| Medial.lobe (mean (SD)) | 28501.7142857143 (4694.7263282010) | 29045.4375000000 (4293.3716660103) | 0.743 | |
| Posterior.lobe (mean (SD)) | 51123.5000000000 (7944.6905807009) | 57725.6875000000 (6357.0069447684) | 0.017 | |
| Flocculonodular.lobe (mean (SD)) | 1039.6428571429 (171.7814385705) | 1084.7500000000 (130.8604855052) | 0.422 | |
| Central (mean (SD)) | 494.1428571429 (138.8357397696) | 471.3125000000 (115.4727781774) | 0.627 | |
| Culmen (mean (SD)) | 2090.3571428571 (300.4288235539) | 2265.1875000000 (396.2178220373) | 0.189 | |
| Declive (mean (SD)) | 1009.5000000000 (222.2752105629) | 1048.9375000000 (158.4354626759) | 0.577 | |
| Folium (mean (SD)) | 429.9285714286 (104.0461911370) | 449.0000000000 (135.9975489975) | 0.673 | |
| Tuber (mean (SD)) | 359.0714285714 (74.5442194898) | 411.5000000000 (109.6290715702) | 0.143 | |
| Pyramid (mean (SD)) | 368.1428571429 (137.8856031660) | 477.8125000000 (237.0651721222) | 0.140 | |
| Uvula (mean (SD)) | 768.3571428571 (121.3416075148) | 798.9375000000 (137.7860509389) | 0.527 | |
| Nodule (mean (SD)) | 210.4285714286 (32.6937168410) | 214.3125000000 (38.1046257034) | 0.768 | |
| Ala.lobuli.centralis (mean (SD)) | 11410.7857142857 (3163.7587717672) | 12503.8750000000 (2601.3639210999) | 0.308 | |
| AQL (mean (SD)) | 16956.9285714286 (3679.3877096037) | 18113.0625000000 (3980.5670026392) | 0.418 | |
| PQL (mean (SD)) | 12835.1428571429 (3258.6597022311) | 13377.5625000000 (1924.1673513064) | 0.577 | |
| SSL (mean (SD)) | 14227.1428571429 (2163.5692325821) | 14169.9375000000 (2856.2831901792) | 0.952 | |
| ISL.gracile (mean (SD)) | 33388.7857142857 (6276.2341866707) | 36279.3750000000 (5996.8659134029) | 0.208 | |
| Biventer (mean (SD)) | 10529.6428571429 (3581.7178217924) | 13592.7500000000 (3454.4626113671) | 0.024 | |
| Tonsilla (mean (SD)) | 5709.5000000000 (1079.5718346427) | 6165.3125000000 (1531.6471620992) | 0.361 | |
| Flocculus (mean (SD)) | 829.2142857143 (158.8619035376) | 870.4375000000 (121.5126708071) | 0.428 |
kable(Table.Prosencephalon.absolute.stratified.gender.RSD.female)
| Table.Prosencephalon.absolute.stratified.gender.RSD.femaleX2/Table.Prosencephalon.absolute.stratified.gender.RSD.femaleX1 |
|---|
| 12.4 |
| 16.7 |
| 10.3 |
| 10.8 |
| 12.2 |
| 11.8 |
| 16.4 |
| 16.5 |
| 15.5 |
| 16.5 |
| 28.1 |
| 14.4 |
| 22.0 |
| 24.2 |
| 20.8 |
| 37.5 |
| 15.8 |
| 15.5 |
| 27.7 |
| 21.7 |
| 25.4 |
| 15.2 |
| 18.8 |
| 34.0 |
| 18.9 |
| 19.2 |
kable(Table.Prosencephalon.absolute.stratified.gender.RSD.male)
| Table.Prosencephalon.absolute.stratified.gender.RSD.maleX2/Table.Prosencephalon.absolute.stratified.gender.RSD.maleX1 |
|---|
| 16.0 |
| 10.9 |
| 15.2 |
| 15.3 |
| 14.1 |
| 8.9 |
| 13.1 |
| 14.8 |
| 11.0 |
| 12.1 |
| 24.5 |
| 17.5 |
| 15.1 |
| 30.3 |
| 26.6 |
| 49.6 |
| 17.2 |
| 17.8 |
| 20.8 |
| 22.0 |
| 14.4 |
| 20.2 |
| 16.5 |
| 25.4 |
| 24.8 |
| 14.0 |
NA
Brainstem.Cerebellum.absolute <- select(Brainstem.Cerebellum.absolute, - c(Gender))
Brainstem.Cerebellum.absolute1 <- Brainstem.Cerebellum.absolute[,c(1:10)]
Brainstem.Cerebellum.absolute2 <- Brainstem.Cerebellum.absolute[,-c(1:10)]
names.anatomical.structures.temporary <- c("Mesencephalon",
"Pons",
"Medulla.oblongata",
"Cerebellar.peduncles",
"Vermis",
"Hemisphere",
"Anterior.lobe",
"Medial.lobe",
"Posterior.lobe",
"Flocculonodular.lobe")
names.anatomical.structures.definitive <- c("Mesencephalon",
"Pons",
"Medulla oblongata",
"Cerebellar peduncles",
"Vermis",
"Hemisphere",
"Anterior lobe",
"Medial lobe",
"Posterior lobe",
"Flocculonodular lobe")
Brainstem.Cerebellum.absolute.plotdata1 <- gather(Brainstem.Cerebellum.absolute1, "anatomical.structure", "relative.volume")
Brainstem.Cerebellum.absolute.plotdata1$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.absolute.plotdata1$Age <- All.Volumes$`Age (years)`
Brainstem.Cerebellum.absolute.plotdata1$Gender <- factor(Brainstem.Cerebellum.absolute.plotdata1$Gender, levels = c("f", "m"), c("f", "m"))
Brainstem.Cerebellum.absolute.plotdata1$anatomical.structure <- factor(Brainstem.Cerebellum.absolute.plotdata1$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Brainstem.Cerebellum.absolute.plot1 <- ggplot(Brainstem.Cerebellum.absolute.plotdata1, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Absolute volume (in mm3)") +
theme_minimal() +
coord_flip() +
ggtitle("BRAINSTEM & CEREBELLUM") +
theme(plot.title = element_text(hjust = 0.5))
Brainstem.Cerebellum.absolute.plot1
ggsave("Brainstem.Cerebellum.absolute.plot1.pdf", plot = Brainstem.Cerebellum.absolute.plot1, width = 12, height = 6, units = "in", dpi = 600)
names.anatomical.structures.temporary <- c(
"Central",
"Culmen",
"Declive",
"Folium",
"Tuber",
"Pyramid",
"Uvula",
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile",
"Biventer",
"Tonsilla",
"Flocculus")
names.anatomical.structures.definitive <- c(
"Central",
"Culmen",
"Declive",
"Folium",
"Tuber",
"Pyramid",
"Uvula",
"Nodule",
"Ala lobuli centralis",
"AQL",
"PQL",
"SSL",
"ISL/gracile",
"Biventer",
"Tonsilla",
"Flocculus")
Brainstem.Cerebellum.absolute.plotdata2 <- gather(Brainstem.Cerebellum.absolute2, "anatomical.structure", "relative.volume")
Brainstem.Cerebellum.absolute.plotdata2$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.absolute.plotdata2$Age <- All.Volumes$`Age (years)`
Brainstem.Cerebellum.absolute.plotdata2$Gender <- factor(Brainstem.Cerebellum.absolute.plotdata2$Gender, levels = c("f", "m"), c("f", "m"))
Brainstem.Cerebellum.absolute.plotdata2$anatomical.structure <- factor(Brainstem.Cerebellum.absolute.plotdata2$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Brainstem.Cerebellum.absolute.plot2 <- ggplot(Brainstem.Cerebellum.absolute.plotdata2, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Absolute volume (in mm3)") +
theme_minimal() +
coord_flip() +
ggtitle("CEREBELLAR LOBES & LOBULES") +
theme(plot.title = element_text(hjust = 0.5))
Brainstem.Cerebellum.absolute.plot2
ggsave("Brainstem.Cerebellum.absolute.plot2.pdf", plot = Brainstem.Cerebellum.absolute.plot2, width = 12, height = 6, units = "in", dpi = 600)
Brainstem.Cerebellum.absolute$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.absolute$Age <- All.Volumes$`Age (years)`
Total.Mesencephalon.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Mesencephalon, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 11700, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME mesencephalon") +
theme(plot.title = element_text(hjust = 0.5))
Total.Mesencephalon.Age.plot
ggsave("Total.Mesencephalon.Age.plot.pdf", plot = Total.Mesencephalon.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Pons.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Pons, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 20500, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME pons") +
theme(plot.title = element_text(hjust = 0.5))
Total.Pons.Age.plot
ggsave("Total.Pons.Age.plot.pdf", plot = Total.Pons.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Medulla.oblongata.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Medulla.oblongata, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3300, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME medulla oblongata") +
theme(plot.title = element_text(hjust = 0.5))
Total.Medulla.oblongata.Age.plot
ggsave("Total.Medulla.oblongata.Age.plot.pdf", plot = Total.Medulla.oblongata.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Cerebellar.peduncles.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Cerebellar.peduncles, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 8700, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME cerebellar peduncles") +
theme(plot.title = element_text(hjust = 0.5))
Total.Cerebellar.peduncles.Age.plot
ggsave("Total.Cerebellar.peduncles.Age.plot.pdf", plot = Total.Cerebellar.peduncles.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Vermis.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Vermis, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 7200, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME vermis") +
theme(plot.title = element_text(hjust = 0.5))
Total.Vermis.Age.plot
ggsave("Total.Vermis.Age.plot.pdf", plot = Total.Vermis.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Hemisphere.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Hemisphere, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 123000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME hemisphere") +
theme(plot.title = element_text(hjust = 0.5))
Total.Hemisphere.Age.plot
ggsave("Total.Hemisphere.Age.plot.pdf", plot = Total.Hemisphere.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Anterior.lobe.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Anterior.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 36000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME anterior lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.Anterior.lobe.Age.plot
ggsave("Total.Anterior.lobe.Age.plot.pdf", plot = Total.Anterior.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Medial.lobe.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Medial.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 31000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME medial lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.Medial.lobe.Age.plot
ggsave("Total.Medial.lobe.Age.plot.pdf", plot = Total.Medial.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Posterior.lobe.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Posterior.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 66000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME posterior lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.Posterior.lobe.Age.plot
ggsave("Total.Posterior.lobe.Age.plot.pdf", plot = Total.Posterior.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Flocculonodular.lobe.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Flocculonodular.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1250, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME flocculonodular lobe") +
theme(plot.title = element_text(hjust = 0.5))
Total.Flocculonodular.lobe.Age.plot
ggsave("Total.Flocculonodular.lobe.Age.plot.pdf", plot = Total.Flocculonodular.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Central.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Central, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 770, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME central") +
theme(plot.title = element_text(hjust = 0.5))
Total.Central.Age.plot
ggsave("Total.Central.Age.plot.pdf", plot = Total.Central.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Culmen.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Culmen, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2600, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME CULMEN") +
theme(plot.title = element_text(hjust = 0.5))
Total.Culmen.Age.plot
ggsave("Total.Culmen.Age.plot.pdf", plot = Total.Culmen.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Declive.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Declive, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1250, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME DECLIVE") +
theme(plot.title = element_text(hjust = 0.5))
Total.Declive.Age.plot
ggsave("Total.Declive.Age.plot.pdf", plot = Total.Declive.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Folium.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Folium, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 630, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME FOLIUM") +
theme(plot.title = element_text(hjust = 0.5))
Total.Folium.Age.plot
ggsave("Total.Folium.Age.plot.pdf", plot = Total.Folium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Tuber.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Tuber, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 570, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME TUBER") +
theme(plot.title = element_text(hjust = 0.5))
Total.Tuber.Age.plot
ggsave("Total.Tuber.Age.plot.pdf", plot = Total.Tuber.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Pyramid.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Pyramid, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 700, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME PYRAMID") +
theme(plot.title = element_text(hjust = 0.5))
Total.Pyramid.Age.plot
ggsave("Total.Pyramid.Age.plot.pdf", plot = Total.Pyramid.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Uvula.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Uvula, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1030, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME UVULA") +
theme(plot.title = element_text(hjust = 0.5))
Total.Uvula.Age.plot
ggsave("Total.Uvula.Age.plot.pdf", plot = Total.Uvula.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Nodule.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Nodule, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 230, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME NODULE") +
theme(plot.title = element_text(hjust = 0.5))
Total.Nodule.Age.plot
ggsave("Total.Nodule.Age.plot.pdf", plot = Total.Nodule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Ala.lobuli.centralis.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Ala.lobuli.centralis, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 16000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME ALA LOBULI CENTRALIS") +
theme(plot.title = element_text(hjust = 0.5))
Total.Ala.lobuli.centralis.Age.plot
ggsave("Total.Ala.lobuli.centralis.Age.plot.pdf", plot = Total.Ala.lobuli.centralis.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.AQL.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=AQL, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 21000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME AQL") +
theme(plot.title = element_text(hjust = 0.5))
Total.AQL.Age.plot
ggsave("Total.AQL.Age.plot.pdf", plot = Total.AQL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.PQL.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=PQL, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 15500, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME PQL") +
theme(plot.title = element_text(hjust = 0.5))
Total.PQL.Age.plot
ggsave("Total.PQL.Age.plot.pdf", plot = Total.PQL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.SSL.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=SSL, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 15500, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME SSL") +
theme(plot.title = element_text(hjust = 0.5))
Total.SSL.Age.plot
ggsave("Total.SSL.Age.plot.pdf", plot = Total.SSL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.ISL.gracile.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=ISL.gracile, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 42000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME ISL/gracile") +
theme(plot.title = element_text(hjust = 0.5))
Total.ISL.gracile.Age.plot
ggsave("Total.ISL.gracile.Age.plot.pdf", plot = Total.ISL.gracile.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Biventer.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Biventer, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 21000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME biventer") +
theme(plot.title = element_text(hjust = 0.5))
Total.Biventer.Age.plot
ggsave("Total.Biventer.Age.plot.pdf", plot = Total.Biventer.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Tonsilla.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Tonsilla, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 7700, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME tonsilla") +
theme(plot.title = element_text(hjust = 0.5))
Total.Tonsilla.Age.plot
ggsave("Total.Tonsilla.Age.plot.pdf", plot = Total.Tonsilla.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Total.Flocculus.Age.plot <- ggplot(Brainstem.Cerebellum.absolute, aes(y=Flocculus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1050, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME flocculus") +
theme(plot.title = element_text(hjust = 0.5))
Total.Flocculus.Age.plot
ggsave("Total.Flocculus.Age.plot.pdf", plot = Total.Flocculus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative Volumes
#Brainstem.Cerebellum.absolute <- select(Brainstem.Cerebellum.absolute, - c(Age, Gender))
Brainstem.Cerebellum.relative <- (100 * (Brainstem.Cerebellum.absolute/All.Volumes$`Total encephalic volume (without ventricles)`))‘/’ not meaningful for factorsBrainstem.Cerebellum.relative1 <- Brainstem.Cerebellum.relative[,c(1:10)]
Brainstem.Cerebellum.relative2 <- Brainstem.Cerebellum.relative[,-c(1:10)]
Table.Brainstem.Cerebellum.relative <- CreateTableOne(
vars = c("Mesencephalon",
"Pons",
"Medulla.oblongata",
"Cerebellar.peduncles",
"Vermis",
"Hemisphere",
"Anterior.lobe",
"Medial.lobe",
"Posterior.lobe",
"Flocculonodular.lobe",
"Central",
"Culmen",
"Declive",
"Folium",
"Tuber",
"Pyramid",
"Uvula",
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile",
"Biventer",
"Tonsilla",
"Flocculus"),
data = Brainstem.Cerebellum.relative)
Brainstem.Cerebellum.relative$Gender <- All.Volumes$Gender
Table.Brainstem.Cerebellum.relative.stratified.gender <- CreateTableOne(
vars = c("Mesencephalon",
"Pons",
"Medulla.oblongata",
"Cerebellar.peduncles",
"Vermis",
"Hemisphere",
"Anterior.lobe",
"Medial.lobe",
"Posterior.lobe",
"Flocculonodular.lobe",
"Central",
"Culmen",
"Declive",
"Folium",
"Tuber",
"Pyramid",
"Uvula",
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile",
"Biventer",
"Tonsilla",
"Flocculus"),
strata = c("Gender"),
data = Brainstem.Cerebellum.relative)
Table.Brainstem.Cerebellum.relative <- print(Table.Brainstem.Cerebellum.relative, contDigits = 10)
Overall
n 30
Mesencephalon (mean (SD)) 0.9207623291 (0.1105191701)
Pons (mean (SD)) 1.4005625067 (0.1727801503)
Medulla.oblongata (mean (SD)) 0.2524061308 (0.0357809075)
Cerebellar.peduncles (mean (SD)) 0.6213805087 (0.0717960379)
Vermis (mean (SD)) 0.5465728045 (0.0706767901)
Hemisphere (mean (SD)) 10.1852538324 (1.1214009177)
Anterior.lobe (mean (SD)) 2.9710945895 (0.4958752121)
Medial.lobe (mean (SD)) 2.6417850797 (0.3687226983)
Posterior.lobe (mean (SD)) 5.0206290086 (0.6951742366)
Flocculonodular.lobe (mean (SD)) 0.0983188189 (0.0174019012)
Central (mean (SD)) 0.0444979014 (0.0120965572)
Culmen (mean (SD)) 0.2006556434 (0.0318361517)
Declive (mean (SD)) 0.0945552953 (0.0166943131)
Folium (mean (SD)) 0.0405176202 (0.0111004272)
Tuber (mean (SD)) 0.0355347542 (0.0084832011)
Pyramid (mean (SD)) 0.0389858949 (0.0169198532)
Uvula (mean (SD)) 0.0722229678 (0.0122092488)
Nodule (mean (SD)) 0.0196238560 (0.0037047686)
Ala.lobuli.centralis (mean (SD)) 1.1040297116 (0.2737967502)
AQL (mean (SD)) 1.6219113332 (0.3797422298)
PQL (mean (SD)) 1.2023063114 (0.2187152577)
SSL (mean (SD)) 1.3044058528 (0.2289872084)
ISL.gracile (mean (SD)) 3.2205673072 (0.6269680120)
Biventer (mean (SD)) 1.1070477516 (0.3117981715)
Tonsilla (mean (SD)) 0.5462703329 (0.1165790011)
Flocculus (mean (SD)) 0.0786949629 (0.0155073243)Table.Brainstem.Cerebellum.relative.stratified.gender <- print(Table.Brainstem.Cerebellum.relative.stratified.gender, contDigits = 10) Stratified by Gender
f m p test
n 14 16
Mesencephalon (mean (SD)) 0.9547649875 (0.1034767729) 0.8910100029 (0.1109575365) 0.116
Pons (mean (SD)) 1.4035711394 (0.1967402245) 1.3979299532 (0.1554158961) 0.931
Medulla.oblongata (mean (SD)) 0.2607569676 (0.0330361387) 0.2450991485 (0.0375138378) 0.238
Cerebellar.peduncles (mean (SD)) 0.6310216888 (0.0701223453) 0.6129444762 (0.0744412960) 0.501
Vermis (mean (SD)) 0.5614348351 (0.0700918934) 0.5335685277 (0.0708028232) 0.289
Hemisphere (mean (SD)) 10.3668103244 (1.1765578929) 10.0263919019 (1.0834401354) 0.416
Anterior.lobe (mean (SD)) 3.0346127423 (0.5156137908) 2.9155162059 (0.4877717301) 0.521
Medial.lobe (mean (SD)) 2.7827036278 (0.3948805255) 2.5184813501 (0.3048895712) 0.048
Posterior.lobe (mean (SD)) 5.0087783797 (0.7749481080) 5.0309983090 (0.6431165505) 0.932
Flocculonodular.lobe (mean (SD)) 0.1022078567 (0.0181582471) 0.0949159108 (0.0165298769) 0.259
Central (mean (SD)) 0.0484920828 (0.0133583251) 0.0410029928 (0.0100163605) 0.091
Culmen (mean (SD)) 0.2045461757 (0.0285689132) 0.1972514276 (0.0350094972) 0.541
Declive (mean (SD)) 0.0983933628 (0.0195622332) 0.0911969862 (0.0134679064) 0.246
Folium (mean (SD)) 0.0418471653 (0.0093337522) 0.0393542682 (0.0126343898) 0.549
Tuber (mean (SD)) 0.0353483403 (0.0083807937) 0.0356978664 (0.0088429783) 0.913
Pyramid (mean (SD)) 0.0367053806 (0.0154573992) 0.0409813449 (0.0183657952) 0.499
Uvula (mean (SD)) 0.0754553918 (0.0127880168) 0.0693945969 (0.0113216555) 0.179
Nodule (mean (SD)) 0.0207119244 (0.0037738611) 0.0186717962 (0.0034814791) 0.135
Ala.lobuli.centralis (mean (SD)) 1.1123470528 (0.2881107524) 1.0967520380 (0.2699453959) 0.879
AQL (mean (SD)) 1.6692274310 (0.4046689735) 1.5805097476 (0.3646286798) 0.533
PQL (mean (SD)) 1.2462629933 (0.2677358247) 1.1638442147 (0.1642482748) 0.311
SSL (mean (SD)) 1.3962001064 (0.2267285630) 1.2240858809 (0.2051269225) 0.038
ISL.gracile (mean (SD)) 3.2680364313 (0.5872772943) 3.1790318236 (0.6761064068) 0.705
Biventer (mean (SD)) 1.0339684324 (0.3635994447) 1.1709921559 (0.2530448256) 0.236
Tonsilla (mean (SD)) 0.5592644032 (0.1111017647) 0.5349005213 (0.1236204433) 0.577
Flocculus (mean (SD)) 0.0814959323 (0.0161449011) 0.0762441146 (0.0150096453) 0.364
Table.Brainstem.Cerebellum.relative.RSD <- as.data.frame(Table.Brainstem.Cerebellum.relative)
Table.Brainstem.Cerebellum.relative.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.Brainstem.Cerebellum.relative.RSD[-1,]),
' (',fixed=TRUE)))
Table.Brainstem.Cerebellum.relative.RSD <- data.frame(cbind(str_replace_all(Table.Brainstem.Cerebellum.relative.RSD$X1, "[ ]", ""),
str_replace_all(Table.Brainstem.Cerebellum.relative.RSD$X2, "[)]", "")))
Table.Brainstem.Cerebellum.relative.RSD$X1 <- as.character(Table.Brainstem.Cerebellum.relative.RSD$X1)
Table.Brainstem.Cerebellum.relative.RSD$X2 <- as.character(Table.Brainstem.Cerebellum.relative.RSD$X2)
Table.Brainstem.Cerebellum.relative.RSD <- as.data.frame(sapply(Table.Brainstem.Cerebellum.relative.RSD, as.numeric))
Table.Brainstem.Cerebellum.relative.RSD <- as.data.frame(Table.Brainstem.Cerebellum.relative.RSD$X2/Table.Brainstem.Cerebellum.relative.RSD$X1)
Table.Brainstem.Cerebellum.relative.RSD <- round(Table.Brainstem.Cerebellum.relative.RSD * 100, 1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD <- as.data.frame(Table.Brainstem.Cerebellum.relative.stratified.gender)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD <- select(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD, - c(p, test))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X1 <- as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X2 <- as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X2)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female, as.numeric))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- as.data.frame(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X2/Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- round(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female * 100, 1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X1 <- as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X2 <- as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X2)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male, as.numeric))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- as.data.frame(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X2/Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- round(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male * 100, 1)
kable(Table.Brainstem.Cerebellum.relative)
| Overall | |
|---|---|
| n | 30 |
| Mesencephalon (mean (SD)) | 0.9207623291 (0.1105191701) |
| Pons (mean (SD)) | 1.4005625067 (0.1727801503) |
| Medulla.oblongata (mean (SD)) | 0.2524061308 (0.0357809075) |
| Cerebellar.peduncles (mean (SD)) | 0.6213805087 (0.0717960379) |
| Vermis (mean (SD)) | 0.5465728045 (0.0706767901) |
| Hemisphere (mean (SD)) | 10.1852538324 (1.1214009177) |
| Anterior.lobe (mean (SD)) | 2.9710945895 (0.4958752121) |
| Medial.lobe (mean (SD)) | 2.6417850797 (0.3687226983) |
| Posterior.lobe (mean (SD)) | 5.0206290086 (0.6951742366) |
| Flocculonodular.lobe (mean (SD)) | 0.0983188189 (0.0174019012) |
| Central (mean (SD)) | 0.0444979014 (0.0120965572) |
| Culmen (mean (SD)) | 0.2006556434 (0.0318361517) |
| Declive (mean (SD)) | 0.0945552953 (0.0166943131) |
| Folium (mean (SD)) | 0.0405176202 (0.0111004272) |
| Tuber (mean (SD)) | 0.0355347542 (0.0084832011) |
| Pyramid (mean (SD)) | 0.0389858949 (0.0169198532) |
| Uvula (mean (SD)) | 0.0722229678 (0.0122092488) |
| Nodule (mean (SD)) | 0.0196238560 (0.0037047686) |
| Ala.lobuli.centralis (mean (SD)) | 1.1040297116 (0.2737967502) |
| AQL (mean (SD)) | 1.6219113332 (0.3797422298) |
| PQL (mean (SD)) | 1.2023063114 (0.2187152577) |
| SSL (mean (SD)) | 1.3044058528 (0.2289872084) |
| ISL.gracile (mean (SD)) | 3.2205673072 (0.6269680120) |
| Biventer (mean (SD)) | 1.1070477516 (0.3117981715) |
| Tonsilla (mean (SD)) | 0.5462703329 (0.1165790011) |
| Flocculus (mean (SD)) | 0.0786949629 (0.0155073243) |
kable(Table.Brainstem.Cerebellum.relative.RSD)
| Table.Brainstem.Cerebellum.relative.RSDX2/Table.Brainstem.Cerebellum.relative.RSDX1 |
|---|
| 12.0 |
| 12.3 |
| 14.2 |
| 11.6 |
| 12.9 |
| 11.0 |
| 16.7 |
| 14.0 |
| 13.8 |
| 17.7 |
| 27.2 |
| 15.9 |
| 17.7 |
| 27.4 |
| 23.9 |
| 43.4 |
| 16.9 |
| 18.9 |
| 24.8 |
| 23.4 |
| 18.2 |
| 17.6 |
| 19.5 |
| 28.2 |
| 21.3 |
| 19.7 |
kable(Table.Brainstem.Cerebellum.relative.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Mesencephalon (mean (SD)) | 0.9547649875 (0.1034767729) | 0.8910100029 (0.1109575365) | 0.116 | |
| Pons (mean (SD)) | 1.4035711394 (0.1967402245) | 1.3979299532 (0.1554158961) | 0.931 | |
| Medulla.oblongata (mean (SD)) | 0.2607569676 (0.0330361387) | 0.2450991485 (0.0375138378) | 0.238 | |
| Cerebellar.peduncles (mean (SD)) | 0.6310216888 (0.0701223453) | 0.6129444762 (0.0744412960) | 0.501 | |
| Vermis (mean (SD)) | 0.5614348351 (0.0700918934) | 0.5335685277 (0.0708028232) | 0.289 | |
| Hemisphere (mean (SD)) | 10.3668103244 (1.1765578929) | 10.0263919019 (1.0834401354) | 0.416 | |
| Anterior.lobe (mean (SD)) | 3.0346127423 (0.5156137908) | 2.9155162059 (0.4877717301) | 0.521 | |
| Medial.lobe (mean (SD)) | 2.7827036278 (0.3948805255) | 2.5184813501 (0.3048895712) | 0.048 | |
| Posterior.lobe (mean (SD)) | 5.0087783797 (0.7749481080) | 5.0309983090 (0.6431165505) | 0.932 | |
| Flocculonodular.lobe (mean (SD)) | 0.1022078567 (0.0181582471) | 0.0949159108 (0.0165298769) | 0.259 | |
| Central (mean (SD)) | 0.0484920828 (0.0133583251) | 0.0410029928 (0.0100163605) | 0.091 | |
| Culmen (mean (SD)) | 0.2045461757 (0.0285689132) | 0.1972514276 (0.0350094972) | 0.541 | |
| Declive (mean (SD)) | 0.0983933628 (0.0195622332) | 0.0911969862 (0.0134679064) | 0.246 | |
| Folium (mean (SD)) | 0.0418471653 (0.0093337522) | 0.0393542682 (0.0126343898) | 0.549 | |
| Tuber (mean (SD)) | 0.0353483403 (0.0083807937) | 0.0356978664 (0.0088429783) | 0.913 | |
| Pyramid (mean (SD)) | 0.0367053806 (0.0154573992) | 0.0409813449 (0.0183657952) | 0.499 | |
| Uvula (mean (SD)) | 0.0754553918 (0.0127880168) | 0.0693945969 (0.0113216555) | 0.179 | |
| Nodule (mean (SD)) | 0.0207119244 (0.0037738611) | 0.0186717962 (0.0034814791) | 0.135 | |
| Ala.lobuli.centralis (mean (SD)) | 1.1123470528 (0.2881107524) | 1.0967520380 (0.2699453959) | 0.879 | |
| AQL (mean (SD)) | 1.6692274310 (0.4046689735) | 1.5805097476 (0.3646286798) | 0.533 | |
| PQL (mean (SD)) | 1.2462629933 (0.2677358247) | 1.1638442147 (0.1642482748) | 0.311 | |
| SSL (mean (SD)) | 1.3962001064 (0.2267285630) | 1.2240858809 (0.2051269225) | 0.038 | |
| ISL.gracile (mean (SD)) | 3.2680364313 (0.5872772943) | 3.1790318236 (0.6761064068) | 0.705 | |
| Biventer (mean (SD)) | 1.0339684324 (0.3635994447) | 1.1709921559 (0.2530448256) | 0.236 | |
| Tonsilla (mean (SD)) | 0.5592644032 (0.1111017647) | 0.5349005213 (0.1236204433) | 0.577 | |
| Flocculus (mean (SD)) | 0.0814959323 (0.0161449011) | 0.0762441146 (0.0150096453) | 0.364 |
kable(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female)
| Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.femaleX2/Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.femaleX1 |
|---|
| 10.8 |
| 14.0 |
| 12.7 |
| 11.1 |
| 12.5 |
| 11.3 |
| 17.0 |
| 14.2 |
| 15.5 |
| 17.8 |
| 27.5 |
| 14.0 |
| 19.9 |
| 22.3 |
| 23.7 |
| 42.1 |
| 16.9 |
| 18.2 |
| 25.9 |
| 24.2 |
| 21.5 |
| 16.2 |
| 18.0 |
| 35.2 |
| 19.9 |
| 19.8 |
kable(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male)
| Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.maleX2/Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.maleX1 |
|---|
| 12.5 |
| 11.1 |
| 15.3 |
| 12.1 |
| 13.3 |
| 10.8 |
| 16.7 |
| 12.1 |
| 12.8 |
| 17.4 |
| 24.4 |
| 17.7 |
| 14.8 |
| 32.1 |
| 24.8 |
| 44.8 |
| 16.3 |
| 18.6 |
| 24.6 |
| 23.1 |
| 14.1 |
| 16.8 |
| 21.3 |
| 21.6 |
| 23.1 |
| 19.7 |
NA
names.anatomical.structures.temporary <- c("Mesencephalon",
"Pons",
"Medulla.oblongata",
"Cerebellar.peduncles",
"Vermis",
"Hemisphere",
"Anterior.lobe",
"Medial.lobe",
"Posterior.lobe",
"Flocculonodular.lobe")
names.anatomical.structures.definitive <- c("Mesencephalon",
"Pons",
"Medulla oblongata",
"Cerebellar peduncles",
"Vermis",
"Hemisphere",
"Anterior lobe",
"Medial lobe",
"Posterior lobe",
"Flocculonodular lobe")
Brainstem.Cerebellum.relative.plotdata1 <- gather(Brainstem.Cerebellum.relative1, "anatomical.structure", "relative.volume")
Brainstem.Cerebellum.relative.plotdata1$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.relative.plotdata1$Age <- All.Volumes$`Age (years)`
Brainstem.Cerebellum.relative.plotdata1$Gender <- factor(Brainstem.Cerebellum.relative.plotdata1$Gender, levels = c("f", "m"), c("f", "m"))
Brainstem.Cerebellum.relative.plotdata1$anatomical.structure <- factor(Brainstem.Cerebellum.relative.plotdata1$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Brainstem.Cerebellum.relative.plot1 <- ggplot(Brainstem.Cerebellum.relative.plotdata1, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Relative volume (in %)") +
theme_minimal() +
coord_flip() +
ggtitle("BRAINSTEM & CEREBELLUM") +
theme(plot.title = element_text(hjust = 0.5))
Brainstem.Cerebellum.relative.plot1
ggsave("Brainstem.Cerebellum.relative.plot1.pdf", plot = Brainstem.Cerebellum.relative.plot1, width = 12, height = 6, units = "in", dpi = 600)
names.anatomical.structures.temporary <- c(
"Central",
"Culmen",
"Declive",
"Folium",
"Tuber",
"Pyramid",
"Uvula",
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile",
"Biventer",
"Tonsilla",
"Flocculus")
names.anatomical.structures.definitive <- c(
"Central",
"Culmen",
"Declive",
"Folium",
"Tuber",
"Pyramid",
"Uvula",
"Nodule",
"Ala lobuli centralis",
"AQL",
"PQL",
"SSL",
"ISL/gracile",
"Biventer",
"Tonsilla",
"Flocculus")
Brainstem.Cerebellum.relative.plotdata2 <- gather(Brainstem.Cerebellum.relative2, "anatomical.structure", "relative.volume")
Brainstem.Cerebellum.relative.plotdata2$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.relative.plotdata2$Age <- All.Volumes$`Age (years)`
Brainstem.Cerebellum.relative.plotdata2$Gender <- factor(Brainstem.Cerebellum.relative.plotdata2$Gender, levels = c("f", "m"), c("f", "m"))
Brainstem.Cerebellum.relative.plotdata2$anatomical.structure <- factor(Brainstem.Cerebellum.relative.plotdata2$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Brainstem.Cerebellum.relative.plot2 <- ggplot(Brainstem.Cerebellum.relative.plotdata2, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Relative volume (in %)") +
theme_minimal() +
coord_flip() +
ggtitle("CEREBELLAR LOBES & LOBULES") +
theme(plot.title = element_text(hjust = 0.5))
Brainstem.Cerebellum.relative.plot2
ggsave("Brainstem.Cerebellum.relative.plot2.pdf", plot = Brainstem.Cerebellum.relative.plot2, width = 12, height = 6, units = "in", dpi = 600)
#Brainstem.Cerebellum.relative$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.relative$Age <- All.Volumes$`Age (years)`
Relative.Mesencephalon.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Mesencephalon, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.02, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME mesencephalon") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Mesencephalon.Age.plot
ggsave("Relative.Mesencephalon.Age.plot.pdf", plot = Relative.Mesencephalon.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Pons.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Pons, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.93, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME pons") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Pons.Age.plot
ggsave("Relative.Pons.Age.plot.pdf", plot = Relative.Pons.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Medulla.oblongata.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Medulla.oblongata, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.33, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME medulla oblongata") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Medulla.oblongata.Age.plot
ggsave("Relative.Medulla.oblongata.Age.plot.pdf", plot = Relative.Medulla.oblongata.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Cerebellar.peduncles.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Cerebellar.peduncles, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.83, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME cerebellar peduncle") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Cerebellar.peduncles.Age.plot
ggsave("Relative.Cerebellar.peduncles.Age.plot.pdf", plot = Relative.Cerebellar.peduncles.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Vermis.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Vermis, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.67, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME vermis") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Vermis.Age.plot
ggsave("Relative.Vermis.Age.plot.pdf", plot = Relative.Vermis.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Hemisphere.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Hemisphere, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 11.7, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME hemisphere") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Hemisphere.Age.plot
ggsave("Relative.Hemisphere.Age.plot.pdf", plot = Relative.Hemisphere.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Anterior.lobe.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Anterior.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3.6, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME anterior lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Anterior.lobe.Age.plot
ggsave("Relative.Anterior.lobe.Age.plot.pdf", plot = Relative.Anterior.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Medial.lobe.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Medial.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3.1, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME medial lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Medial.lobe.Age.plot
ggsave("Relative.Medial.lobe.Age.plot.pdf", plot = Relative.Medial.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Posterior.lobe.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Posterior.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 6.6, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME posterior lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Posterior.lobe.Age.plot
ggsave("Relative.Posterior.lobe.Age.plot.pdf", plot = Relative.Posterior.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Flocculonodular.lobe.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Flocculonodular.lobe, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.121, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME flocculonodular lobe") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Flocculonodular.lobe.Age.plot
ggsave("Relative.Flocculonodular.lobe.Age.plot.pdf", plot = Relative.Flocculonodular.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Central.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Central, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.072, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME CENTRAL") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Central.Age.plot
ggsave("Relative.Central.Age.plot.pdf", plot = Relative.Central.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Culmen.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Culmen, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.228, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME CULMEN") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Culmen.Age.plot
ggsave("Relative.Culmen.Age.plot.pdf", plot = Relative.Culmen.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Declive.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Declive, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.13, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME DECLIVE") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Declive.Age.plot
ggsave("Relative.Declive.Age.plot.pdf", plot = Relative.Declive.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Folium.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Folium, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.062, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME FOLIUM") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Folium.Age.plot
ggsave("Relative.Folium.Age.plot.pdf", plot = Relative.Folium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Tuber.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Tuber, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.052, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME TUBER") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Tuber.Age.plot
ggsave("Relative.Tuber.Age.plot.pdf", plot = Relative.Tuber.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Pyramid.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Pyramid, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.062, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME PYRAMID") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Pyramid.Age.plot
ggsave("Relative.Pyramid.Age.plot.pdf", plot = Relative.Pyramid.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Uvula.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Uvula, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.097, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME UVULA") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Uvula.Age.plot
ggsave("Relative.Uvula.Age.plot.pdf", plot = Relative.Uvula.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Nodule.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Nodule, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.023, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME NODULE") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Nodule.Age.plot
ggsave("Relative.Nodule.Age.plot.pdf", plot = Relative.Nodule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Ala.lobuli.centralis.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Ala.lobuli.centralis, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.45, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME ALA LOBULI CENTRALIS") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Ala.lobuli.centralis.Age.plot
ggsave("Relative.Ala.lobuli.centralis.Age.plot.pdf", plot = Relative.Ala.lobuli.centralis.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.AQL.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=AQL, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.7, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.1, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME AQL") +
theme(plot.title = element_text(hjust = 0.5))
Relative.AQL.Age.plot
ggsave("Relative.AQL.Age.plot.pdf", plot = Relative.AQL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.PQL.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=PQL, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.55, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME PQL") +
theme(plot.title = element_text(hjust = 0.5))
Relative.PQL.Age.plot
ggsave("Relative.PQL.Age.plot.pdf", plot = Relative.PQL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.SSL.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=SSL, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.55, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME SSL") +
theme(plot.title = element_text(hjust = 0.5))
Relative.SSL.Age.plot
ggsave("Relative.SSL.Age.plot.pdf", plot = Relative.SSL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.ISL.gracile.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=ISL.gracile, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4.2, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME ISL/GRACILE") +
theme(plot.title = element_text(hjust = 0.5))
Relative.ISL.gracile.Age.plot
ggsave("Relative.ISL.gracile.Age.plot.pdf", plot = Relative.ISL.gracile.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Biventer.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Biventer, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.7, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME BIVENTER") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Biventer.Age.plot
ggsave("Relative.Biventer.Age.plot.pdf", plot = Relative.Biventer.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Tonsilla.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Tonsilla, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.72, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME TONSILLA") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Tonsilla.Age.plot
ggsave("Relative.Tonsilla.Age.plot.pdf", plot = Relative.Tonsilla.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Flocculus.Age.plot <- ggplot(Brainstem.Cerebellum.relative, aes(y=Flocculus, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.105, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME FLOCCULUS") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Flocculus.Age.plot
ggsave("Relative.Flocculus.Age.plot.pdf", plot = Relative.Flocculus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
The Ventricular System
Absolute Volumes
Total.volume.ventricles <- All.Volumes$`Total volume ventricles`
LV.total <- All.Volumes$`Total volume lateral ventricles`
LV.frontal.horn <- All.Volumes$`Total volume frontal horn`
LV.body <- All.Volumes$`Total volume body of LV`
LV.atrium <- All.Volumes$`Total volume atrium`
LV.occipital.horn <- All.Volumes$`Total volume occipital horn`
LV.temporal.horn <- All.Volumes$`Total volume temporal horn`
Third.ventricle <- All.Volumes$`3rd ventricle`
Fourth.ventricle.total <- All.Volumes$`4th ventricle`
Apex <- All.Volumes$`Apex of 4th`
Lateral.recess <- All.Volumes$`Total volume lateral recess`
Obex <- All.Volumes$`Obex of 4th`
Fastigium <- All.Volumes$Fastigium
Ventricles.absolute <- as.data.frame(cbind(
Total.volume.ventricles,
LV.total,
LV.frontal.horn,
LV.body,
LV.atrium,
LV.occipital.horn,
LV.temporal.horn,
Third.ventricle,
Fourth.ventricle.total,
Apex,
Lateral.recess,
Obex,
Fastigium
))
Ventricles.absolute$Gender <- All.Volumes$Gender
Table.Ventricles.absolute <- CreateTableOne(
vars = c("Total.volume.ventricles",
"LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
data = Ventricles.absolute)
Table.Ventricles.absolute.stratified.gender <- CreateTableOne(
vars = c("Total.volume.ventricles",
"LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
strata = c("Gender"),
data = Ventricles.absolute)
Table.Ventricles.absolute <- print(Table.Ventricles.absolute, contDigits = 10)
Overall
n 30
Total.volume.ventricles (mean (SD)) 21184.5666666667 (16714.0388331514)
LV.total (mean (SD)) 18429.8333333333 (15998.6774174991)
LV.frontal.horn (mean (SD)) 6124.0666666667 (5138.9726528205)
LV.body (mean (SD)) 4852.4333333333 (4797.8072123759)
LV.atrium (mean (SD)) 5499.3666666667 (5464.3878193472)
LV.occipital.horn (mean (SD)) 1093.7000000000 (1013.8288691205)
LV.temporal.horn (mean (SD)) 860.3333333333 (619.2339724560)
Third.ventricle (mean (SD)) 1134.4000000000 (807.5322391344)
Fourth.ventricle.total (mean (SD)) 1620.4000000000 (345.0322923418)
Apex (mean (SD)) 162.8333333333 (45.5911994936)
Lateral.recess (mean (SD)) 198.6666666667 (46.2708169989)
Obex (mean (SD)) 174.4000000000 (109.4032717156)
Fastigium (mean (SD)) 199.3333333333 (53.8640263379) Table.Ventricles.absolute.stratified.gender <- print(Table.Ventricles.absolute.stratified.gender, contDigits = 10) Stratified by Gender
f m p test
n 14 16
Total.volume.ventricles (mean (SD)) 19612.2857142857 (16045.3969552189) 22560.3125000000 (17681.6478029952) 0.638
LV.total (mean (SD)) 17214.2857142857 (15961.2766090000) 19493.4375000000 (16476.3998614938) 0.704
LV.frontal.horn (mean (SD)) 6001.6428571429 (5667.2323133437) 6231.1875000000 (4816.2291365583) 0.905
LV.body (mean (SD)) 4804.7857142857 (5434.6698035508) 4894.1250000000 (4347.6185492750) 0.960
LV.atrium (mean (SD)) 4547.0714285714 (3755.7582347087) 6332.6250000000 (6626.9495031022) 0.381
LV.occipital.horn (mean (SD)) 1175.2857142857 (1256.6319104025) 1022.3125000000 (779.0714745773) 0.688
LV.temporal.horn (mean (SD)) 685.4285714286 (400.5451504917) 1013.3750000000 (740.7818279809) 0.151
Third.ventricle (mean (SD)) 847.3571428571 (371.9043729742) 1385.5625000000 (998.3400201501) 0.068
Fourth.ventricle.total (mean (SD)) 1550.7142857143 (294.6558432037) 1681.3750000000 (382.6409761992) 0.309
Apex (mean (SD)) 153.0000000000 (44.1047703682) 171.4375000000 (46.5130358072) 0.277
Lateral.recess (mean (SD)) 195.2857142857 (45.7391072723) 201.6250000000 (48.0220435495) 0.715
Obex (mean (SD)) 179.5714285714 (109.0622364917) 169.8750000000 (113.0698161904) 0.813
Fastigium (mean (SD)) 194.2142857143 (54.4880771312) 203.8125000000 (54.6835974798) 0.635 write.csv(Table.Ventricles.absolute, "Table.Ventricles.absolute.csv")
write.csv(Table.Ventricles.absolute.stratified.gender, "Table.Ventricles.absolute.stratified.gender.csv")
Table.Ventricles.absolute.RSD <- as.data.frame(Table.Ventricles.absolute)
Table.Ventricles.absolute.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.absolute.RSD[-1,]),' (',fixed=TRUE)))
Table.Ventricles.absolute.RSD <- data.frame(cbind(str_replace_all(Table.Ventricles.absolute.RSD$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.absolute.RSD$X2, "[)]", "")))
Table.Ventricles.absolute.RSD$X1 <- as.character(Table.Ventricles.absolute.RSD$X1)
Table.Ventricles.absolute.RSD$X2 <- as.character(Table.Ventricles.absolute.RSD$X2)
Table.Ventricles.absolute.RSD <- as.data.frame(sapply(Table.Ventricles.absolute.RSD, as.numeric))
Table.Ventricles.absolute.RSD <- as.data.frame(Table.Ventricles.absolute.RSD$X2/Table.Ventricles.absolute.RSD$X1)
Table.Ventricles.absolute.RSD <- round(Table.Ventricles.absolute.RSD * 100, 1)
Table.Ventricles.absolute.stratified.gender.RSD <- as.data.frame(Table.Ventricles.absolute.stratified.gender)
Table.Ventricles.absolute.stratified.gender.RSD <- select(Table.Ventricles.absolute.stratified.gender.RSD, - c(p, test))
Table.Ventricles.absolute.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.absolute.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.Ventricles.absolute.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Ventricles.absolute.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.absolute.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Ventricles.absolute.stratified.gender.RSD.female$X1 <- as.character(Table.Ventricles.absolute.stratified.gender.RSD.female$X1)
Table.Ventricles.absolute.stratified.gender.RSD.female$X2 <- as.character(Table.Ventricles.absolute.stratified.gender.RSD.female$X2)
Table.Ventricles.absolute.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Ventricles.absolute.stratified.gender.RSD.female, as.numeric))
Table.Ventricles.absolute.stratified.gender.RSD.female <- as.data.frame(Table.Ventricles.absolute.stratified.gender.RSD.female$X2/Table.Ventricles.absolute.stratified.gender.RSD.female$X1)
Table.Ventricles.absolute.stratified.gender.RSD.female <- round(Table.Ventricles.absolute.stratified.gender.RSD.female * 100, 1)
Table.Ventricles.absolute.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.absolute.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.Ventricles.absolute.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Ventricles.absolute.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.absolute.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Ventricles.absolute.stratified.gender.RSD.male$X1 <- as.character(Table.Ventricles.absolute.stratified.gender.RSD.male$X1)
Table.Ventricles.absolute.stratified.gender.RSD.male$X2 <- as.character(Table.Ventricles.absolute.stratified.gender.RSD.male$X2)
Table.Ventricles.absolute.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Ventricles.absolute.stratified.gender.RSD.male, as.numeric))
Table.Ventricles.absolute.stratified.gender.RSD.male <- as.data.frame(Table.Ventricles.absolute.stratified.gender.RSD.male$X2/Table.Ventricles.absolute.stratified.gender.RSD.male$X1)
Table.Ventricles.absolute.stratified.gender.RSD.male <- round(Table.Ventricles.absolute.stratified.gender.RSD.male * 100, 1)
kable(Table.Ventricles.absolute)
| Overall | |
|---|---|
| n | 30 |
| Total.volume.ventricles (mean (SD)) | 21184.5666666667 (16714.0388331514) |
| LV.total (mean (SD)) | 18429.8333333333 (15998.6774174991) |
| LV.frontal.horn (mean (SD)) | 6124.0666666667 (5138.9726528205) |
| LV.body (mean (SD)) | 4852.4333333333 (4797.8072123759) |
| LV.atrium (mean (SD)) | 5499.3666666667 (5464.3878193472) |
| LV.occipital.horn (mean (SD)) | 1093.7000000000 (1013.8288691205) |
| LV.temporal.horn (mean (SD)) | 860.3333333333 (619.2339724560) |
| Third.ventricle (mean (SD)) | 1134.4000000000 (807.5322391344) |
| Fourth.ventricle.total (mean (SD)) | 1620.4000000000 (345.0322923418) |
| Apex (mean (SD)) | 162.8333333333 (45.5911994936) |
| Lateral.recess (mean (SD)) | 198.6666666667 (46.2708169989) |
| Obex (mean (SD)) | 174.4000000000 (109.4032717156) |
| Fastigium (mean (SD)) | 199.3333333333 (53.8640263379) |
kable(Table.Ventricles.absolute.RSD)
| Table.Ventricles.absolute.RSDX2/Table.Ventricles.absolute.RSDX1 |
|---|
| 78.9 |
| 86.8 |
| 83.9 |
| 98.9 |
| 99.4 |
| 92.7 |
| 72.0 |
| 71.2 |
| 21.3 |
| 28.0 |
| 23.3 |
| 62.7 |
| 27.0 |
kable(Table.Ventricles.absolute.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Total.volume.ventricles (mean (SD)) | 19612.2857142857 (16045.3969552189) | 22560.3125000000 (17681.6478029952) | 0.638 | |
| LV.total (mean (SD)) | 17214.2857142857 (15961.2766090000) | 19493.4375000000 (16476.3998614938) | 0.704 | |
| LV.frontal.horn (mean (SD)) | 6001.6428571429 (5667.2323133437) | 6231.1875000000 (4816.2291365583) | 0.905 | |
| LV.body (mean (SD)) | 4804.7857142857 (5434.6698035508) | 4894.1250000000 (4347.6185492750) | 0.960 | |
| LV.atrium (mean (SD)) | 4547.0714285714 (3755.7582347087) | 6332.6250000000 (6626.9495031022) | 0.381 | |
| LV.occipital.horn (mean (SD)) | 1175.2857142857 (1256.6319104025) | 1022.3125000000 (779.0714745773) | 0.688 | |
| LV.temporal.horn (mean (SD)) | 685.4285714286 (400.5451504917) | 1013.3750000000 (740.7818279809) | 0.151 | |
| Third.ventricle (mean (SD)) | 847.3571428571 (371.9043729742) | 1385.5625000000 (998.3400201501) | 0.068 | |
| Fourth.ventricle.total (mean (SD)) | 1550.7142857143 (294.6558432037) | 1681.3750000000 (382.6409761992) | 0.309 | |
| Apex (mean (SD)) | 153.0000000000 (44.1047703682) | 171.4375000000 (46.5130358072) | 0.277 | |
| Lateral.recess (mean (SD)) | 195.2857142857 (45.7391072723) | 201.6250000000 (48.0220435495) | 0.715 | |
| Obex (mean (SD)) | 179.5714285714 (109.0622364917) | 169.8750000000 (113.0698161904) | 0.813 | |
| Fastigium (mean (SD)) | 194.2142857143 (54.4880771312) | 203.8125000000 (54.6835974798) | 0.635 |
kable(Table.Ventricles.absolute.stratified.gender.RSD.female)
| Table.Ventricles.absolute.stratified.gender.RSD.femaleX2/Table.Ventricles.absolute.stratified.gender.RSD.femaleX1 |
|---|
| 81.8 |
| 92.7 |
| 94.4 |
| 113.1 |
| 82.6 |
| 106.9 |
| 58.4 |
| 43.9 |
| 19.0 |
| 28.8 |
| 23.4 |
| 60.7 |
| 28.1 |
kable(Table.Ventricles.absolute.stratified.gender.RSD.male)
| Table.Ventricles.absolute.stratified.gender.RSD.maleX2/Table.Ventricles.absolute.stratified.gender.RSD.maleX1 |
|---|
| 78.4 |
| 84.5 |
| 77.3 |
| 88.8 |
| 104.6 |
| 76.2 |
| 73.1 |
| 72.1 |
| 22.8 |
| 27.1 |
| 23.8 |
| 66.6 |
| 26.8 |
NA
Ventricles.absolute <- select(Ventricles.absolute, - c(Gender))
Ventricles.absolute.red <- select(Ventricles.absolute, - c(Total.volume.ventricles))
names.anatomical.structures.temporary <- c("LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium")
names.anatomical.structures.definitive <- c("LV- Total",
"LV - Frontal horn",
"LV - Body",
"LV - Atrium",
"LV - Occipital horn",
"LV - Temporal horn",
"Third ventricle",
"Fourth ventricle - Total",
"Apex",
"Lateral recess",
"Obex",
"Fastigium")
Ventricles.absolute.plotdata <- gather(Ventricles.absolute.red, "anatomical.structure", "relative.volume")
Ventricles.absolute.plotdata$Gender <- All.Volumes$Gender
Ventricles.absolute.plotdata$Age <- All.Volumes$`Age (years)`
Ventricles.absolute.plotdata$Gender <- factor(Ventricles.absolute.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Ventricles.absolute.plotdata$anatomical.structure <- factor(Ventricles.absolute.plotdata$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Ventricles.absolute.plot <- ggplot(Ventricles.absolute.plotdata, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Absolute volume (in mm3)") +
theme_minimal() +
coord_flip() +
ggtitle("VENTRICULAR SYSTEM") +
theme(plot.title = element_text(hjust = 0.5))
Ventricles.absolute.plot
ggsave("Ventricles.absolute.plot.pdf", plot = Ventricles.absolute.plot, width = 12, height = 5, units = "in", dpi = 600)
Total.ventricles.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume ventricles`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 82000, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME total ventricular system") +
theme(plot.title = element_text(hjust = 0.5))
Total.ventricles.Age.plot
ggsave("Total.Total.ventricles.Age.plot.pdf", plot = Total.ventricles.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Lateral.ventricle.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume lateral ventricles`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 72000, label.x = 65, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME lateral ventricles") +
theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.Age.plot
ggsave("Total.Lateral.ventricle.Age.plot.pdf", plot = Lateral.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Lateral.ventricle.frontal.horn.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume frontal horn`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 21000, label.x = 65, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME frontal horn") +
theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.frontal.horn.Age.plot
ggsave("Total.Lateral.ventricle.frontal.horn.Age.plot.pdf", plot = Lateral.ventricle.frontal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Lateral.ventricle.body.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume body of LV`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 19000, label.x = 65, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME body") +
theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.body.Age.plot
ggsave("Total.Lateral.ventricle.body.Age.plot.pdf", plot = Lateral.ventricle.body.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Lateral.ventricle.atrium.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume atrium`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 24000, label.x = 65, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME atrium") +
theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.atrium.Age.plot
ggsave("Total.Lateral.ventricle.atrium.Age.plot.pdf", plot = Lateral.ventricle.atrium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Lateral.ventricle.occipital.horn.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume occipital horn`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4100, label.x = 65, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME occipital horn") +
theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.occipital.horn.Age.plot
ggsave("Total.Lateral.ventricle.occipital.horn.Age.plot.pdf", plot = Lateral.ventricle.occipital.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Lateral.ventricle.temporal.horn.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume temporal horn`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 3100, label.x = 65, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME temporal horn") +
theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.temporal.horn.Age.plot
ggsave("Total.Lateral.ventricle.temporal.horn.Age.plot.pdf", plot = Lateral.ventricle.temporal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Third.ventricle.Age.plot <- ggplot(All.Volumes, aes(y =`3rd ventricle`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 4250, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME third ventricle") +
theme(plot.title = element_text(hjust = 0.5))
Third.ventricle.Age.plot
ggsave("Total.Third.ventricle.Age.plot.pdf", plot = Third.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Fourth.ventricle.Age.plot <- ggplot(All.Volumes, aes(y =`4th ventricle`, x = `Age (years)`)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2700, label.x = 70, color = "dodgerblue4") +
ylab("Volume in mm3") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("ABSOLUTE VOLUME fourth ventricle") +
theme(plot.title = element_text(hjust = 0.5))
Fourth.ventricle.Age.plot
ggsave("Total.Fourth.ventricle.Age.plot.pdf", plot = Fourth.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative Volumes 1: Normalized to Total Encephalic Volume
#Ventricles.absolute <- select(Ventricles.absolute, - c(Gender))
Total.ventricular.volume.relative <- (100 * (Ventricles.absolute$Total.volume.ventricles/All.Volumes$`Total encephalic volume (without ventricles)`))
Ventricles.relative <- (100 * (Ventricles.absolute[, -1]/All.Volumes$`Total encephalic volume (without ventricles)`))
Table.Ventricles.relative <- cbind(Total.ventricular.volume.relative, Ventricles.relative)
Table.Ventricles.relative$Gender <- All.Volumes$Gender
Table.Ventricles.relative1 <- CreateTableOne(
vars = c("Total.ventricular.volume.relative",
"LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
data = Table.Ventricles.relative)
Table.Ventricles.relative.stratified.gender <- CreateTableOne(
vars = c("Total.ventricular.volume.relative",
"LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
strata = c("Gender"),
data = Table.Ventricles.relative)
Table.Ventricles.relative1 <- print(Table.Ventricles.relative1, contDigits = 10)
Overall
n 30
Total.ventricular.volume.relative (mean (SD)) 1.9273127232 (1.5160752528)
LV.total (mean (SD)) 1.6743917636 (1.4458630666)
LV.frontal.horn (mean (SD)) 0.5544339978 (0.4457583254)
LV.body (mean (SD)) 0.4411089345 (0.4277559363)
LV.atrium (mean (SD)) 0.4999720439 (0.5129573777)
LV.occipital.horn (mean (SD)) 0.0992362568 (0.0919251551)
LV.temporal.horn (mean (SD)) 0.0796461516 (0.0609277346)
Third.ventricle (mean (SD)) 0.1036226244 (0.0756082790)
Fourth.ventricle.total (mean (SD)) 0.1493055163 (0.0331934574)
Apex (mean (SD)) 0.0149830331 (0.0042490310)
Lateral.recess (mean (SD)) 0.0182578178 (0.0041320330)
Obex (mean (SD)) 0.0159650951 (0.0097282418)
Fastigium (mean (SD)) 0.0183133305 (0.0048392615)Table.Ventricles.relative.stratified.gender <- print(Table.Ventricles.relative.stratified.gender, contDigits = 10) Stratified by Gender
f m p test
n 14 16
Total.ventricular.volume.relative (mean (SD)) 1.8554635462 (1.3268579426) 1.9901807530 (1.7055469213) 0.813
LV.total (mean (SD)) 1.6208567323 (1.3239504826) 1.7212349160 (1.5866708182) 0.853
LV.frontal.horn (mean (SD)) 0.5647158943 (0.4689867048) 0.5454373384 (0.4397126405) 0.908
LV.body (mean (SD)) 0.4491770751 (0.4459306767) 0.4340493115 (0.4257907279) 0.925
LV.atrium (mean (SD)) 0.4293273395 (0.3142686102) 0.5617861603 (0.6437228077) 0.490
LV.occipital.horn (mean (SD)) 0.1112826435 (0.1145831457) 0.0886956685 (0.0685889424) 0.512
LV.temporal.horn (mean (SD)) 0.0663469710 (0.0375726047) 0.0912829346 (0.0751259289) 0.271
Third.ventricle (mean (SD)) 0.0826068089 (0.0357482716) 0.1220114630 (0.0957687648) 0.158
Fourth.ventricle.total (mean (SD)) 0.1520088916 (0.0289166876) 0.1469400629 (0.0373186717) 0.684
Apex (mean (SD)) 0.0150095263 (0.0043100506) 0.0149598516 (0.0043363600) 0.975
Lateral.recess (mean (SD)) 0.0190608542 (0.0040699140) 0.0175551609 (0.0041862858) 0.328
Obex (mean (SD)) 0.0174969331 (0.0102282698) 0.0146247369 (0.0093911238) 0.429
Fastigium (mean (SD)) 0.0189991246 (0.0050327628) 0.0177132608 (0.0047435307) 0.477
Table.Ventricles.relative1.RSD <- as.data.frame(Table.Ventricles.relative1)
Table.Ventricles.relative1.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.relative1.RSD[-1,]),
' (',fixed=TRUE)))
Table.Ventricles.relative1.RSD <- data.frame(cbind(str_replace_all(Table.Ventricles.relative1.RSD$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.relative1.RSD$X2, "[)]", "")))
Table.Ventricles.relative1.RSD$X1 <- as.character(Table.Ventricles.relative1.RSD$X1)
Table.Ventricles.relative1.RSD$X2 <- as.character(Table.Ventricles.relative1.RSD$X2)
Table.Ventricles.relative1.RSD <- as.data.frame(sapply(Table.Ventricles.relative1.RSD, as.numeric))
Table.Ventricles.relative1.RSD <- as.data.frame(Table.Ventricles.relative1.RSD$X2/Table.Ventricles.relative1.RSD$X1)
Table.Ventricles.relative1.RSD <- round(Table.Ventricles.relative1.RSD * 100, 1)
Table.Ventricles.relative.stratified.gender.RSD <- as.data.frame(Table.Ventricles.relative.stratified.gender)
Table.Ventricles.relative.stratified.gender.RSD <- select(Table.Ventricles.relative.stratified.gender.RSD, - c(p, test))
Table.Ventricles.relative.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.relative.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.Ventricles.relative.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.relative.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Ventricles.relative.stratified.gender.RSD.female$X1 <- as.character(Table.Ventricles.relative.stratified.gender.RSD.female$X1)
Table.Ventricles.relative.stratified.gender.RSD.female$X2 <- as.character(Table.Ventricles.relative.stratified.gender.RSD.female$X2)
Table.Ventricles.relative.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Ventricles.relative.stratified.gender.RSD.female, as.numeric))
Table.Ventricles.relative.stratified.gender.RSD.female <- as.data.frame(Table.Ventricles.relative.stratified.gender.RSD.female$X2/Table.Ventricles.relative.stratified.gender.RSD.female$X1)
Table.Ventricles.relative.stratified.gender.RSD.female <- round(Table.Ventricles.relative.stratified.gender.RSD.female * 100, 1)
Table.Ventricles.relative.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.relative.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.Ventricles.relative.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.relative.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Ventricles.relative.stratified.gender.RSD.male$X1 <- as.character(Table.Ventricles.relative.stratified.gender.RSD.male$X1)
Table.Ventricles.relative.stratified.gender.RSD.male$X2 <- as.character(Table.Ventricles.relative.stratified.gender.RSD.male$X2)
Table.Ventricles.relative.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Ventricles.relative.stratified.gender.RSD.male, as.numeric))
Table.Ventricles.relative.stratified.gender.RSD.male <- as.data.frame(Table.Ventricles.relative.stratified.gender.RSD.male$X2/Table.Ventricles.relative.stratified.gender.RSD.male$X1)
Table.Ventricles.relative.stratified.gender.RSD.male <- round(Table.Ventricles.relative.stratified.gender.RSD.male * 100, 1)
kable(Table.Ventricles.relative1)
| Overall | |
|---|---|
| n | 30 |
| Total.ventricular.volume.relative (mean (SD)) | 1.9273127232 (1.5160752528) |
| LV.total (mean (SD)) | 1.6743917636 (1.4458630666) |
| LV.frontal.horn (mean (SD)) | 0.5544339978 (0.4457583254) |
| LV.body (mean (SD)) | 0.4411089345 (0.4277559363) |
| LV.atrium (mean (SD)) | 0.4999720439 (0.5129573777) |
| LV.occipital.horn (mean (SD)) | 0.0992362568 (0.0919251551) |
| LV.temporal.horn (mean (SD)) | 0.0796461516 (0.0609277346) |
| Third.ventricle (mean (SD)) | 0.1036226244 (0.0756082790) |
| Fourth.ventricle.total (mean (SD)) | 0.1493055163 (0.0331934574) |
| Apex (mean (SD)) | 0.0149830331 (0.0042490310) |
| Lateral.recess (mean (SD)) | 0.0182578178 (0.0041320330) |
| Obex (mean (SD)) | 0.0159650951 (0.0097282418) |
| Fastigium (mean (SD)) | 0.0183133305 (0.0048392615) |
kable(Table.Ventricles.relative1.RSD)
| Table.Ventricles.relative1.RSDX2/Table.Ventricles.relative1.RSDX1 |
|---|
| 78.7 |
| 86.4 |
| 80.4 |
| 97.0 |
| 102.6 |
| 92.6 |
| 76.5 |
| 73.0 |
| 22.2 |
| 28.4 |
| 22.6 |
| 60.9 |
| 26.4 |
kable(Table.Ventricles.relative.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| Total.ventricular.volume.relative (mean (SD)) | 1.8554635462 (1.3268579426) | 1.9901807530 (1.7055469213) | 0.813 | |
| LV.total (mean (SD)) | 1.6208567323 (1.3239504826) | 1.7212349160 (1.5866708182) | 0.853 | |
| LV.frontal.horn (mean (SD)) | 0.5647158943 (0.4689867048) | 0.5454373384 (0.4397126405) | 0.908 | |
| LV.body (mean (SD)) | 0.4491770751 (0.4459306767) | 0.4340493115 (0.4257907279) | 0.925 | |
| LV.atrium (mean (SD)) | 0.4293273395 (0.3142686102) | 0.5617861603 (0.6437228077) | 0.490 | |
| LV.occipital.horn (mean (SD)) | 0.1112826435 (0.1145831457) | 0.0886956685 (0.0685889424) | 0.512 | |
| LV.temporal.horn (mean (SD)) | 0.0663469710 (0.0375726047) | 0.0912829346 (0.0751259289) | 0.271 | |
| Third.ventricle (mean (SD)) | 0.0826068089 (0.0357482716) | 0.1220114630 (0.0957687648) | 0.158 | |
| Fourth.ventricle.total (mean (SD)) | 0.1520088916 (0.0289166876) | 0.1469400629 (0.0373186717) | 0.684 | |
| Apex (mean (SD)) | 0.0150095263 (0.0043100506) | 0.0149598516 (0.0043363600) | 0.975 | |
| Lateral.recess (mean (SD)) | 0.0190608542 (0.0040699140) | 0.0175551609 (0.0041862858) | 0.328 | |
| Obex (mean (SD)) | 0.0174969331 (0.0102282698) | 0.0146247369 (0.0093911238) | 0.429 | |
| Fastigium (mean (SD)) | 0.0189991246 (0.0050327628) | 0.0177132608 (0.0047435307) | 0.477 |
kable(Table.Ventricles.relative.stratified.gender.RSD.female)
| Table.Ventricles.relative.stratified.gender.RSD.femaleX2/Table.Ventricles.relative.stratified.gender.RSD.femaleX1 |
|---|
| 71.5 |
| 81.7 |
| 83.0 |
| 99.3 |
| 73.2 |
| 103.0 |
| 56.6 |
| 43.3 |
| 19.0 |
| 28.7 |
| 21.4 |
| 58.5 |
| 26.5 |
kable(Table.Ventricles.relative.stratified.gender.RSD.male)
| Table.Ventricles.relative.stratified.gender.RSD.maleX2/Table.Ventricles.relative.stratified.gender.RSD.maleX1 |
|---|
| 85.7 |
| 92.2 |
| 80.6 |
| 98.1 |
| 114.6 |
| 77.3 |
| 82.3 |
| 78.5 |
| 25.4 |
| 29.0 |
| 23.8 |
| 64.2 |
| 26.8 |
NA
names.anatomical.structures.temporary <- c("LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium")
names.anatomical.structures.definitive <- c("LV- Total",
"LV - Frontal horn",
"LV - Body",
"LV - Atrium",
"LV - Occipital horn",
"LV - Temporal horn",
"Third ventricle",
"Fourth ventricle - Total",
"Apex",
"Lateral recess",
"Obex",
"Fastigium")
Ventricles.relative.plotdata <- gather(Ventricles.relative, "anatomical.structure", "relative.volume")
Ventricles.relative.plotdata$Gender <- All.Volumes$Gender
Ventricles.relative.plotdata$Age <- All.Volumes$`Age (years)`
Ventricles.relative.plotdata$Gender <- factor(Ventricles.relative.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Ventricles.relative.plotdata$anatomical.structure <- factor(Ventricles.relative.plotdata$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Ventricles.relative1.plot <- ggplot(Ventricles.relative.plotdata, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Relative volume (in %)") +
theme_minimal() +
coord_flip() +
ggtitle("VENTRICULAR SYSTEM") +
theme(plot.title = element_text(hjust = 0.5))
Ventricles.relative1.plot
ggsave("Ventricles.relative1.plot.pdf", plot = Ventricles.relative1.plot, width = 12, height = 5, units = "in", dpi = 600)
Ventricles.relative$Gender <- All.Volumes$Gender
Ventricles.relative$Age <- All.Volumes$`Age (years)`
Ventricles.relative$Total.ventricular <- Total.ventricular.volume.relative
Relative.Total.ventricular.Age.plot <- ggplot(Ventricles.relative, aes(y=Total.ventricular, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 8.2, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME total ventricular system (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Total.ventricular.Age.plot
ggsave("Relative.Total.ventricular.Age.plot.pdf", plot = Relative.Total.ventricular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.LV.total.Age.plot <- ggplot(Ventricles.relative, aes(y=LV.total, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 7.2, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME lateral ventricles (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.LV.total.Age.plot
ggsave("Relative.LV.total.Age.plot.pdf", plot = Relative.LV.total.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.LV.frontal.horn.Age.plot <- ggplot(Ventricles.relative, aes(y=LV.frontal.horn, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.9, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME frontal horn (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.LV.frontal.horn.Age.plot
ggsave("Relative.LV.frontal.horn.Age.plot.pdf", plot = Relative.LV.frontal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.LV.body.Age.plot <- ggplot(Ventricles.relative, aes(y=LV.body, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 1.9, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME body (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.LV.body.Age.plot
ggsave("Relative.LV.body.Age.plot.pdf", plot = Relative.LV.body.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.LV.atrium.Age.plot <- ggplot(Ventricles.relative, aes(y=LV.atrium, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 2.4, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME atrium (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.LV.atrium.Age.plot
ggsave("Relative.LV.atrium.Age.plot.pdf", plot = Relative.LV.atrium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.LV.occipital.horn.Age.plot <- ggplot(Ventricles.relative, aes(y=LV.occipital.horn, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.37, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME occipital horn (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.LV.occipital.horn.Age.plot
ggsave("Relative.LV.occipital.horn.Age.plot.pdf", plot = Relative.LV.occipital.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.LV.temporal.horn.Age.plot <- ggplot(Ventricles.relative, aes(y=LV.temporal.horn, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.28, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME temporal horn (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.LV.temporal.horn.Age.plot
ggsave("Relative.LV.temporal.horn.Age.plot.pdf", plot = Relative.LV.temporal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Third.ventricle.Age.plot <- ggplot(Ventricles.relative, aes(y=Third.ventricle, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.47, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME third ventricle (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Third.ventricle.Age.plot
ggsave("Relative.Third.ventricle.Age.plot.pdf", plot = Relative.Third.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative.Fourth.ventricle.total.Age.plot <- ggplot(Ventricles.relative, aes(y=Fourth.ventricle.total, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 0.26, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME fourth ventricle (E)") +
theme(plot.title = element_text(hjust = 0.5))
Relative.Fourth.ventricle.total.Age.plot
ggsave("Relative.Fourth.ventricle.total.Age.plot.pdf", plot = Relative.Fourth.ventricle.total.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
Relative Volumes 2: Normalized to Total Ventricular Volume
#Ventricles.absolute <- select(Ventricles.absolute, - c(Gender))
#Total.ventricular.volume.relative <- (100 * (Ventricles.absolute$Total.volume.ventricles/All.Volumes$`Total encephalic volume (without ventricles)`))
Ventricles.relative.V <- (100 * (Ventricles.absolute[, -1]/Total.volume.ventricles))
Table.Ventricles.relative.V <- Ventricles.relative.V
Table.Ventricles.relative.V$Gender <- All.Volumes$Gender
Table.Ventricles.relative.V.1 <- CreateTableOne(
vars = c("LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
data = Table.Ventricles.relative.V)
Table.Ventricles.relative.V.stratified.gender <- CreateTableOne(
vars = c("LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
strata = c("Gender"),
data = Table.Ventricles.relative.V)
Table.Ventricles.relative.V.1 <- print(Table.Ventricles.relative.V.1, contDigits = 10)
Overall
n 30
LV.total (mean (SD)) 83.2019241187 (6.8352468610)
LV.frontal.horn (mean (SD)) 28.1033071294 (4.6967677272)
LV.body (mean (SD)) 21.1173441521 (4.9285078989)
LV.atrium (mean (SD)) 24.1985317285 (5.9615108255)
LV.occipital.horn (mean (SD)) 4.9538082192 (2.6076054839)
LV.temporal.horn (mean (SD)) 4.8294159930 (2.7406173762)
Third.ventricle (mean (SD)) 6.0195881402 (1.7866435917)
Fourth.ventricle.total (mean (SD)) 10.7788178347 (5.5922504933)
Apex (mean (SD)) 1.1099936692 (0.6736993658)
Lateral.recess (mean (SD)) 1.3060807781 (0.6557212781)
Obex (mean (SD)) 1.1389316903 (0.8450359678)
Fastigium (mean (SD)) 1.3124771989 (0.6617054188)Table.Ventricles.relative.V.stratified.gender <- print(Table.Ventricles.relative.V.stratified.gender, contDigits = 10) Stratified by Gender
f m p test
n 14 16
LV.total (mean (SD)) 82.9014835540 (8.2210783349) 83.4648096127 (5.6208437624) 0.826
LV.frontal.horn (mean (SD)) 28.7486156433 (4.3258459503) 27.5386621798 (5.0697128006) 0.491
LV.body (mean (SD)) 21.5355720813 (6.0195063390) 20.7513947142 (3.9053473560) 0.671
LV.atrium (mean (SD)) 22.9934302143 (4.6642173365) 25.2529955534 (6.8785301050) 0.309
LV.occipital.horn (mean (SD)) 5.3475006354 (2.7639521142) 4.6093273550 (2.5007651169) 0.449
LV.temporal.horn (mean (SD)) 4.2757800011 (1.9524248970) 5.3138474860 (3.2682007737) 0.309
Third.ventricle (mean (SD)) 5.4244625196 (1.9402082378) 6.5403230583 (1.5129745442) 0.088
Fourth.ventricle.total (mean (SD)) 11.6741098633 (6.5598775626) 9.9954373097 (4.6652485061) 0.422
Apex (mean (SD)) 1.1864994023 (0.7826748904) 1.0430511527 (0.5799465405) 0.570
Lateral.recess (mean (SD)) 1.4379087032 (0.7630568986) 1.1907313436 (0.5442810149) 0.311
Obex (mean (SD)) 1.3338238640 (0.9841474404) 0.9684010383 (0.6889767649) 0.244
Fastigium (mean (SD)) 1.4478393571 (0.7744449436) 1.1940353105 (0.5428226641) 0.303
Table.Ventricles.relative.V.1.RSD <- as.data.frame(Table.Ventricles.relative.V.1)
Table.Ventricles.relative.V.1.RSD <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.relative.V.1.RSD[-1,]),
' (',fixed=TRUE)))
Table.Ventricles.relative.V.1.RSD <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.V.1.RSD$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.relative.V.1.RSD$X2, "[)]", "")))
Table.Ventricles.relative.V.1.RSD$X1 <- as.character(Table.Ventricles.relative.V.1.RSD$X1)
Table.Ventricles.relative.V.1.RSD$X2 <- as.character(Table.Ventricles.relative.V.1.RSD$X2)
Table.Ventricles.relative.V.1.RSD <- as.data.frame(sapply(Table.Ventricles.relative.V.1.RSD, as.numeric))
Table.Ventricles.relative.V.1.RSD <- as.data.frame(Table.Ventricles.relative.V.1.RSD$X2/Table.Ventricles.relative.V.1.RSD$X1)
Table.Ventricles.relative.V.1.RSD <- round(Table.Ventricles.relative.V.1.RSD * 100, 1)
Table.Ventricles.relative.V.stratified.gender.RSD <- as.data.frame(Table.Ventricles.relative.V.stratified.gender)
Table.Ventricles.relative.V.stratified.gender.RSD <- select(Table.Ventricles.relative.V.stratified.gender.RSD, - c(p, test))
Table.Ventricles.relative.V.stratified.gender.RSD.female <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.relative.V.stratified.gender.RSD[-1, "f"]),
' (',fixed=TRUE)))
Table.Ventricles.relative.V.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.V.stratified.gender.RSD.female$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.relative.V.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Ventricles.relative.V.stratified.gender.RSD.female$X1 <- as.character(Table.Ventricles.relative.V.stratified.gender.RSD.female$X1)
Table.Ventricles.relative.V.stratified.gender.RSD.female$X2 <- as.character(Table.Ventricles.relative.V.stratified.gender.RSD.female$X2)
Table.Ventricles.relative.V.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Ventricles.relative.V.stratified.gender.RSD.female, as.numeric))
Table.Ventricles.relative.V.stratified.gender.RSD.female <- as.data.frame(Table.Ventricles.relative.V.stratified.gender.RSD.female$X2/Table.Ventricles.relative.V.stratified.gender.RSD.female$X1)
Table.Ventricles.relative.V.stratified.gender.RSD.female <- round(Table.Ventricles.relative.V.stratified.gender.RSD.female * 100, 1)
Table.Ventricles.relative.V.stratified.gender.RSD.male <- data.frame(do.call('rbind',
strsplit(as.character(Table.Ventricles.relative.V.stratified.gender.RSD[-1, "m"]),
' (',fixed=TRUE)))
Table.Ventricles.relative.V.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.V.stratified.gender.RSD.male$X1, "[ ]", ""),
str_replace_all(Table.Ventricles.relative.V.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Ventricles.relative.V.stratified.gender.RSD.male$X1 <- as.character(Table.Ventricles.relative.V.stratified.gender.RSD.male$X1)
Table.Ventricles.relative.V.stratified.gender.RSD.male$X2 <- as.character(Table.Ventricles.relative.V.stratified.gender.RSD.male$X2)
Table.Ventricles.relative.V.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Ventricles.relative.V.stratified.gender.RSD.male, as.numeric))
Table.Ventricles.relative.V.stratified.gender.RSD.male <- as.data.frame(Table.Ventricles.relative.V.stratified.gender.RSD.male$X2/Table.Ventricles.relative.V.stratified.gender.RSD.male$X1)
Table.Ventricles.relative.V.stratified.gender.RSD.male <- round(Table.Ventricles.relative.V.stratified.gender.RSD.male * 100, 1)
kable(Table.Ventricles.relative.V.1)
| Overall | |
|---|---|
| n | 30 |
| LV.total (mean (SD)) | 83.2019241187 (6.8352468610) |
| LV.frontal.horn (mean (SD)) | 28.1033071294 (4.6967677272) |
| LV.body (mean (SD)) | 21.1173441521 (4.9285078989) |
| LV.atrium (mean (SD)) | 24.1985317285 (5.9615108255) |
| LV.occipital.horn (mean (SD)) | 4.9538082192 (2.6076054839) |
| LV.temporal.horn (mean (SD)) | 4.8294159930 (2.7406173762) |
| Third.ventricle (mean (SD)) | 6.0195881402 (1.7866435917) |
| Fourth.ventricle.total (mean (SD)) | 10.7788178347 (5.5922504933) |
| Apex (mean (SD)) | 1.1099936692 (0.6736993658) |
| Lateral.recess (mean (SD)) | 1.3060807781 (0.6557212781) |
| Obex (mean (SD)) | 1.1389316903 (0.8450359678) |
| Fastigium (mean (SD)) | 1.3124771989 (0.6617054188) |
kable(Table.Ventricles.relative.V.1.RSD)
| Table.Ventricles.relative.V.1.RSDX2/Table.Ventricles.relative.V.1.RSDX1 |
|---|
| 8.2 |
| 16.7 |
| 23.3 |
| 24.6 |
| 52.6 |
| 56.7 |
| 29.7 |
| 51.9 |
| 60.7 |
| 50.2 |
| 74.2 |
| 50.4 |
kable(Table.Ventricles.relative.V.stratified.gender)
| f | m | p | test | |
|---|---|---|---|---|
| n | 14 | 16 | ||
| LV.total (mean (SD)) | 82.9014835540 (8.2210783349) | 83.4648096127 (5.6208437624) | 0.826 | |
| LV.frontal.horn (mean (SD)) | 28.7486156433 (4.3258459503) | 27.5386621798 (5.0697128006) | 0.491 | |
| LV.body (mean (SD)) | 21.5355720813 (6.0195063390) | 20.7513947142 (3.9053473560) | 0.671 | |
| LV.atrium (mean (SD)) | 22.9934302143 (4.6642173365) | 25.2529955534 (6.8785301050) | 0.309 | |
| LV.occipital.horn (mean (SD)) | 5.3475006354 (2.7639521142) | 4.6093273550 (2.5007651169) | 0.449 | |
| LV.temporal.horn (mean (SD)) | 4.2757800011 (1.9524248970) | 5.3138474860 (3.2682007737) | 0.309 | |
| Third.ventricle (mean (SD)) | 5.4244625196 (1.9402082378) | 6.5403230583 (1.5129745442) | 0.088 | |
| Fourth.ventricle.total (mean (SD)) | 11.6741098633 (6.5598775626) | 9.9954373097 (4.6652485061) | 0.422 | |
| Apex (mean (SD)) | 1.1864994023 (0.7826748904) | 1.0430511527 (0.5799465405) | 0.570 | |
| Lateral.recess (mean (SD)) | 1.4379087032 (0.7630568986) | 1.1907313436 (0.5442810149) | 0.311 | |
| Obex (mean (SD)) | 1.3338238640 (0.9841474404) | 0.9684010383 (0.6889767649) | 0.244 | |
| Fastigium (mean (SD)) | 1.4478393571 (0.7744449436) | 1.1940353105 (0.5428226641) | 0.303 |
kable(Table.Ventricles.relative.V.stratified.gender.RSD.female)
| Table.Ventricles.relative.V.stratified.gender.RSD.femaleX2/Table.Ventricles.relative.V.stratified.gender.RSD.femaleX1 |
|---|
| 9.9 |
| 15.0 |
| 28.0 |
| 20.3 |
| 51.7 |
| 45.7 |
| 35.8 |
| 56.2 |
| 66.0 |
| 53.1 |
| 73.8 |
| 53.5 |
kable(Table.Ventricles.relative.V.stratified.gender.RSD.male)
| Table.Ventricles.relative.V.stratified.gender.RSD.maleX2/Table.Ventricles.relative.V.stratified.gender.RSD.maleX1 |
|---|
| 6.7 |
| 18.4 |
| 18.8 |
| 27.2 |
| 54.3 |
| 61.5 |
| 23.1 |
| 46.7 |
| 55.6 |
| 45.7 |
| 71.1 |
| 45.5 |
NA
names.anatomical.structures.temporary <- c("LV.total",
"LV.frontal.horn",
"LV.body",
"LV.atrium",
"LV.occipital.horn",
"LV.temporal.horn",
"Third.ventricle",
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium")
names.anatomical.structures.definitive <- c("LV- Total",
"LV - Frontal horn",
"LV - Body",
"LV - Atrium",
"LV - Occipital horn",
"LV - Temporal horn",
"Third ventricle",
"Fourth ventricle - Total",
"Apex",
"Lateral recess",
"Obex",
"Fastigium")
Ventricles.relative.V.plotdata <- gather(Ventricles.relative.V, "anatomical.structure", "relative.volume")
Ventricles.relative.V.plotdata$Gender <- All.Volumes$Gender
Ventricles.relative.V.plotdata$Age <- All.Volumes$`Age (years)`
Ventricles.relative.V.plotdata$Gender <- factor(Ventricles.relative.V.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Ventricles.relative.V.plotdata$anatomical.structure <- factor(Ventricles.relative.V.plotdata$anatomical.structure,
levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))
Ventricles.relative2.plot <- ggplot(Ventricles.relative.V.plotdata, aes(x=anatomical.structure, y = relative.volume)) +
stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") +
geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6),
outlier.shape = NA, color = "gray30") +
scale_fill_manual(values = c("chartreuse4", "orangered2")) +
geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
scale_color_continuous(low = "steelblue1", high = "red4") +
xlab("") + ylab("Relative volume (in %)") +
theme_minimal() +
coord_flip() +
ggtitle("VENTRICULAR SYSTEM") +
theme(plot.title = element_text(hjust = 0.5))
Ventricles.relative2.plot
ggsave("Ventricles.relative2.plot.pdf", plot = Ventricles.relative2.plot, width = 12, height = 5, units = "in", dpi = 600)
Ventricles.relative.V$Gender <- All.Volumes$Gender
Ventricles.relative.V$Age <- All.Volumes$`Age (years)`
V.Relative.LV.total.Age.plot <- ggplot(Ventricles.relative.V, aes(y=LV.total, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 102, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME lateral ventricles (V)") +
theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.total.Age.plot
ggsave("V.Relative.LV.total.Age.plot.pdf", plot = V.Relative.LV.total.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
V.Relative.LV.frontal.horn.Age.plot <- ggplot(Ventricles.relative.V, aes(y=LV.frontal.horn, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 39, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME frontal horn (V)") +
theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.frontal.horn.Age.plot
ggsave("V.Relative.LV.frontal.horn.Age.plot.pdf", plot = V.Relative.LV.frontal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
V.Relative.LV.body.Age.plot <- ggplot(Ventricles.relative.V, aes(y=LV.body, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 33, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME body (V)") +
theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.body.Age.plot
ggsave("V.Relative.LV.body.Age.plot.pdf", plot = V.Relative.LV.body.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
V.Relative.LV.atrium.Age.plot <- ggplot(Ventricles.relative.V, aes(y=LV.atrium, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 43, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME atrium (V)") +
theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.atrium.Age.plot
ggsave("V.Relative.LV.atrium.Age.plot.pdf", plot = V.Relative.LV.atrium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
V.Relative.LV.occipital.horn.Age.plot <- ggplot(Ventricles.relative.V, aes(y=LV.occipital.horn, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 11, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME occipital horn (V)") +
theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.occipital.horn.Age.plot
ggsave("V.Relative.LV.occipital.horn.Age.plot.pdf", plot = V.Relative.LV.occipital.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
V.Relative.LV.temporal.horn.Age.plot <- ggplot(Ventricles.relative.V, aes(y=LV.temporal.horn, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 16, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME temporal horn (V)") +
theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.temporal.horn.Age.plot
ggsave("V.Relative.LV.temporal.horn.Age.plot.pdf", plot = V.Relative.LV.temporal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
V.Relative.Third.ventricle.Age.plot <- ggplot(Ventricles.relative.V, aes(y=Third.ventricle, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 9.2, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME third ventricle (E)") +
theme(plot.title = element_text(hjust = 0.5))
V.Relative.Third.ventricle.Age.plot
ggsave("V.Relative.Third.ventricle.Age.plot.pdf", plot = V.Relative.Third.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
V.Relative.Fourth.ventricle.total.Age.plot <- ggplot(Ventricles.relative.V, aes(y=Fourth.ventricle.total, x = Age)) +
geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
scale_color_manual(values = c("chartreuse4", "orangered2")) +
geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
stat_cor(method = "pearson", label.y = 17, label.x = 70, color = "dodgerblue4") +
ylab("Relative Volume (in %)") + xlab("Age (in years)") +
theme_minimal() +
ggtitle("RELATIVE VOLUME fourth ventricle (E)") +
theme(plot.title = element_text(hjust = 0.5))
V.Relative.Fourth.ventricle.total.Age.plot
ggsave("V.Relative.Fourth.ventricle.total.Age.plot.pdf", plot = V.Relative.Fourth.ventricle.total.Age.plot, width = 8, height = 6, units = "in", dpi = 600)
---
title: "Statistical code"
subtitle: "Topographic volume-standardization atlas of the human brain"
author: "Akeret et al. 2020"
output: html_notebook
---

# Setup

```{r Setup, message = FALSE, warning = FALSE, error = FALSE}

knitr::opts_chunk$set(message = FALSE, warning = FALSE, error = FALSE)

library(readxl)
library(biostatUZH)
library(dplyr)
library(tableone)
library(knitr)
library(captioner)
library(beeswarm)
library(readr)
library(ggplot2)
library(reshape2)
library(tidyr)
library(ggpubr)
library(graphics)
library(ggbeeswarm)
library(jcolors)
library(pals)
library(ggsci)
library(Hmisc)
library(stringr)

kableone <- function(x, ...) {
  capture.output(x <- print(x))
  knitr::kable(x, ...)
}

```

```{r Load and format data}

All.Volumes <- read_csv("Encephalic structures_volumes.csv")
All.Volumes$ID <- as.factor(All.Volumes$ID)
All.Volumes$Gender <- as.factor(All.Volumes$Gender)
All.Volumes$Handedness <- as.factor(All.Volumes$Handedness)

```

# Baseline Data

```{r Basic Data Summary}

Baseline.data <- CreateTableOne(vars = c("Age (years)", "Handedness"), 
               data = All.Volumes, strat = c("Gender"))
Baseline.data <- print(Baseline.data)

```

```{r Table 1}

kable(Baseline.data)

```

# Total encephalic volume (w/o ventricles)

```{r Total Encephalic Volume}

summary(All.Volumes$`Total encephalic volume (without ventricles)`)
Total.encephalic.volume <- CreateTableOne(vars = c("Total encephalic volume (without ventricles)"), 
                                                  data = All.Volumes)
Total.encephalic.volume.stratified.gender <- CreateTableOne(vars = c("Total encephalic volume (without ventricles)"), 
                                                   strata = c("Gender"), data = All.Volumes)
Total.encephalic.volume <- print(Total.encephalic.volume)
Total.encephalic.volume.stratified.gender <- print(Total.encephalic.volume.stratified.gender)

```

```{r Total Encephalic Volumes relative standard deviations}

Total.encephalic.volume.RSD <- round((111353.18/1093437.27)*100,1)
Total.encephalic.volume.stratified.gender.female.RDS <- round((89784.56/1024921.71)*100,1)
Total.encephalic.volume.stratified.gender.male.RSD <- round((93652.70/1153388.38)*100,1)

```

```{r Total Encephalic Volume: Tables}

kable(Total.encephalic.volume)
kable(Total.encephalic.volume.RSD)
kable(Total.encephalic.volume.stratified.gender)
kable(Total.encephalic.volume.stratified.gender.female.RDS)
kable(Total.encephalic.volume.stratified.gender.male.RSD)

```

```{r Total Encephalic Volume: Absolute Volumes and Gender Plot}

Total.encephalic.volume.plot <-  ggplot(All.Volumes, aes(x= Gender, y = `Total encephalic volume (without ventricles)`))  +
  geom_quasirandom(aes(color = `Age (years)`), alpha = 1, size = 2, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  geom_boxplot(aes(fill = Gender), alpha = 0.5, size = 0.3, width = 0.35, outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  xlab("Gender") + ylab("") +
  theme_minimal() +
  coord_flip() +
  ggtitle("TOTAL ENCEPHALIC VOLUME (mm3)") +
  theme(plot.title = element_text(hjust = 0.5))
Total.encephalic.volume.plot
ggsave("Total.encephalic.volume.plot.pdf", plot = Total.encephalic.volume.plot, width = 8, height = 6, units = "in", dpi = 600)

```

```{r Total Encephalic Volume: Absolute Volumes and Age Plot}

Total.encephalic.volume.Age.plot <-  ggplot(All.Volumes, aes(y=`Total encephalic volume (without ventricles)`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 0.8, shape = 16) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender, group = Gender), method='lm', se = F, alpha = 0.2,linetype = "longdash", size = 0.3, weight = 0.3) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  stat_cor(method = "pearson", label.y = 1250000, label.x = 65, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME encephalon") +
  theme(plot.title = element_text(hjust = 0.5))
Total.encephalic.volume.Age.plot
ggsave("Total.encephalic.volume.Age.plot.pdf", plot = Total.encephalic.volume.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```


# Topographic overview

## Absolute Volumes

```{r Topographic Overview: Absolute Volumes}

Frontal.lobe <- All.Volumes$`Total volume frontal pole`+
                       All.Volumes$`Total volume F1`+
                       All.Volumes$`Total volume F2`+
                       All.Volumes$`Total volume F3 orbital`+
                       All.Volumes$`Total volume F3 triangular`+
                       All.Volumes$`Total volume F3 opercular`+
                       All.Volumes$`Total volume anterior orbital`+
                       All.Volumes$`Total volume posterior orbital`+
                       All.Volumes$`Total volume medial orbital`+
                       All.Volumes$`Total volume lateral orbital`+
                       All.Volumes$`Total volume rectus`+
                       All.Volumes$`Total volume rostral`
  
Central.lobe <- (All.Volumes$`Total volume PreC`+
                       All.Volumes$`Total volume PostC`+
                       All.Volumes$`Total volume ParaC lobule`+
                       All.Volumes$`Total volume SubC gyrus`)
  
Parietal.lobe <- (All.Volumes$`Total volume SPL`+
                       All.Volumes$`Total volume SMG`+
                       All.Volumes$`Total volume ANG`+
                       All.Volumes$`Total volume Precuneus`)

Occipital.lobe <- (All.Volumes$`Total volume Cuneus`+
                       All.Volumes$`Total volume O1`+
                       All.Volumes$`Total volume O2`+
                       All.Volumes$`Total volume O3`+
                       All.Volumes$`Total volume occipital pole`+
                       All.Volumes$`Total volume lingual`)

Temporal.lobe <- (All.Volumes$`Total volume fusiform`+
                       All.Volumes$`Total volume T1`+
                       All.Volumes$`Total volume T2`+
                       All.Volumes$`Total volume T3`+
                       All.Volumes$`Total volume Planum temporale`+
                       All.Volumes$`Total volume Planum polare`+
                       All.Volumes$`Total volume temporal pole`)

Insular.lobe <- (All.Volumes$`Total volume short insular gyri`+
                       All.Volumes$`Total volume long insular gyri`)

Limbic.lobe <- (All.Volumes$`Total volume SCA`+
                       All.Volumes$`Total volume PHG`+
                       All.Volumes$`Total volume ant cingulate`+
                       All.Volumes$`Total volume mid cingulate`+
                       All.Volumes$`Total volume post cingulate`+
                       All.Volumes$`Total volume hippocampus` +
                       All.Volumes$`Total volume amygdala`)

Basal.ganglia <- (All.Volumes$`Total volume caudate`+
                       All.Volumes$`Total volume putamen`+
                       All.Volumes$`Total volume pallidum`)

Diencephalon <- (All.Volumes$`Total volume hypothalamus`+
                        All.Volumes$`Total volume thalamus`)

Brainstem <- (All.Volumes$`Total volume brainstem`)

Cerebellum <- (All.Volumes$`Total volume cerebellum`)

Topographic.overview.absolute <- as.data.frame(cbind(Frontal.lobe,
                                       Central.lobe,
                                       Parietal.lobe,
                                       Occipital.lobe,
                                       Temporal.lobe,
                                       Insular.lobe,
                                       Limbic.lobe,
                                       Basal.ganglia,
                                       Diencephalon,
                                       Brainstem,
                                       Cerebellum))

Topographic.overview.absolute$Gender <- All.Volumes$Gender
Topographic.overview.absolute$Age <- All.Volumes$`Age (years)`

Table.topographic.overview.absolute <- CreateTableOne(vars = c("Frontal.lobe","Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe","Limbic.lobe","Basal.ganglia", "Diencephalon","Brainstem","Cerebellum"), 
               data = Topographic.overview.absolute)

Table.topographic.overview.absolute.stratified.gender <- CreateTableOne(vars = c("Frontal.lobe","Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe","Limbic.lobe","Basal.ganglia", "Diencephalon","Brainstem","Cerebellum"), 
               strata = c("Gender"), data = Topographic.overview.absolute)

Table.topographic.overview.absolute <- print(Table.topographic.overview.absolute, contDigits = 10)
Table.topographic.overview.absolute.stratified.gender <- print(Table.topographic.overview.absolute.stratified.gender,contDigits = 10)

write.csv(Table.topographic.overview.absolute, "Table.topographic.overview.absolute.csv")
write.csv(Table.topographic.overview.absolute.stratified.gender, "Table.topographic.overview.absolute.stratified.gender.csv")

```

```{r Topographic Overview: Absolute Volumes relative standard deviations}

Table.topographic.overview.absolute.RSD <- as.data.frame(Table.topographic.overview.absolute)
Table.topographic.overview.absolute.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.topographic.overview.absolute.RSD[-1,]),
                                                                       ' (',fixed=TRUE)))
Table.topographic.overview.absolute.RSD <- data.frame(cbind(str_replace_all(Table.topographic.overview.absolute.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.topographic.overview.absolute.RSD$X2, "[)]", "")))
Table.topographic.overview.absolute.RSD$X1 <- as.character(Table.topographic.overview.absolute.RSD$X1)
Table.topographic.overview.absolute.RSD$X2 <- as.character(Table.topographic.overview.absolute.RSD$X2)
Table.topographic.overview.absolute.RSD <- as.data.frame(sapply(Table.topographic.overview.absolute.RSD, as.numeric))
Table.topographic.overview.absolute.RSD <- as.data.frame(Table.topographic.overview.absolute.RSD$X2/Table.topographic.overview.absolute.RSD$X1)
Table.topographic.overview.absolute.RSD <- round(Table.topographic.overview.absolute.RSD * 100, 1)


Table.topographic.overview.absolute.stratified.gender.RSD <- as.data.frame(Table.topographic.overview.absolute.stratified.gender)
Table.topographic.overview.absolute.stratified.gender.RSD <- select(Table.topographic.overview.absolute.stratified.gender.RSD, - c(p, test))

Table.topographic.overview.absolute.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.topographic.overview.absolute.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.topographic.overview.absolute.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.topographic.overview.absolute.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.topographic.overview.absolute.stratified.gender.RSD.female$X2, "[)]", "")))
Table.topographic.overview.absolute.stratified.gender.RSD.female$X1 <- as.character(Table.topographic.overview.absolute.stratified.gender.RSD.female$X1)
Table.topographic.overview.absolute.stratified.gender.RSD.female$X2 <- as.character(Table.topographic.overview.absolute.stratified.gender.RSD.female$X2)
Table.topographic.overview.absolute.stratified.gender.RSD.female <- as.data.frame(sapply(Table.topographic.overview.absolute.stratified.gender.RSD.female, as.numeric))
Table.topographic.overview.absolute.stratified.gender.RSD.female <- as.data.frame(Table.topographic.overview.absolute.stratified.gender.RSD.female$X2/Table.topographic.overview.absolute.stratified.gender.RSD.female$X1)
Table.topographic.overview.absolute.stratified.gender.RSD.female <- round(Table.topographic.overview.absolute.stratified.gender.RSD.female * 100, 1)

Table.topographic.overview.absolute.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.topographic.overview.absolute.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.topographic.overview.absolute.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.topographic.overview.absolute.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.topographic.overview.absolute.stratified.gender.RSD.male$X2, "[)]", "")))
Table.topographic.overview.absolute.stratified.gender.RSD.male$X1 <- as.character(Table.topographic.overview.absolute.stratified.gender.RSD.male$X1)
Table.topographic.overview.absolute.stratified.gender.RSD.male$X2 <- as.character(Table.topographic.overview.absolute.stratified.gender.RSD.male$X2)
Table.topographic.overview.absolute.stratified.gender.RSD.male <- as.data.frame(sapply(Table.topographic.overview.absolute.stratified.gender.RSD.male, as.numeric))
Table.topographic.overview.absolute.stratified.gender.RSD.male <- as.data.frame(Table.topographic.overview.absolute.stratified.gender.RSD.male$X2/Table.topographic.overview.absolute.stratified.gender.RSD.male$X1)
Table.topographic.overview.absolute.stratified.gender.RSD.male <- round(Table.topographic.overview.absolute.stratified.gender.RSD.male * 100, 1)

```

```{r Topographic Overview: Absolute Volumes Tables}

kable(Table.topographic.overview.absolute)
kable(Table.topographic.overview.absolute.RSD)
kable(Table.topographic.overview.absolute.stratified.gender)
kable(Table.topographic.overview.absolute.stratified.gender.RSD.female)
kable(Table.topographic.overview.absolute.stratified.gender.RSD.male)

```

```{r Topographic Overview: Absolute Volumes and Gender Plot}

Topographic.overview.absolute <- select(Topographic.overview.absolute, - c(Gender, Age))
Topographic.overview.absolute.plotdata <- gather(Topographic.overview.absolute, "anatomical.structure", "relative.volume")
Topographic.overview.absolute.plotdata$Gender <- All.Volumes$Gender
Topographic.overview.absolute.plotdata$Age <- All.Volumes$`Age (years)`

Topographic.overview.absolute.plotdata$Gender <- factor(Topographic.overview.absolute.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Topographic.overview.absolute.plotdata$anatomical.structure <- factor(Topographic.overview.absolute.plotdata$anatomical.structure, levels = rev(c("Frontal.lobe", "Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe", "Limbic.lobe", "Basal.ganglia", "Diencephalon", "Brainstem", "Cerebellum")), rev(c("Frontal lobe", "Central lobe", "Parietal lobe", "Occipital lobe", "Temporal lobe", "Insular lobe", "Limbic lobe", "Basal ganglia", "Diencephalon", "Brainstem", "Cerebellum")))

Topographic.overview.absolute.plot <-  ggplot(Topographic.overview.absolute.plotdata, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Absolute volume (in %)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("TOPOGRAPHIC OVERVIEW") +
  theme(plot.title = element_text(hjust = 0.5))

Topographic.overview.absolute.plot
ggsave("Topographic.overview.absolute.plot.pdf", plot = Topographic.overview.absolute.plot, width = 12, height = 6, units = "in", dpi = 600)

```

```{r Topographic Overview: Absolute Volumes and Age Plot}

Topographic.overview.absolute$Gender <- All.Volumes$Gender
Topographic.overview.absolute$Age <- All.Volumes$`Age (years)`

Total.frontal.lobe.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Frontal.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 230000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME frontal lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.frontal.lobe.Age.plot
ggsave("TO.Total.frontal.lobe.Age.plot.pdf", plot = Total.frontal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.central.lobe.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Central.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 97000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME central lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.central.lobe.Age.plot
ggsave("TO.Total.central.lobe.Age.plot.pdf", plot = Total.central.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.parietal.lobe.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Parietal.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 145000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME parietal lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.parietal.lobe.Age.plot
ggsave("TO.Total.parietal.lobe.Age.plot.pdf", plot = Total.parietal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.occipital.lobe.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Occipital.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 86000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME occipital lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.occipital.lobe.Age.plot
ggsave("TO.Total.occipital.lobe.Age.plot.pdf", plot = Total.occipital.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.temporal.lobe.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Temporal.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 86000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME temporal lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.temporal.lobe.Age.plot
ggsave("TO.Total.temporal.lobe.Age.plot.pdf", plot = Total.temporal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.insular.lobe.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Insular.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 29000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME insular lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.insular.lobe.Age.plot
ggsave("TO.Total.insular.lobe.Age.plot.pdf", plot = Total.insular.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.limbic.lobe.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Limbic.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 82000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME limbic lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.limbic.lobe.Age.plot
ggsave("TO.Total.limbic.lobe.Age.plot.pdf", plot = Total.limbic.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Basal.ganglia.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Basal.ganglia, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 24000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME basal ganglia") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Basal.ganglia.Age.plot
ggsave("TO.Total.Basal.ganglia.Age.plot.pdf", plot = Total.Basal.ganglia.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Diencephalon.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Diencephalon, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 25000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME diencephalon") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Diencephalon.Age.plot
ggsave("TO.Total.Diencephalon.Age.plot.pdf", plot = Total.Diencephalon.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Brainstem.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Brainstem, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 33000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME brainstem") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Brainstem.Age.plot
ggsave("TO.Total.Brainstem.Age.plot.pdf", plot = Total.Brainstem.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Cerebellum.Age.plot <-  ggplot(Topographic.overview.absolute, aes(y=Cerebellum, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 122000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME cerebellum") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Cerebellum.Age.plot
ggsave("TO.Total.Cerebellum.Age.plot.pdf", plot = Total.Cerebellum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```

## Relative Volumes

```{r Topographic Overview: Relative Volumes}

Topographic.overview.absolute <- select(Topographic.overview.absolute, - c(Gender, Age))
Topographic.overview.relative1 <- (100 * (Topographic.overview.absolute/All.Volumes$`Total encephalic volume (without ventricles)`))

Topographic.overview.relative2 <- CreateTableOne(vars = c("Frontal.lobe","Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe","Limbic.lobe","Basal.ganglia", "Diencephalon","Brainstem","Cerebellum"), 
               data = Topographic.overview.relative1)
Topographic.overview.relative1$Gender <- All.Volumes$Gender
Topographic.overview.relative.stratified.gender <- CreateTableOne(vars = c("Frontal.lobe","Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe","Limbic.lobe","Basal.ganglia", "Diencephalon","Brainstem","Cerebellum"), 
                strata = c("Gender"), data = Topographic.overview.relative1)

Topographic.overview.relative2 <- print(Topographic.overview.relative2, quote = FALSE, contDigits = 10)
Topographic.overview.relative.stratified.gender <- print(Topographic.overview.relative.stratified.gender, quote = FALSE, contDigits = 10)

```


```{r Topographic Overview: Relative Volumes relative standard deviations}

Table.topographic.overview.relative.RSD <- as.data.frame(Topographic.overview.relative2)
Table.topographic.overview.relative.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.topographic.overview.relative.RSD[-1,]),
                                                                       ' (',fixed=TRUE)))
Table.topographic.overview.relative.RSD <- data.frame(cbind(str_replace_all(Table.topographic.overview.relative.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.topographic.overview.relative.RSD$X2, "[)]", "")))
Table.topographic.overview.relative.RSD$X1 <- as.character(Table.topographic.overview.relative.RSD$X1)
Table.topographic.overview.relative.RSD$X2 <- as.character(Table.topographic.overview.relative.RSD$X2)
Table.topographic.overview.relative.RSD <- as.data.frame(sapply(Table.topographic.overview.relative.RSD, as.numeric))
Table.topographic.overview.relative.RSD <- as.data.frame(Table.topographic.overview.relative.RSD$X2/Table.topographic.overview.relative.RSD$X1)
Table.topographic.overview.relative.RSD <- round(Table.topographic.overview.relative.RSD*100, 1)

Table.topographic.overview.relative.stratified.gender.RSD <- as.data.frame(Topographic.overview.relative.stratified.gender)
Table.topographic.overview.relative.stratified.gender.RSD <- select(Table.topographic.overview.relative.stratified.gender.RSD, - c(p, test))

Table.topographic.overview.relative.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.topographic.overview.relative.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.topographic.overview.relative.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.topographic.overview.relative.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.topographic.overview.relative.stratified.gender.RSD.female$X2, "[)]", "")))
Table.topographic.overview.relative.stratified.gender.RSD.female$X1 <- as.character(Table.topographic.overview.relative.stratified.gender.RSD.female$X1)
Table.topographic.overview.relative.stratified.gender.RSD.female$X2 <- as.character(Table.topographic.overview.relative.stratified.gender.RSD.female$X2)
Table.topographic.overview.relative.stratified.gender.RSD.female <- as.data.frame(sapply(Table.topographic.overview.relative.stratified.gender.RSD.female, as.numeric))
Table.topographic.overview.relative.stratified.gender.RSD.female <- as.data.frame(Table.topographic.overview.relative.stratified.gender.RSD.female$X2/Table.topographic.overview.relative.stratified.gender.RSD.female$X1)
Table.topographic.overview.relative.stratified.gender.RSD.female <- round(Table.topographic.overview.relative.stratified.gender.RSD.female*100, 1)

Table.topographic.overview.relative.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.topographic.overview.relative.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.topographic.overview.relative.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.topographic.overview.relative.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.topographic.overview.relative.stratified.gender.RSD.male$X2, "[)]", "")))
Table.topographic.overview.relative.stratified.gender.RSD.male$X1 <- as.character(Table.topographic.overview.relative.stratified.gender.RSD.male$X1)
Table.topographic.overview.relative.stratified.gender.RSD.male$X2 <- as.character(Table.topographic.overview.relative.stratified.gender.RSD.male$X2)
Table.topographic.overview.relative.stratified.gender.RSD.male <- as.data.frame(sapply(Table.topographic.overview.relative.stratified.gender.RSD.male, as.numeric))
Table.topographic.overview.relative.stratified.gender.RSD.male <- as.data.frame(Table.topographic.overview.relative.stratified.gender.RSD.male$X2/Table.topographic.overview.relative.stratified.gender.RSD.male$X1)
Table.topographic.overview.relative.stratified.gender.RSD.male <- round(Table.topographic.overview.relative.stratified.gender.RSD.male*100, 1)

```

```{r Topographic Overview: Relative Volumes Tables}

kable(Topographic.overview.relative2)
kable(Table.topographic.overview.relative.RSD)
kable(Topographic.overview.relative.stratified.gender)
kable(Table.topographic.overview.relative.stratified.gender.RSD.female)
kable(Table.topographic.overview.relative.stratified.gender.RSD.male)

```

```{r Topographic overwiew: Relative Volumes and Gender Plot}

Topographic.overview.relative1 <- select(Topographic.overview.relative1, - c(Gender))
Topographic.overview.relative.plotdata <- gather(Topographic.overview.relative1, "anatomical.structure", "relative.volume")
Topographic.overview.relative.plotdata$Gender <- All.Volumes$Gender
Topographic.overview.relative.plotdata$Age <- All.Volumes$`Age (years)`

Topographic.overview.relative.plotdata$Gender <- factor(Topographic.overview.relative.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Topographic.overview.relative.plotdata$anatomical.structure <- factor(Topographic.overview.relative.plotdata$anatomical.structure, levels = rev(c("Frontal.lobe", "Central.lobe", "Parietal.lobe", "Occipital.lobe", "Temporal.lobe", "Insular.lobe", "Limbic.lobe", "Basal.ganglia", "Diencephalon", "Brainstem", "Cerebellum")), rev(c("Frontal lobe", "Central lobe", "Parietal lobe", "Occipital lobe", "Temporal lobe", "Insular lobe", "Limbic lobe", "Basal ganglia", "Diencephalon", "Brainstem", "Cerebellum")))

Topographic.overview.relative.plot <-  ggplot(Topographic.overview.relative.plotdata, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Relative volume (in %)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("TOPOGRAPHIC OVERVIEW") +
  theme(plot.title = element_text(hjust = 0.5))

Topographic.overview.relative.plot
ggsave("Topographic.overview.relative.plot.pdf", plot = Topographic.overview.relative.plot, width = 12, height = 6, units = "in", dpi = 600)

```

```{r Topographic overview: Relative Volumes and Age Plot}

Topographic.overview.relative1$Gender <- All.Volumes$Gender
Topographic.overview.relative1$Age <- All.Volumes$`Age (years)`

Relative.frontal.lobe.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Frontal.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 19.3, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME frontal lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.frontal.lobe.Age.plot
ggsave("TOR.Relative.frontal.lobe.Age.plot.pdf", plot = Relative.frontal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Central.lobe.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Central.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 9.7, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME central lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Central.lobe.Age.plot
ggsave("TOR.Relative.Central.lobe.Age.plot.pdf", plot = Relative.Central.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Parietal.lobe.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Parietal.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 13.2, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME parietal lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Parietal.lobe.Age.plot
ggsave("TOR.Relative.Parietal.lobe.Age.plot.pdf", plot = Relative.Parietal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Occipital.lobe.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Occipital.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 7.6, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME occipital lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Occipital.lobe.Age.plot
ggsave("TOR.Relative.Occipital.lobe.Age.plot.pdf", plot = Relative.Occipital.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Temporal.lobe.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Temporal.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 8.6, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME temporal lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Temporal.lobe.Age.plot
ggsave("TOR.Relative.Temporal.lobe.Age.plot.pdf", plot = Relative.Temporal.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Insular.lobe.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Insular.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.05, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME insular lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Insular.lobe.Age.plot
ggsave("TOR.Relative.Insular.lobe.Age.plot.pdf", plot = Relative.Insular.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Limbic.lobe.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Limbic.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 7.7, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME limbic lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Limbic.lobe.Age.plot
ggsave("TOR.Relative.Limbic.lobe.Age.plot.pdf", plot = Relative.Limbic.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Basal.ganglia.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Basal.ganglia, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.25, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME basal ganglia") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Basal.ganglia.Age.plot
ggsave("TOR.Relative.Basal.ganglia.Age.plot.pdf", plot = Relative.Basal.ganglia.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Diencephalon.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Diencephalon, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.25, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME diencephalon") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Diencephalon.Age.plot
ggsave("TOR.Relative.Diencephalon.Age.plot.pdf", plot = Relative.Diencephalon.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Brainstem.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Brainstem, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3.18, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME brainstem") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Brainstem.Age.plot
ggsave("TOR.Relative.Brainstem.Age.plot.pdf", plot = Relative.Brainstem.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Cerebellum.Age.plot <-  ggplot(Topographic.overview.relative1, aes(y=Cerebellum, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 12.7, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME cerebellum") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Cerebellum.Age.plot
ggsave("TOR.Relative.Cerebellum.Age.plot.pdf", plot = Relative.Cerebellum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```


# The Prosencephalon

## Absolute Volumes

```{r The Prosencephalon: Absolute Volumes}

Frontal.pole <- All.Volumes$`Total volume frontal pole`
F1 <- All.Volumes$`Total volume F1`
F2 <- All.Volumes$`Total volume F2`
F3.orbital <- All.Volumes$`Total volume F3 orbital`
F3.triangular <- All.Volumes$`Total volume F3 triangular`
F3.opercular <- All.Volumes$`Total volume F3 opercular`
Anterior.orbital <- All.Volumes$`Total volume anterior orbital`
Medial.orbital <- All.Volumes$`Total volume medial orbital`
Lateral.orbital <- All.Volumes$`Total volume lateral orbital`
Posterior.orbital <- All.Volumes$`Total volume posterior orbital`
Rectus <- All.Volumes$`Total volume rectus`
Rostral <- All.Volumes$`Total volume rostral`

Precentral <- All.Volumes$`Total volume PreC`
Postcentral <- All.Volumes$`Total volume PostC`
Paracentral.lobule <- All.Volumes$`Total volume ParaC lobule`
Subcentral <- All.Volumes$`Total volume SubC gyrus`

SPL <- All.Volumes$`Total volume SPL`
SMG <- All.Volumes$`Total volume SMG`
ANG <- All.Volumes$`Total volume ANG`
Precuneus <- All.Volumes$`Total volume Precuneus`

Cuneus <- All.Volumes$`Total volume Cuneus`
O1 <- All.Volumes$`Total volume O1`
O2 <- All.Volumes$`Total volume O2`
O3 <- All.Volumes$`Total volume O3`
Occipital.pole <- All.Volumes$`Total volume occipital pole`
Lingual <- All.Volumes$`Total volume lingual`

Fusiform <- All.Volumes$`Total volume fusiform`
Temporal.pole <- All.Volumes$`Total volume T1`
T1 <- All.Volumes$`Total volume T2`
T2 <- All.Volumes$`Total volume T3`
T3 <- All.Volumes$`Total volume Planum temporale`
Planum.temporale <- All.Volumes$`Total volume Planum polare`
Planum.polare <- All.Volumes$`Total volume temporal pole`

Short.insular <- All.Volumes$`Total volume short insular gyri`
Long.insular <- All.Volumes$`Total volume long insular gyri`

SCA <- All.Volumes$`Total volume SCA`
Cingulate.anterior <- All.Volumes$`Total volume ant cingulate`
Cingulate.middle <- All.Volumes$`Total volume mid cingulate`
Cingulate.posterior <- All.Volumes$`Total volume post cingulate`
PHG <- All.Volumes$`Total volume PHG`
Hippocampus <- All.Volumes$`Total volume hippocampus`
Amygdala <- All.Volumes$`Total volume amygdala`

Corpus.callosum <- All.Volumes$`Total volume corpus callosum`

Claustrum <- All.Volumes$`Total volume claustrum`
Putamen <- All.Volumes$`Total volume putamen`
Caudate <- All.Volumes$`Total volume caudate`
Globus.pallidum <- All.Volumes$`Total volume pallidum`
Internal.capsule <- All.Volumes$`Total volume internal capsule`
Innominate.substance <- All.Volumes$`Total volume substantia innominata`
Hypothalamus <- All.Volumes$`Total volume hypothalamus`
Thalamus <- All.Volumes$`Total volume thalamus`

Prosencephalon.absolute <- as.data.frame(cbind(
  Frontal.pole,
  F1,
  F2,
  F3.orbital, 
  F3.triangular, 
  F3.opercular, 
  Anterior.orbital, 
  Medial.orbital, 
  Lateral.orbital, 
  Posterior.orbital,
  Rectus, 
  Rostral, 
  Precentral, 
  Postcentral, 
  Paracentral.lobule, 
  Subcentral, 
  SPL, 
  SMG, 
  ANG, 
  Precuneus,
  Cuneus, 
  O1,
  O2, 
  O3, 
  Occipital.pole, 
  Lingual, 
  Fusiform, 
  Temporal.pole, 
  T1,
  T2, 
  T3, 
  Planum.temporale, 
  Planum.polare, 
  Short.insular, 
  Long.insular, 
  SCA, 
  Cingulate.anterior, 
  Cingulate.middle, 
  Cingulate.posterior, 
  PHG, 
  Hippocampus, 
  Amygdala, 
  Corpus.callosum, 
  Claustrum, 
  Putamen, 
  Caudate, 
  Globus.pallidum, 
  Internal.capsule,
  Innominate.substance, 
  Hypothalamus,
  Thalamus
))

Prosencephalon.absolute$Gender <- All.Volumes$Gender

Table.Prosencephalon.absolute <- CreateTableOne(
  vars = c("Frontal.pole",
  "F1",
  "F2",
  "F3.orbital", 
  "F3.triangular", 
  "F3.opercular", 
  "Anterior.orbital", 
  "Medial.orbital", 
  "Lateral.orbital", 
  "Posterior.orbital",
  "Rectus", 
  "Rostral", 
  "Precentral", 
  "Postcentral", 
  "Paracentral.lobule", 
  "Subcentral", 
  "SPL", 
  "SMG", 
  "ANG", 
  "Precuneus",
  "Cuneus", 
  "O1",
  "O2", 
  "O3", 
  "Occipital.pole", 
  "Lingual", 
  "Fusiform", 
  "Temporal.pole", 
  "T1",
  "T2", 
  "T3", 
  "Planum.temporale", 
  "Planum.polare", 
  "Short.insular", 
  "Long.insular", 
  "SCA", 
  "Cingulate.anterior", 
  "Cingulate.middle", 
  "Cingulate.posterior", 
  "PHG", 
  "Hippocampus", 
  "Amygdala", 
  "Corpus.callosum", 
  "Claustrum", 
  "Putamen", 
  "Caudate", 
  "Globus.pallidum", 
  "Internal.capsule",
  "Innominate.substance", 
  "Hypothalamus",
  "Thalamus"),
  data = Prosencephalon.absolute)

Table.Prosencephalon.absolute.stratified.gender <- CreateTableOne(
  vars = c("Frontal.pole",
  "F1",
  "F2",
  "F3.orbital", 
  "F3.triangular", 
  "F3.opercular", 
  "Anterior.orbital", 
  "Medial.orbital", 
  "Lateral.orbital", 
  "Posterior.orbital",
  "Rectus", 
  "Rostral", 
  "Precentral", 
  "Postcentral", 
  "Paracentral.lobule", 
  "Subcentral", 
  "SPL", 
  "SMG", 
  "ANG", 
  "Precuneus",
  "Cuneus", 
  "O1",
  "O2", 
  "O3", 
  "Occipital.pole", 
  "Lingual", 
  "Fusiform", 
  "Temporal.pole", 
  "T1",
  "T2", 
  "T3", 
  "Planum.temporale", 
  "Planum.polare", 
  "Short.insular", 
  "Long.insular", 
  "SCA", 
  "Cingulate.anterior", 
  "Cingulate.middle", 
  "Cingulate.posterior", 
  "PHG", 
  "Hippocampus", 
  "Amygdala", 
  "Corpus.callosum", 
  "Claustrum", 
  "Putamen", 
  "Caudate", 
  "Globus.pallidum", 
  "Internal.capsule",
  "Innominate.substance", 
  "Hypothalamus",
  "Thalamus"),
  strata = c("Gender"),
  data = Prosencephalon.absolute)

Table.Prosencephalon.absolute <- print(Table.Prosencephalon.absolute, contDigits = 2)
Table.Prosencephalon.absolute.stratified.gender <- print(Table.Prosencephalon.absolute.stratified.gender, contDigits = 2)

write.csv(Table.Prosencephalon.absolute, "Table.Prosencephalon.absolute.csv")
write.csv(Table.Prosencephalon.absolute.stratified.gender, "Table.Prosencephalon.absolute.stratified.gender.csv")

```

```{r The Prosencephalon: Absolute Volumes relative standard deviations}

Table.Prosencephalon.absolute.RSD <- as.data.frame(Table.Prosencephalon.absolute)
Table.Prosencephalon.absolute.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Prosencephalon.absolute.RSD[-1,]),
                                                                       ' (',fixed=TRUE)))
Table.Prosencephalon.absolute.RSD <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.Prosencephalon.absolute.RSD$X2, "[)]", "")))
Table.Prosencephalon.absolute.RSD$X1 <- as.character(Table.Prosencephalon.absolute.RSD$X1)
Table.Prosencephalon.absolute.RSD$X2 <- as.character(Table.Prosencephalon.absolute.RSD$X2)
Table.Prosencephalon.absolute.RSD <- as.data.frame(sapply(Table.Prosencephalon.absolute.RSD, as.numeric))
Table.Prosencephalon.absolute.RSD <- as.data.frame(Table.Prosencephalon.absolute.RSD$X2/Table.Prosencephalon.absolute.RSD$X1)
Table.Prosencephalon.absolute.RSD <- round(Table.Prosencephalon.absolute.RSD * 100, 1)


Table.Prosencephalon.absolute.stratified.gender.RSD <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender)
Table.Prosencephalon.absolute.stratified.gender.RSD <- select(Table.Prosencephalon.absolute.stratified.gender.RSD, - c(p, test))

Table.Prosencephalon.absolute.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Prosencephalon.absolute.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2)
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Prosencephalon.absolute.stratified.gender.RSD.female, as.numeric))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2/Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- round(Table.Prosencephalon.absolute.stratified.gender.RSD.female * 100, 1)

Table.Prosencephalon.absolute.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Prosencephalon.absolute.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Prosencephalon.absolute.stratified.gender.RSD.male, as.numeric))
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2/Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- round(Table.Prosencephalon.absolute.stratified.gender.RSD.male * 100, 1)

```

```{r The Prosencephalon: Absolute Volumes Tables}

kable(Table.Prosencephalon.absolute)
kable(Table.Prosencephalon.absolute.RSD)
kable(Table.Prosencephalon.absolute.stratified.gender)
kable(Table.Prosencephalon.absolute.stratified.gender.RSD.female)
kable(Table.Prosencephalon.absolute.stratified.gender.RSD.male)

```

```{r The Prosencephalon_Gyri: Absolute Volumes and Gender Plot}

Prosencephalon.absolute <- select(Prosencephalon.absolute, - c(Gender))
Prosencephalon.absolute1 <- Prosencephalon.absolute[,-c(41:51)]
Prosencephalon.absolute2 <- Prosencephalon.absolute[, c(41:51)]

names.anatomical.structures.temporary <- c("Frontal.pole",
  "F1",
  "F2",
  "F3.orbital", 
  "F3.triangular", 
  "F3.opercular", 
  "Anterior.orbital", 
  "Medial.orbital", 
  "Lateral.orbital", 
  "Posterior.orbital",
  "Rectus", 
  "Rostral", 
  "Precentral", 
  "Postcentral", 
  "Paracentral.lobule", 
  "Subcentral", 
  "SPL", 
  "SMG", 
  "ANG", 
  "Precuneus",
  "Cuneus", 
  "O1",
  "O2", 
  "O3", 
  "Occipital.pole", 
  "Lingual", 
  "Fusiform", 
  "Temporal.pole", 
  "T1",
  "T2", 
  "T3", 
  "Planum.temporale", 
  "Planum.polare", 
  "Short.insular", 
  "Long.insular", 
  "SCA", 
  "Cingulate.anterior", 
  "Cingulate.middle", 
  "Cingulate.posterior", 
  "PHG")

names.anatomical.structures.definitive <- c("Frontal pole",
  "F1",
  "F2",
  "F3 orbital", 
  "F3 triangular", 
  "F3 opercular", 
  "Anterior orbital", 
  "Medial orbital", 
  "Lateral orbital", 
  "Posterior orbital",
  "Rectus", 
  "Rostral", 
  "Precentral", 
  "Postcentral", 
  "Paracentral lobule", 
  "Subcentral", 
  "SPL", 
  "SMG", 
  "ANG", 
  "Precuneus",
  "Cuneus", 
  "O1",
  "O2", 
  "O3", 
  "Occipital pole", 
  "Lingual", 
  "Fusiform", 
  "Temporal pole", 
  "T1",
  "T2", 
  "T3", 
  "Planum temporale", 
  "Planum polare", 
  "Short insular", 
  "Long insular", 
  "SCA", 
  "Cingulate anterior", 
  "Cingulate middle", 
  "Cingulate posterior", 
  "PHG")


Prosencephalon.absolute.plotdata1 <- gather(Prosencephalon.absolute1, "anatomical.structure", "relative.volume")
Prosencephalon.absolute.plotdata1$Gender <- All.Volumes$Gender
Prosencephalon.absolute.plotdata1$Age <- All.Volumes$`Age (years)`
Prosencephalon.absolute.plotdata1$Gender <- factor(Prosencephalon.absolute.plotdata1$Gender, levels = c("f", "m"), c("f", "m"))

Prosencephalon.absolute.plotdata1$anatomical.structure <- factor(Prosencephalon.absolute.plotdata1$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Prosencephalon.absolute.plot1 <-  ggplot(Prosencephalon.absolute.plotdata1, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Absolute volume (in mm3)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("CEREBRAL GYRI") +
  theme(plot.title = element_text(hjust = 0.5))

Prosencephalon.absolute.plot1
ggsave("Prosencephalon.absolute.plot1.pdf", plot = Prosencephalon.absolute.plot1, width = 14, height = 12, units = "in", dpi = 600)

```

```{r The Prosencephalon_Central Structures: Absolute Volumes and Gender Plot}

names.anatomical.structures.temporary <- c(
  "Corpus.callosum", 
  "Claustrum", 
  "Putamen", 
  "Caudate", 
  "Globus.pallidum", 
  "Internal.capsule",
  "Innominate.substance", 
  "Hypothalamus",
  "Thalamus", 
  "Hippocampus", 
  "Amygdala")

names.anatomical.structures.definitive <- c(
  "Corpus callosum", 
  "Claustrum", 
  "Putamen", 
  "Caudate", 
  "Globus pallidum", 
  "Internal capsule",
  "Innominate substance", 
  "Hypothalamus",
  "Thalamus",
  "Hippocampus", 
  "Amygdala")

Prosencephalon.absolute.plotdata2 <- gather(Prosencephalon.absolute2, "anatomical.structure", "relative.volume")
Prosencephalon.absolute.plotdata2$Gender <- All.Volumes$Gender
Prosencephalon.absolute.plotdata2$Age <- All.Volumes$`Age (years)`

Prosencephalon.absolute.plotdata2$Gender <- factor(Prosencephalon.absolute.plotdata2$Gender, levels = c("f", "m"), c("f", "m"))
Prosencephalon.absolute.plotdata2$anatomical.structure <- factor(Prosencephalon.absolute.plotdata2$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Prosencephalon.absolute.plot2 <-  ggplot(Prosencephalon.absolute.plotdata2, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Absolute volume (in mm3)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("CENTRAL PROSENCEPHALON") +
  theme(plot.title = element_text(hjust = 0.5))

Prosencephalon.absolute.plot2
ggsave("Prosencephalon.absolute.plot2.pdf", plot = Prosencephalon.absolute.plot2, width = 10, height = 6, units = "in", dpi = 600)

```

```{r The Prosencephalon: Absolute Volumes and Age Plot}

Prosencephalon.absolute$Gender <- All.Volumes$Gender
Prosencephalon.absolute$Age <- All.Volumes$`Age (years)`

Total.Frontal.pole.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Frontal.pole, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 6700, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME frontal pole") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Frontal.pole.Age.plot
ggsave("Total.Frontal.pole.Age.plot.pdf", plot = Total.Frontal.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.F1.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=F1, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 77000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME F1") +
  theme(plot.title = element_text(hjust = 0.5))
Total.F1.Age.plot
ggsave("Total.F1.Age.plot.pdf", plot = Total.F1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.F2.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=F2, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 72000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME F2") +
  theme(plot.title = element_text(hjust = 0.5))
Total.F2.Age.plot
ggsave("Total.F2.Age.plot.pdf", plot = Total.F2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.F3.orbital.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=F3.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4600, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME F3 orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Total.F3.orbital.Age.plot
ggsave("Total.F3.orbital.Age.plot.pdf", plot = Total.F3.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.F3.triangular.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=F3.triangular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 13300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME F3 triangular") +
  theme(plot.title = element_text(hjust = 0.5))
Total.F3.triangular.Age.plot
ggsave("Total.F3.triangular.Age.plot.pdf", plot = Total.F3.triangular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.F3.opercular.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=F3.opercular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 17300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME F3 opercular") +
  theme(plot.title = element_text(hjust = 0.5))
Total.F3.opercular.Age.plot
ggsave("Total.F3.opercular.Age.plot.pdf", plot = Total.F3.opercular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Anterior.orbital.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Anterior.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 5200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME anterior orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Anterior.orbital.Age.plot
ggsave("Total.Anterior.orbital.Age.plot.pdf", plot = Total.Anterior.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Medial.orbital.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Medial.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 10500, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME medial orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Medial.orbital.Age.plot
ggsave("Total.Medial.orbital.Age.plot.pdf", plot = Total.Medial.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Lateral.orbital.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Lateral.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 8200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME lateral orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Lateral.orbital.Age.plot
ggsave("Total.Lateral.orbital.Age.plot.pdf", plot = Total.Lateral.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Posterior.orbital.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Posterior.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 10200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME posterior orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Posterior.orbital.Age.plot
ggsave("Total.Posterior.orbital.Age.plot.pdf", plot = Total.Posterior.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Rectus.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Rectus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 22000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME rectus") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Rectus.Age.plot
ggsave("Total.Rectus.Age.plot.pdf", plot = Total.Rectus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Rostral.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Rostral, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME rostral") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Rostral.Age.plot
ggsave("Total.Rostral.Age.plot.pdf", plot = Total.Rostral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Precentral.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Precentral, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 48000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME precentral") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Precentral.Age.plot
ggsave("Total.Precentral.Age.plot.pdf", plot = Total.Precentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Postcentral.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Postcentral, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 26500, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME postcentral") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Postcentral.Age.plot
ggsave("Total.Postcentral.Age.plot.pdf", plot = Total.Postcentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Paracentral.lobule.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Paracentral.lobule, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 17200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME paracentral lobule") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Paracentral.lobule.Age.plot
ggsave("Total.Paracentral.lobule.Age.plot.pdf", plot = Total.Paracentral.lobule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Subcentral.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Subcentral, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 7200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME subcentral") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Subcentral.Age.plot
ggsave("Total.Subcentral.Age.plot.pdf", plot = Total.Subcentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.SPL.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=SPL, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 38000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME superior parietal lobule") +
  theme(plot.title = element_text(hjust = 0.5))
Total.SPL.Age.plot
ggsave("Total.SPL.Age.plot.pdf", plot = Total.SPL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.SMG.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=SMG, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 36000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME supramarginal") +
  theme(plot.title = element_text(hjust = 0.5))
Total.SMG.Age.plot
ggsave("Total.SMG.Age.plot.pdf", plot = Total.SMG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.ANG.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=ANG, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 38000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME angular") +
  theme(plot.title = element_text(hjust = 0.5))
Total.ANG.Age.plot
ggsave("Total.ANG.Age.plot.pdf", plot = Total.ANG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Precuneus.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Precuneus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 33000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME precuneus") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Precuneus.Age.plot
ggsave("Total.Precuneus.Age.plot.pdf", plot = Total.Precuneus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Cuneus.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Cuneus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 11700, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME cuneus") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Cuneus.Age.plot
ggsave("Total.Cuneus.Age.plot.pdf", plot = Total.Cuneus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.O1.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=O1, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 11200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME O1") +
  theme(plot.title = element_text(hjust = 0.5))
Total.O1.Age.plot
ggsave("Total.O1.Age.plot.pdf", plot = Total.O1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.O2.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=O2, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 18000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME O2") +
  theme(plot.title = element_text(hjust = 0.5))
Total.O2.Age.plot
ggsave("Total.O2.Age.plot.pdf", plot = Total.O2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.O3.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=O3, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 11500, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME O3") +
  theme(plot.title = element_text(hjust = 0.5))
Total.O3.Age.plot
ggsave("Total.O3.Age.plot.pdf", plot = Total.O3.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Occipital.pole.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Occipital.pole, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 13200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME occipital pole") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Occipital.pole.Age.plot
ggsave("Total.Occipital.pole.Age.plot.pdf", plot = Total.Occipital.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Lingual.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Lingual, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 25000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME lingual") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Lingual.Age.plot
ggsave("Total.Lingual.Age.plot.pdf", plot = Total.Lingual.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Fusiform.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Fusiform, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 26000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME fusiform") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Fusiform.Age.plot
ggsave("Total.Fusiform.Age.plot.pdf", plot = Total.Fusiform.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Temporal.pole.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Temporal.pole, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 31000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME temporal pole") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Temporal.pole.Age.plot
ggsave("Total.Temporal.pole.Age.plot.pdf", plot = Total.Temporal.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.T1.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=T1, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 31000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME T1") +
  theme(plot.title = element_text(hjust = 0.5))
Total.T1.Age.plot
ggsave("Total.T1.Age.plot.pdf", plot = Total.T1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.T2.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=T2, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 29000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME T2") +
  theme(plot.title = element_text(hjust = 0.5))
Total.T2.Age.plot
ggsave("Total.T2.Age.plot.pdf", plot = Total.T2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.T3.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=T3, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 6100, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME T3") +
  theme(plot.title = element_text(hjust = 0.5))
Total.T3.Age.plot
ggsave("Total.T3.Age.plot.pdf", plot = Total.T3.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Planum.temporale.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Planum.temporale, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4100, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME planum temporale") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Planum.temporale.Age.plot
ggsave("Total.Planum.temporale.Age.plot.pdf", plot = Total.Planum.temporale.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Planum.polare.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Planum.polare, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 13300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME planum polare") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Planum.polare.Age.plot
ggsave("Total.Planum.polare.Age.plot.pdf", plot = Total.Planum.polare.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Short.insular.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Short.insular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 19000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME short insular") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Short.insular.Age.plot
ggsave("Total.Short.insular.Age.plot.pdf", plot = Total.Short.insular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Long.insular.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Long.insular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 12000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME long insular") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Long.insular.Age.plot
ggsave("Total.Long.insular.Age.plot.pdf", plot = Total.Long.insular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.SCA.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=SCA, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3800, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME subcallosal area") +
  theme(plot.title = element_text(hjust = 0.5))
Total.SCA.Age.plot
ggsave("Total.SCA.Age.plot.pdf", plot = Total.SCA.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Cingulate.anterior.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Cingulate.anterior, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 17300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME cingulate anterior") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Cingulate.anterior.Age.plot
ggsave("Total.Cingulate.anterior.Age.plot.pdf", plot = Total.Cingulate.anterior.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Cingulate.middle.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Cingulate.middle, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 20300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME cingulate middle") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Cingulate.middle.Age.plot
ggsave("Total.Cingulate.middle.Age.plot.pdf", plot = Total.Cingulate.middle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Cingulate.posterior.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Cingulate.posterior, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 24300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME cingulate posterior") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Cingulate.posterior.Age.plot
ggsave("Total.Cingulate.posterior.Age.plot.pdf", plot = Total.Cingulate.posterior.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.PHG.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=PHG, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 14000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME PHG") +
  theme(plot.title = element_text(hjust = 0.5))
Total.PHG.Age.plot
ggsave("Total.PHG.Age.plot.pdf", plot = Total.PHG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Hippocampus.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Hippocampus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 9700, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME hippocampus") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Hippocampus.Age.plot
ggsave("Total.Hippocampus.Age.plot.pdf", plot = Total.Hippocampus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Amygdala.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Amygdala, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3600, label.x = 60, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME amygdala") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Amygdala.Age.plot
ggsave("Total.Amygdala.Age.plot.pdf", plot = Total.Amygdala.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Corpus.callosum.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Corpus.callosum, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4100, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME corpus callosum") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Corpus.callosum.Age.plot
ggsave("Total.Corpus.callosum.Age.plot.pdf", plot = Total.Corpus.callosum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Claustrum.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Claustrum, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2100, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME claustrum") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Claustrum.Age.plot
ggsave("Total.Claustrum.Age.plot.pdf", plot = Total.Claustrum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Putamen.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Putamen, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 12300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME putamen") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Putamen.Age.plot
ggsave("Total.Putamen.Age.plot.pdf", plot = Total.Putamen.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Caudate.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Caudate, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 10200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME caudate") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Caudate.Age.plot
ggsave("Total.Caudate.Age.plot.pdf", plot = Total.Caudate.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Globus.pallidum.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Globus.pallidum, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME globus pallidum") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Globus.pallidum.Age.plot
ggsave("Total.Globus.pallidum.Age.plot.pdf", plot = Total.Globus.pallidum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Internal.capsule.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Internal.capsule, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 12200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME internal capsule") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Internal.capsule.Age.plot
ggsave("Total.Internal.capsule.Age.plot.pdf", plot = Total.Internal.capsule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Innominate.substance.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Innominate.substance, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3180, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME innominate substance") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Innominate.substance.Age.plot
ggsave("Total.Innominate.substance.Age.plot.pdf", plot = Total.Innominate.substance.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Hypothalamus.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Hypothalamus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 9200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME hypothalamus") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Hypothalamus.Age.plot
ggsave("Total.Hypothalamus.Age.plot.pdf", plot = Total.Hypothalamus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Thalamus.Age.plot <-  ggplot(Prosencephalon.absolute, aes(y=Thalamus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 17000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME thalamus") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Thalamus.Age.plot
ggsave("Total.Thalamus.Age.plot.pdf", plot = Total.Thalamus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```

## Relative Volumes

```{r The Prosencephalon: Relative Volumes}

#Prosencephalon.absolute <- select(Prosencephalon.absolute, - c(Gender))
Prosencephalon.relative <- (100 * (Prosencephalon.absolute/All.Volumes$`Total encephalic volume (without ventricles)`))
Prosencephalon.relative1 <- Prosencephalon.relative[,-c(41:51)]
Prosencephalon.relative2 <- Prosencephalon.relative[, c(41:51)]

Table.Prosencephalon.relative <- CreateTableOne(
  vars = c("Frontal.pole",
  "F1",
  "F2",
  "F3.orbital", 
  "F3.triangular", 
  "F3.opercular", 
  "Anterior.orbital", 
  "Medial.orbital", 
  "Lateral.orbital", 
  "Posterior.orbital",
  "Rectus", 
  "Rostral", 
  "Precentral", 
  "Postcentral", 
  "Paracentral.lobule", 
  "Subcentral", 
  "SPL", 
  "SMG", 
  "ANG", 
  "Precuneus",
  "Cuneus", 
  "O1",
  "O2", 
  "O3", 
  "Occipital.pole", 
  "Lingual", 
  "Fusiform", 
  "Temporal.pole", 
  "T1",
  "T2", 
  "T3", 
  "Planum.temporale", 
  "Planum.polare", 
  "Short.insular", 
  "Long.insular", 
  "SCA", 
  "Cingulate.anterior", 
  "Cingulate.middle", 
  "Cingulate.posterior", 
  "PHG", 
  "Hippocampus", 
  "Amygdala", 
  "Corpus.callosum", 
  "Claustrum", 
  "Putamen", 
  "Caudate", 
  "Globus.pallidum", 
  "Internal.capsule",
  "Innominate.substance", 
  "Hypothalamus",
  "Thalamus"),
  data = Prosencephalon.relative)

Prosencephalon.relative$Gender <- All.Volumes$Gender
Table.Prosencephalon.relative.stratified.gender <- CreateTableOne(
  vars = c("Frontal.pole",
  "F1",
  "F2",
  "F3.orbital", 
  "F3.triangular", 
  "F3.opercular", 
  "Anterior.orbital", 
  "Medial.orbital", 
  "Lateral.orbital", 
  "Posterior.orbital",
  "Rectus", 
  "Rostral", 
  "Precentral", 
  "Postcentral", 
  "Paracentral.lobule", 
  "Subcentral", 
  "SPL", 
  "SMG", 
  "ANG", 
  "Precuneus",
  "Cuneus", 
  "O1",
  "O2", 
  "O3", 
  "Occipital.pole", 
  "Lingual", 
  "Fusiform", 
  "Temporal.pole", 
  "T1",
  "T2", 
  "T3", 
  "Planum.temporale", 
  "Planum.polare", 
  "Short.insular", 
  "Long.insular", 
  "SCA", 
  "Cingulate.anterior", 
  "Cingulate.middle", 
  "Cingulate.posterior", 
  "PHG", 
  "Hippocampus", 
  "Amygdala", 
  "Corpus.callosum", 
  "Claustrum", 
  "Putamen", 
  "Caudate", 
  "Globus.pallidum", 
  "Internal.capsule",
  "Innominate.substance", 
  "Hypothalamus",
  "Thalamus"),
  strata = c("Gender"),
  data = Prosencephalon.relative)

Table.Prosencephalon.relative <- print(Table.Prosencephalon.relative, contDigits = 10)
Table.Prosencephalon.relative.stratified.gender <- print(Table.Prosencephalon.relative.stratified.gender, contDigits = 10)

```

```{r The Prosencephalon: Relative Volumes relative standard deviations}

Table.Prosencephalon.relative.RSD <- as.data.frame(Table.Prosencephalon.relative)
Table.Prosencephalon.relative.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Prosencephalon.relative.RSD[-1,]),
                                                                       ' (',fixed=TRUE)))
Table.Prosencephalon.relative.RSD <- data.frame(cbind(str_replace_all(Table.Prosencephalon.relative.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.Prosencephalon.relative.RSD$X2, "[)]", "")))
Table.Prosencephalon.relative.RSD$X1 <- as.character(Table.Prosencephalon.relative.RSD$X1)
Table.Prosencephalon.relative.RSD$X2 <- as.character(Table.Prosencephalon.relative.RSD$X2)
Table.Prosencephalon.relative.RSD <- as.data.frame(sapply(Table.Prosencephalon.relative.RSD, as.numeric))
Table.Prosencephalon.relative.RSD <- as.data.frame(Table.Prosencephalon.relative.RSD$X2/Table.Prosencephalon.relative.RSD$X1)
Table.Prosencephalon.relative.RSD <- round(Table.Prosencephalon.relative.RSD * 100, 1)


Table.Prosencephalon.relative.stratified.gender.RSD <- as.data.frame(Table.Prosencephalon.relative.stratified.gender)
Table.Prosencephalon.relative.stratified.gender.RSD <- select(Table.Prosencephalon.relative.stratified.gender.RSD, - c(p, test))

Table.Prosencephalon.relative.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Prosencephalon.relative.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.Prosencephalon.relative.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Prosencephalon.relative.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.Prosencephalon.relative.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Prosencephalon.relative.stratified.gender.RSD.female$X1 <- as.character(Table.Prosencephalon.relative.stratified.gender.RSD.female$X1)
Table.Prosencephalon.relative.stratified.gender.RSD.female$X2 <- as.character(Table.Prosencephalon.relative.stratified.gender.RSD.female$X2)
Table.Prosencephalon.relative.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Prosencephalon.relative.stratified.gender.RSD.female, as.numeric))
Table.Prosencephalon.relative.stratified.gender.RSD.female <- as.data.frame(Table.Prosencephalon.relative.stratified.gender.RSD.female$X2/Table.Prosencephalon.relative.stratified.gender.RSD.female$X1)
Table.Prosencephalon.relative.stratified.gender.RSD.female <- round(Table.Prosencephalon.relative.stratified.gender.RSD.female * 100, 1)

Table.Prosencephalon.relative.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Prosencephalon.relative.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.Prosencephalon.relative.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Prosencephalon.relative.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.Prosencephalon.relative.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Prosencephalon.relative.stratified.gender.RSD.male$X1 <- as.character(Table.Prosencephalon.relative.stratified.gender.RSD.male$X1)
Table.Prosencephalon.relative.stratified.gender.RSD.male$X2 <- as.character(Table.Prosencephalon.relative.stratified.gender.RSD.male$X2)
Table.Prosencephalon.relative.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Prosencephalon.relative.stratified.gender.RSD.male, as.numeric))
Table.Prosencephalon.relative.stratified.gender.RSD.male <- as.data.frame(Table.Prosencephalon.relative.stratified.gender.RSD.male$X2/Table.Prosencephalon.relative.stratified.gender.RSD.male$X1)
Table.Prosencephalon.relative.stratified.gender.RSD.male <- round(Table.Prosencephalon.relative.stratified.gender.RSD.male * 100, 1)

```

```{r The Prosencephalon: Relative Volumes Tables}

kable(Table.Prosencephalon.relative)
kable(Table.Prosencephalon.relative.RSD)
kable(Table.Prosencephalon.relative.stratified.gender)
kable(Table.Prosencephalon.relative.stratified.gender.RSD.female)
kable(Table.Prosencephalon.relative.stratified.gender.RSD.male)

```

```{r The Prosencephalon_Gyri: Relative Volumes and Gender Plot}

Prosencephalon.relative1 <- select(Prosencephalon.relative1, - c(Age))

names.anatomical.structures.temporary <- c("Frontal.pole",
  "F1",
  "F2",
  "F3.orbital", 
  "F3.triangular", 
  "F3.opercular", 
  "Anterior.orbital", 
  "Medial.orbital", 
  "Lateral.orbital", 
  "Posterior.orbital",
  "Rectus", 
  "Rostral", 
  "Precentral", 
  "Postcentral", 
  "Paracentral.lobule", 
  "Subcentral", 
  "SPL", 
  "SMG", 
  "ANG", 
  "Precuneus",
  "Cuneus", 
  "O1",
  "O2", 
  "O3", 
  "Occipital.pole", 
  "Lingual", 
  "Fusiform", 
  "Temporal.pole", 
  "T1",
  "T2", 
  "T3", 
  "Planum.temporale", 
  "Planum.polare", 
  "Short.insular", 
  "Long.insular", 
  "SCA", 
  "Cingulate.anterior", 
  "Cingulate.middle", 
  "Cingulate.posterior", 
  "PHG")

names.anatomical.structures.definitive <- c("Frontal pole",
  "F1",
  "F2",
  "F3 orbital", 
  "F3 triangular", 
  "F3 opercular", 
  "Anterior orbital", 
  "Medial orbital", 
  "Lateral orbital", 
  "Posterior orbital",
  "Rectus", 
  "Rostral", 
  "Precentral", 
  "Postcentral", 
  "Paracentral lobule", 
  "Subcentral", 
  "SPL", 
  "SMG", 
  "ANG", 
  "Precuneus",
  "Cuneus", 
  "O1",
  "O2", 
  "O3", 
  "Occipital pole", 
  "Lingual", 
  "Fusiform", 
  "Temporal pole", 
  "T1",
  "T2", 
  "T3", 
  "Planum temporale", 
  "Planum polare", 
  "Short insular", 
  "Long insular", 
  "SCA", 
  "Cingulate anterior", 
  "Cingulate middle", 
  "Cingulate posterior", 
  "PHG")

Prosencephalon.relative.plotdata1 <- gather(Prosencephalon.relative1, "anatomical.structure", "relative.volume")
Prosencephalon.relative.plotdata1$Gender <- All.Volumes$Gender
Prosencephalon.relative.plotdata1$Age <- All.Volumes$`Age (years)`

Prosencephalon.relative.plotdata1$Gender <- factor(Prosencephalon.relative.plotdata1$Gender, levels = c("f", "m"), c("f", "m"))
Prosencephalon.relative.plotdata1$anatomical.structure <- factor(Prosencephalon.relative.plotdata1$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Prosencephalon.relative.plot1 <-  ggplot(Prosencephalon.relative.plotdata1, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Relative volume (in %)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("CEREBRAL GYRI") +
  theme(plot.title = element_text(hjust = 0.5))

Prosencephalon.relative.plot1
ggsave("Prosencephalon.relative.plot1.pdf", plot = Prosencephalon.relative.plot1, width = 14, height = 12, units = "in", dpi = 600)

```

```{r The Prosencephalon_Central Structures: Relative Volumes and Gender Plot}

names.anatomical.structures.temporary <- c(
  "Corpus.callosum", 
  "Claustrum", 
  "Putamen", 
  "Caudate", 
  "Globus.pallidum", 
  "Internal.capsule",
  "Innominate.substance", 
  "Hypothalamus",
  "Thalamus", 
  "Hippocampus", 
  "Amygdala")

names.anatomical.structures.definitive <- c(
  "Corpus callosum", 
  "Claustrum", 
  "Putamen", 
  "Caudate", 
  "Globus pallidum", 
  "Internal capsule",
  "Innominate substance", 
  "Hypothalamus",
  "Thalamus",
  "Hippocampus", 
  "Amygdala")

Prosencephalon.relative.plotdata2 <- gather(Prosencephalon.relative2, "anatomical.structure", "relative.volume")
Prosencephalon.relative.plotdata2$Gender <- All.Volumes$Gender
Prosencephalon.relative.plotdata2$Age <- All.Volumes$`Age (years)`

Prosencephalon.relative.plotdata2$Gender <- factor(Prosencephalon.relative.plotdata2$Gender, levels = c("f", "m"), c("f", "m"))
Prosencephalon.relative.plotdata2$anatomical.structure <- factor(Prosencephalon.relative.plotdata2$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Prosencephalon.relative.plot2 <-  ggplot(Prosencephalon.relative.plotdata2, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Relative volume (in %)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("CENTRAL PROSENCEPHALON") +
  theme(plot.title = element_text(hjust = 0.5))

Prosencephalon.relative.plot2
ggsave("Prosencephalon.relative.plot2.pdf", plot = Prosencephalon.relative.plot2, width = 10, height = 6, units = "in", dpi = 600)

```

```{r The Prosencephalon: Relative Volumes and Age Plot}

#Prosencephalon.relative$Gender <- All.Volumes$Gender
Prosencephalon.relative$Age <- All.Volumes$`Age (years)`

Relative.Frontal.pole.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Frontal.pole, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.57, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME frontal pole") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Frontal.pole.Age.plot
ggsave("Relative.Frontal.pole.Age.plot.pdf", plot = Relative.Frontal.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.F1.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=F1, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 6.6, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME F1") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.F1.Age.plot
ggsave("Relative.F1.Age.plot.pdf", plot = Relative.F1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.F2.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=F2, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 5.95, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME F2") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.F2.Age.plot
ggsave("Relative.F2.Age.plot.pdf", plot = Relative.F2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.F3.orbital.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=F3.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.41, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME F3 orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.F3.orbital.Age.plot
ggsave("Relative.F3.orbital.Age.plot.pdf", plot = Relative.F3.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.F3.triangular.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=F3.triangular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.22, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME F3 triangular") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.F3.triangular.Age.plot
ggsave("Relative.F3.triangular.Age.plot.pdf", plot = Relative.F3.triangular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.F3.opercular.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=F3.opercular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.65, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME F3 opercular") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.F3.opercular.Age.plot
ggsave("Relative.F3.opercular.Age.plot.pdf", plot = Relative.F3.opercular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Anterior.orbital.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Anterior.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.385, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME anterior orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Anterior.orbital.Age.plot
ggsave("Relative.Anterior.orbital.Age.plot.pdf", plot = Relative.Anterior.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Medial.orbital.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Medial.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.87, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME medial orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Medial.orbital.Age.plot
ggsave("Relative.Medial.orbital.Age.plot.pdf", plot = Relative.Medial.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Lateral.orbital.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Lateral.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.67, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME lateral orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Lateral.orbital.Age.plot
ggsave("Relative.Lateral.orbital.Age.plot.pdf", plot = Relative.Lateral.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Posterior.orbital.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Posterior.orbital, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.82, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME posterior orbital") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Posterior.orbital.Age.plot
ggsave("Relative.Posterior.orbital.Age.plot.pdf", plot = Relative.Posterior.orbital.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Rectus.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Rectus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.8, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME rectus") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Rectus.Age.plot
ggsave("Relative.Rectus.Age.plot.pdf", plot = Relative.Rectus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Rostral.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Rostral, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.41, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME rostral") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Rostral.Age.plot
ggsave("Relative.Rostral.Age.plot.pdf", plot = Relative.Rostral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Precentral.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Precentral, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4.6, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME precentral") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Precentral.Age.plot
ggsave("Relative.Precentral.Age.plot.pdf", plot = Relative.Precentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Postcentral.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Postcentral, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.45, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME postcentral") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Postcentral.Age.plot
ggsave("Relative.Postcentral.Age.plot.pdf", plot = Relative.Postcentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Paracentral.lobule.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Paracentral.lobule, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.62, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME paracentral lobule") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Paracentral.lobule.Age.plot
ggsave("Relative.Paracentral.lobule.Age.plot.pdf", plot = Relative.Paracentral.lobule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Subcentral.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Subcentral, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.66, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME subcentral") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Subcentral.Age.plot
ggsave("Relative.Subcentral.Age.plot.pdf", plot = Relative.Subcentral.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.SPL.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=SPL, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3.5, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME SPL") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.SPL.Age.plot
ggsave("Relative.SPL.Age.plot.pdf", plot = Relative.SPL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.SMG.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=SMG, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3.3, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME supramarginal") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.SMG.Age.plot
ggsave("Relative.SMG.Age.plot.pdf", plot = Relative.SMG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.ANG.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=ANG, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3.4, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME angular") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.ANG.Age.plot
ggsave("Relative.ANG.Age.plot.pdf", plot = Relative.ANG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Precuneus.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Precuneus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3.05, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME precuneus") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Precuneus.Age.plot
ggsave("Relative.Precuneus.Age.plot.pdf", plot = Relative.Precuneus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Cuneus.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Cuneus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.07, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME cuneus") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Cuneus.Age.plot
ggsave("Relative.Cuneus.Age.plot.pdf", plot = Relative.Cuneus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.O1.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=O1, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.97, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME O1") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.O1.Age.plot
ggsave("Relative.O1.Age.plot.pdf", plot = Relative.O1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.O2.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=O2, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.62, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME O2") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.O2.Age.plot
ggsave("Relative.O2.Age.plot.pdf", plot = Relative.O2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.O3.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=O3, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.02, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME O3") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.O3.Age.plot
ggsave("Relative.O3.Age.plot.pdf", plot = Relative.O3.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Occipital.pole.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Occipital.pole, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.17, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME occipital pole") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Occipital.pole.Age.plot
ggsave("Relative.Occipital.pole.Age.plot.pdf", plot = Relative.Occipital.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Lingual.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Lingual, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.3, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME lingual") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Lingual.Age.plot
ggsave("Relative.Lingual.Age.plot.pdf", plot = Relative.Lingual.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Fusiform.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Fusiform, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.28, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME fusiform") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Fusiform.Age.plot
ggsave("Relative.Fusiform.Age.plot.pdf", plot = Relative.Fusiform.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Temporal.pole.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Temporal.pole, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.7, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME temporal pole") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Temporal.pole.Age.plot
ggsave("Relative.Temporal.pole.Age.plot.pdf", plot = Relative.Temporal.pole.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.T1.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=T1, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.75, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME T1") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.T1.Age.plot
ggsave("Relative.T1.Age.plot.pdf", plot = Relative.T1.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.T2.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=T2, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.43, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME T2") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.T2.Age.plot
ggsave("Relative.T2.Age.plot.pdf", plot = Relative.T2.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.T3.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=T3, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.51, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME T3") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.T3.Age.plot
ggsave("Relative.T3.Age.plot.pdf", plot = Relative.T3.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Planum.temporale.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Planum.temporale, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.335, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME planum temporale") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Planum.temporale.Age.plot
ggsave("Relative.Planum.temporale.Age.plot.pdf", plot = Relative.Planum.temporale.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Planum.polare.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Planum.polare, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.18, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME planum polare") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Planum.polare.Age.plot
ggsave("Relative.Planum.polare.Age.plot.pdf", plot = Relative.Planum.polare.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Short.insular.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Short.insular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.85, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME short insular") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Short.insular.Age.plot
ggsave("Relative.Short.insular.Age.plot.pdf", plot = Relative.Short.insular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Long.insular.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Long.insular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.97, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME long insular") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Long.insular.Age.plot
ggsave("Relative.Long.insular.Age.plot.pdf", plot = Relative.Long.insular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.SCA.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=SCA, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.33, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME subcallosal area") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.SCA.Age.plot
ggsave("Relative.SCA.Age.plot.pdf", plot = Relative.SCA.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Cingulate.anterior.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Cingulate.anterior, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.57, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME cingulate anterior") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Cingulate.anterior.Age.plot
ggsave("Relative.Cingulate.anterior.Age.plot.pdf", plot = Relative.Cingulate.anterior.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Cingulate.middle.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Cingulate.middle, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.75, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME cingulate middle") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Cingulate.middle.Age.plot
ggsave("Relative.Cingulate.middle.Age.plot.pdf", plot = Relative.Cingulate.middle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Cingulate.posterior.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Cingulate.posterior, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.05, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME cingulate posterior") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Cingulate.posterior.Age.plot
ggsave("Relative.Cingulate.posterior.Age.plot.pdf", plot = Relative.Cingulate.posterior.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.PHG.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=PHG, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.15, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME PHG") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.PHG.Age.plot
ggsave("Relative.PHG.Age.plot.pdf", plot = Relative.PHG.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Hippocampus.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Hippocampus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.77, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME hippocampus") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Hippocampus.Age.plot
ggsave("Relative.Hippocampus.Age.plot.pdf", plot = Relative.Hippocampus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Amygdala.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Amygdala, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.335, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME amygdala") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Amygdala.Age.plot
ggsave("Relative.Amygdala.Age.plot.pdf", plot = Relative.Amygdala.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Corpus.callosum.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Corpus.callosum, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.36, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME corpus callosum") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Corpus.callosum.Age.plot
ggsave("Relative.Corpus.callosum.Age.plot.pdf", plot = Relative.Corpus.callosum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Claustrum.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Claustrum, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.18, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME claustrum") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Claustrum.Age.plot
ggsave("Relative.Claustrum.Age.plot.pdf", plot = Relative.Claustrum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Putamen.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Putamen, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.15, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME putamen") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Putamen.Age.plot
ggsave("Relative.Putamen.Age.plot.pdf", plot = Relative.Putamen.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Caudate.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Caudate, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.87, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME caudate") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Caudate.Age.plot
ggsave("Relative.Caudate.Age.plot.pdf", plot = Relative.Caudate.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Globus.pallidum.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Globus.pallidum, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.335, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME globus pallidum") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Globus.pallidum.Age.plot
ggsave("Relative.Globus.pallidum.Age.plot.pdf", plot = Relative.Globus.pallidum.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Internal.capsule.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Internal.capsule, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.125, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME internal capsule") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Internal.capsule.Age.plot
ggsave("Relative.Internal.capsule.Age.plot.pdf", plot = Relative.Internal.capsule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Innominate.substance.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Innominate.substance, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.31, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME innominate substance") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Innominate.substance.Age.plot
ggsave("Relative.Innominate.substance.Age.plot.pdf", plot = Relative.Innominate.substance.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Hypothalamus.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Hypothalamus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.78, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME hypothalamus") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Hypothalamus.Age.plot
ggsave("Relative.Hypothalamus.Age.plot.pdf", plot = Relative.Hypothalamus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Thalamus.Age.plot <-  ggplot(Prosencephalon.relative, aes(y=Thalamus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.47, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME thalamus") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Thalamus.Age.plot
ggsave("Relative.Thalamus.Age.plot.pdf", plot = Relative.Thalamus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```

# Brainstem and Cerebellum

## Absolute Volumes

```{r Brainstem and Cerebellum: Absolute Volumes}

Mesencephalon <- All.Volumes$Mesencephalon
Pons <- All.Volumes$Pons
Medulla.oblongata <- All.Volumes$`Medulla oblongata`
  
Cerebellar.peduncles <- All.Volumes$`Total volume cerebellar peduncles`

Vermis <- All.Volumes$`Total volume vermis`
Hemisphere <- All.Volumes$`Total volume cerebellar hemisphere`

Anterior.lobe <- (All.Volumes$Central+All.Volumes$Culmen+All.Volumes$`Total volume ala lobuli centralis`+All.Volumes$`Total volume AQL`)
Medial.lobe <- (All.Volumes$Declive+All.Volumes$Folium+All.Volumes$`Total volume PQL`+All.Volumes$`Total volume SSL`)
Posterior.lobe <- (All.Volumes$Tuber+All.Volumes$Pyramid+All.Volumes$Uvula+All.Volumes$`Total volume inferior semilunar /gracile`+All.Volumes$`Total volume biventer`+All.Volumes$`Total volume tonsilla`)
Flocculonodular.lobe <- (All.Volumes$Nodule+All.Volumes$`Total volume flocculus`) 

Central <- All.Volumes$Central
Culmen <- All.Volumes$Culmen
Declive <- All.Volumes$Declive
Folium <- All.Volumes$Folium
Tuber <- All.Volumes$Tuber
Pyramid <- All.Volumes$Pyramid
Uvula <- All.Volumes$Uvula
Nodule <- All.Volumes$Nodule

Ala.lobuli.centralis <- All.Volumes$`Total volume ala lobuli centralis`
AQL <- All.Volumes$`Total volume AQL`
PQL <- All.Volumes$`Total volume PQL`
SSL <-  All.Volumes$`Total volume SSL`
ISL.gracile <- All.Volumes$`Total volume inferior semilunar /gracile`
Biventer <- All.Volumes$`Total volume biventer`
Tonsilla <- All.Volumes$`Total volume tonsilla`
Flocculus <- All.Volumes$`Total volume flocculus`

Brainstem.Cerebellum.absolute <- as.data.frame(cbind(
Mesencephalon,
Pons,
Medulla.oblongata, 
Cerebellar.peduncles, 
Vermis, 
Hemisphere, 
Anterior.lobe, 
Medial.lobe, 
Posterior.lobe, 
Flocculonodular.lobe, 
Central, 
Culmen, 
Declive, 
Folium, 
Tuber,
Pyramid, 
Uvula, 
Nodule,
Ala.lobuli.centralis,
AQL,
PQL,
SSL,
ISL.gracile, 
Biventer,
Tonsilla, 
Flocculus 
))

Brainstem.Cerebellum.absolute$Gender <- All.Volumes$Gender

Table.Brainstem.Cerebellum.absolute <- CreateTableOne(
  vars = c("Mesencephalon",
"Pons",
"Medulla.oblongata", 
"Cerebellar.peduncles", 
"Vermis", 
"Hemisphere", 
"Anterior.lobe", 
"Medial.lobe", 
"Posterior.lobe", 
"Flocculonodular.lobe", 
"Central", 
"Culmen", 
"Declive", 
"Folium", 
"Tuber",
"Pyramid", 
"Uvula", 
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile", 
"Biventer",
"Tonsilla", 
"Flocculus"),
  data = Brainstem.Cerebellum.absolute)

Table.Brainstem.Cerebellum.absolute.stratified.gender <- CreateTableOne(
  vars = c("Mesencephalon",
"Pons",
"Medulla.oblongata", 
"Cerebellar.peduncles", 
"Vermis", 
"Hemisphere", 
"Anterior.lobe", 
"Medial.lobe", 
"Posterior.lobe", 
"Flocculonodular.lobe", 
"Central", 
"Culmen", 
"Declive", 
"Folium", 
"Tuber",
"Pyramid", 
"Uvula", 
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile", 
"Biventer",
"Tonsilla", 
"Flocculus"), 
strata = c("Gender"),
data = Brainstem.Cerebellum.absolute)

Table.Brainstem.Cerebellum.absolute <- print(Table.Brainstem.Cerebellum.absolute, contDigits = 10)
Table.Brainstem.Cerebellum.absolute.stratified.gender <- print(Table.Brainstem.Cerebellum.absolute.stratified.gender, contDigits = 10)

write.csv(Table.Brainstem.Cerebellum.absolute, "Table.Brainstem.Cerebellum.absolute.csv")
write.csv(Table.Brainstem.Cerebellum.absolute.stratified.gender, "Table.Brainstem.Cerebellum.absolute.stratified.gender.csv")

```

```{r Brainstem and Cerebellum: Absolute Volumes relative standard deviations}

Table.Brainstem.Cerebellum.absolute.RSD <- as.data.frame(Table.Brainstem.Cerebellum.absolute)
Table.Brainstem.Cerebellum.absolute.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Brainstem.Cerebellum.absolute.RSD[-1,]),' (',fixed=TRUE)))
Table.Brainstem.Cerebellum.absolute.RSD <- data.frame(cbind(str_replace_all(Table.Brainstem.Cerebellum.absolute.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.Brainstem.Cerebellum.absolute.RSD$X2, "[)]", "")))
Table.Brainstem.Cerebellum.absolute.RSD$X1 <- as.character(Table.Brainstem.Cerebellum.absolute.RSD$X1)
Table.Brainstem.Cerebellum.absolute.RSD$X2 <- as.character(Table.Brainstem.Cerebellum.absolute.RSD$X2)
Table.Brainstem.Cerebellum.absolute.RSD <- as.data.frame(sapply(Table.Brainstem.Cerebellum.absolute.RSD, as.numeric))
Table.Brainstem.Cerebellum.absolute.RSD <- as.data.frame(Table.Brainstem.Cerebellum.absolute.RSD$X2/Table.Brainstem.Cerebellum.absolute.RSD$X1)
Table.Brainstem.Cerebellum.absolute.RSD <- round(Table.Brainstem.Cerebellum.absolute.RSD * 100, 1)


Table.Prosencephalon.absolute.stratified.gender.RSD <- as.data.frame(Table.Brainstem.Cerebellum.absolute.stratified.gender)
Table.Prosencephalon.absolute.stratified.gender.RSD <- select(Table.Prosencephalon.absolute.stratified.gender.RSD, - c(p, test))

Table.Prosencephalon.absolute.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Prosencephalon.absolute.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2)
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Prosencephalon.absolute.stratified.gender.RSD.female, as.numeric))
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender.RSD.female$X2/Table.Prosencephalon.absolute.stratified.gender.RSD.female$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.female <- round(Table.Prosencephalon.absolute.stratified.gender.RSD.female * 100, 1)

Table.Prosencephalon.absolute.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Prosencephalon.absolute.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2 <- as.character(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Prosencephalon.absolute.stratified.gender.RSD.male, as.numeric))
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- as.data.frame(Table.Prosencephalon.absolute.stratified.gender.RSD.male$X2/Table.Prosencephalon.absolute.stratified.gender.RSD.male$X1)
Table.Prosencephalon.absolute.stratified.gender.RSD.male <- round(Table.Prosencephalon.absolute.stratified.gender.RSD.male * 100, 1)

```

```{r Brainstem and Cerebellum: Absolute Volumes Tables}

kable(Table.Brainstem.Cerebellum.absolute)
kable(Table.Brainstem.Cerebellum.absolute.RSD)
kable(Table.Brainstem.Cerebellum.absolute.stratified.gender)
kable(Table.Prosencephalon.absolute.stratified.gender.RSD.female)
kable(Table.Prosencephalon.absolute.stratified.gender.RSD.male)

```

```{r Brainstem and Cerebellum: Absolute Volumes and Gender Plot 1}

Brainstem.Cerebellum.absolute <- select(Brainstem.Cerebellum.absolute, - c(Gender))
Brainstem.Cerebellum.absolute1 <- Brainstem.Cerebellum.absolute[,c(1:10)]
Brainstem.Cerebellum.absolute2 <- Brainstem.Cerebellum.absolute[,-c(1:10)]

names.anatomical.structures.temporary <- c("Mesencephalon",
"Pons",
"Medulla.oblongata", 
"Cerebellar.peduncles", 
"Vermis", 
"Hemisphere", 
"Anterior.lobe", 
"Medial.lobe", 
"Posterior.lobe", 
"Flocculonodular.lobe")

names.anatomical.structures.definitive <- c("Mesencephalon",
"Pons",
"Medulla oblongata", 
"Cerebellar peduncles", 
"Vermis", 
"Hemisphere", 
"Anterior lobe", 
"Medial lobe", 
"Posterior lobe", 
"Flocculonodular lobe")

Brainstem.Cerebellum.absolute.plotdata1 <- gather(Brainstem.Cerebellum.absolute1, "anatomical.structure", "relative.volume")
Brainstem.Cerebellum.absolute.plotdata1$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.absolute.plotdata1$Age <- All.Volumes$`Age (years)`

Brainstem.Cerebellum.absolute.plotdata1$Gender <- factor(Brainstem.Cerebellum.absolute.plotdata1$Gender, levels = c("f", "m"), c("f", "m"))
Brainstem.Cerebellum.absolute.plotdata1$anatomical.structure <- factor(Brainstem.Cerebellum.absolute.plotdata1$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Brainstem.Cerebellum.absolute.plot1 <-  ggplot(Brainstem.Cerebellum.absolute.plotdata1, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Absolute volume (in mm3)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("BRAINSTEM & CEREBELLUM") +
  theme(plot.title = element_text(hjust = 0.5))

Brainstem.Cerebellum.absolute.plot1
ggsave("Brainstem.Cerebellum.absolute.plot1.pdf", plot = Brainstem.Cerebellum.absolute.plot1, width = 12, height = 6, units = "in", dpi = 600)

```

```{r Brainstem and Cerebellum: Absolute Volumes and Gender Plot 2}

names.anatomical.structures.temporary <- c(
"Central", 
"Culmen", 
"Declive", 
"Folium", 
"Tuber",
"Pyramid", 
"Uvula", 
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile", 
"Biventer",
"Tonsilla", 
"Flocculus")

names.anatomical.structures.definitive <- c(
"Central", 
"Culmen", 
"Declive", 
"Folium", 
"Tuber",
"Pyramid", 
"Uvula", 
"Nodule",
"Ala lobuli centralis",
"AQL",
"PQL",
"SSL",
"ISL/gracile", 
"Biventer",
"Tonsilla", 
"Flocculus")

Brainstem.Cerebellum.absolute.plotdata2 <- gather(Brainstem.Cerebellum.absolute2, "anatomical.structure", "relative.volume")
Brainstem.Cerebellum.absolute.plotdata2$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.absolute.plotdata2$Age <- All.Volumes$`Age (years)`

Brainstem.Cerebellum.absolute.plotdata2$Gender <- factor(Brainstem.Cerebellum.absolute.plotdata2$Gender, levels = c("f", "m"), c("f", "m"))
Brainstem.Cerebellum.absolute.plotdata2$anatomical.structure <- factor(Brainstem.Cerebellum.absolute.plotdata2$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Brainstem.Cerebellum.absolute.plot2 <-  ggplot(Brainstem.Cerebellum.absolute.plotdata2, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Absolute volume (in mm3)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("CEREBELLAR LOBES & LOBULES") +
  theme(plot.title = element_text(hjust = 0.5))

Brainstem.Cerebellum.absolute.plot2
ggsave("Brainstem.Cerebellum.absolute.plot2.pdf", plot = Brainstem.Cerebellum.absolute.plot2, width = 12, height = 6, units = "in", dpi = 600)

```

```{r Brainstem and Cerebellum: Absolute Volumes and Age Plot}

Brainstem.Cerebellum.absolute$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.absolute$Age <- All.Volumes$`Age (years)`

Total.Mesencephalon.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Mesencephalon, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 11700, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME mesencephalon") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Mesencephalon.Age.plot
ggsave("Total.Mesencephalon.Age.plot.pdf", plot = Total.Mesencephalon.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Pons.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Pons, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 20500, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME pons") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Pons.Age.plot
ggsave("Total.Pons.Age.plot.pdf", plot = Total.Pons.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Medulla.oblongata.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Medulla.oblongata, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3300, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME medulla oblongata") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Medulla.oblongata.Age.plot
ggsave("Total.Medulla.oblongata.Age.plot.pdf", plot = Total.Medulla.oblongata.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Cerebellar.peduncles.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Cerebellar.peduncles, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 8700, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME cerebellar peduncles") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Cerebellar.peduncles.Age.plot
ggsave("Total.Cerebellar.peduncles.Age.plot.pdf", plot = Total.Cerebellar.peduncles.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Vermis.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Vermis, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 7200, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME vermis") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Vermis.Age.plot
ggsave("Total.Vermis.Age.plot.pdf", plot = Total.Vermis.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Hemisphere.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Hemisphere, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 123000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME hemisphere") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Hemisphere.Age.plot
ggsave("Total.Hemisphere.Age.plot.pdf", plot = Total.Hemisphere.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Anterior.lobe.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Anterior.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 36000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME anterior lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Anterior.lobe.Age.plot
ggsave("Total.Anterior.lobe.Age.plot.pdf", plot = Total.Anterior.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Medial.lobe.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Medial.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 31000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME medial lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Medial.lobe.Age.plot
ggsave("Total.Medial.lobe.Age.plot.pdf", plot = Total.Medial.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Posterior.lobe.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Posterior.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 66000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME posterior lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Posterior.lobe.Age.plot
ggsave("Total.Posterior.lobe.Age.plot.pdf", plot = Total.Posterior.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Flocculonodular.lobe.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Flocculonodular.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1250, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME flocculonodular lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Flocculonodular.lobe.Age.plot
ggsave("Total.Flocculonodular.lobe.Age.plot.pdf", plot = Total.Flocculonodular.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Central.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Central, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 770, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME central") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Central.Age.plot
ggsave("Total.Central.Age.plot.pdf", plot = Total.Central.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Culmen.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Culmen, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2600, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME CULMEN") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Culmen.Age.plot
ggsave("Total.Culmen.Age.plot.pdf", plot = Total.Culmen.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Declive.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Declive, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1250, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME DECLIVE") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Declive.Age.plot
ggsave("Total.Declive.Age.plot.pdf", plot = Total.Declive.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Folium.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Folium, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 630, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME FOLIUM") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Folium.Age.plot
ggsave("Total.Folium.Age.plot.pdf", plot = Total.Folium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Tuber.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Tuber, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 570, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME TUBER") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Tuber.Age.plot
ggsave("Total.Tuber.Age.plot.pdf", plot = Total.Tuber.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Pyramid.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Pyramid, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 700, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME PYRAMID") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Pyramid.Age.plot
ggsave("Total.Pyramid.Age.plot.pdf", plot = Total.Pyramid.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Uvula.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Uvula, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1030, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME UVULA") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Uvula.Age.plot
ggsave("Total.Uvula.Age.plot.pdf", plot = Total.Uvula.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Nodule.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Nodule, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 230, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME NODULE") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Nodule.Age.plot
ggsave("Total.Nodule.Age.plot.pdf", plot = Total.Nodule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Ala.lobuli.centralis.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Ala.lobuli.centralis, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 16000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME ALA LOBULI CENTRALIS") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Ala.lobuli.centralis.Age.plot
ggsave("Total.Ala.lobuli.centralis.Age.plot.pdf", plot = Total.Ala.lobuli.centralis.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.AQL.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=AQL, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 21000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME AQL") +
  theme(plot.title = element_text(hjust = 0.5))
Total.AQL.Age.plot
ggsave("Total.AQL.Age.plot.pdf", plot = Total.AQL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.PQL.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=PQL, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 15500, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME PQL") +
  theme(plot.title = element_text(hjust = 0.5))
Total.PQL.Age.plot
ggsave("Total.PQL.Age.plot.pdf", plot = Total.PQL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.SSL.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=SSL, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 15500, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME SSL") +
  theme(plot.title = element_text(hjust = 0.5))
Total.SSL.Age.plot
ggsave("Total.SSL.Age.plot.pdf", plot = Total.SSL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.ISL.gracile.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=ISL.gracile, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 42000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME ISL/gracile") +
  theme(plot.title = element_text(hjust = 0.5))
Total.ISL.gracile.Age.plot
ggsave("Total.ISL.gracile.Age.plot.pdf", plot = Total.ISL.gracile.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Biventer.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Biventer, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 21000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME biventer") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Biventer.Age.plot
ggsave("Total.Biventer.Age.plot.pdf", plot = Total.Biventer.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Tonsilla.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Tonsilla, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 7700, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME tonsilla") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Tonsilla.Age.plot
ggsave("Total.Tonsilla.Age.plot.pdf", plot = Total.Tonsilla.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Total.Flocculus.Age.plot <-  ggplot(Brainstem.Cerebellum.absolute, aes(y=Flocculus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1050, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME flocculus") +
  theme(plot.title = element_text(hjust = 0.5))
Total.Flocculus.Age.plot
ggsave("Total.Flocculus.Age.plot.pdf", plot = Total.Flocculus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```

## Relative Volumes

```{r Brainstem and Cerebellum: Relative Volumes}

#Brainstem.Cerebellum.absolute <- select(Brainstem.Cerebellum.absolute, - c(Age, Gender))
Brainstem.Cerebellum.relative <- (100 * (Brainstem.Cerebellum.absolute/All.Volumes$`Total encephalic volume (without ventricles)`))
Brainstem.Cerebellum.relative1 <- Brainstem.Cerebellum.relative[,c(1:10)]
Brainstem.Cerebellum.relative2 <- Brainstem.Cerebellum.relative[,-c(1:10)]

Table.Brainstem.Cerebellum.relative <- CreateTableOne(
  vars = c("Mesencephalon",
"Pons",
"Medulla.oblongata", 
"Cerebellar.peduncles", 
"Vermis", 
"Hemisphere", 
"Anterior.lobe", 
"Medial.lobe", 
"Posterior.lobe", 
"Flocculonodular.lobe", 
"Central", 
"Culmen", 
"Declive", 
"Folium", 
"Tuber",
"Pyramid", 
"Uvula", 
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile", 
"Biventer",
"Tonsilla", 
"Flocculus"),
  data = Brainstem.Cerebellum.relative)

Brainstem.Cerebellum.relative$Gender <- All.Volumes$Gender

Table.Brainstem.Cerebellum.relative.stratified.gender <- CreateTableOne(
  vars = c("Mesencephalon",
"Pons",
"Medulla.oblongata", 
"Cerebellar.peduncles", 
"Vermis", 
"Hemisphere", 
"Anterior.lobe", 
"Medial.lobe", 
"Posterior.lobe", 
"Flocculonodular.lobe", 
"Central", 
"Culmen", 
"Declive", 
"Folium", 
"Tuber",
"Pyramid", 
"Uvula", 
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile", 
"Biventer",
"Tonsilla", 
"Flocculus"),
strata = c("Gender"),
  data = Brainstem.Cerebellum.relative)

Table.Brainstem.Cerebellum.relative <- print(Table.Brainstem.Cerebellum.relative, contDigits = 10)
Table.Brainstem.Cerebellum.relative.stratified.gender <- print(Table.Brainstem.Cerebellum.relative.stratified.gender, contDigits = 10)

```

```{r Brainstem and Cerebellum: Relative Volumes relative standard deviations}

Table.Brainstem.Cerebellum.relative.RSD <- as.data.frame(Table.Brainstem.Cerebellum.relative)
Table.Brainstem.Cerebellum.relative.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Brainstem.Cerebellum.relative.RSD[-1,]),
                                                                       ' (',fixed=TRUE)))
Table.Brainstem.Cerebellum.relative.RSD <- data.frame(cbind(str_replace_all(Table.Brainstem.Cerebellum.relative.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.Brainstem.Cerebellum.relative.RSD$X2, "[)]", "")))
Table.Brainstem.Cerebellum.relative.RSD$X1 <- as.character(Table.Brainstem.Cerebellum.relative.RSD$X1)
Table.Brainstem.Cerebellum.relative.RSD$X2 <- as.character(Table.Brainstem.Cerebellum.relative.RSD$X2)
Table.Brainstem.Cerebellum.relative.RSD <- as.data.frame(sapply(Table.Brainstem.Cerebellum.relative.RSD, as.numeric))
Table.Brainstem.Cerebellum.relative.RSD <- as.data.frame(Table.Brainstem.Cerebellum.relative.RSD$X2/Table.Brainstem.Cerebellum.relative.RSD$X1)
Table.Brainstem.Cerebellum.relative.RSD <- round(Table.Brainstem.Cerebellum.relative.RSD * 100, 1)


Table.Brainstem.Cerebellum.relative.stratified.gender.RSD <- as.data.frame(Table.Brainstem.Cerebellum.relative.stratified.gender)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD <- select(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD, - c(p, test))

Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X1 <- as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X2 <- as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X2)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female, as.numeric))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- as.data.frame(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X2/Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female$X1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female <- round(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female * 100, 1)

Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X1 <- as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X2 <- as.character(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X2)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male, as.numeric))
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- as.data.frame(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X2/Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male$X1)
Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male <- round(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male * 100, 1)

```

```{r Brainstem and Cerebellum: Relative Volumes Tables}

kable(Table.Brainstem.Cerebellum.relative)
kable(Table.Brainstem.Cerebellum.relative.RSD)
kable(Table.Brainstem.Cerebellum.relative.stratified.gender)
kable(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.female)
kable(Table.Brainstem.Cerebellum.relative.stratified.gender.RSD.male)

```

```{r Brainstem and Cerebellum: Relative Volumes and Gender Plot 1}

names.anatomical.structures.temporary <- c("Mesencephalon",
"Pons",
"Medulla.oblongata", 
"Cerebellar.peduncles", 
"Vermis", 
"Hemisphere", 
"Anterior.lobe", 
"Medial.lobe", 
"Posterior.lobe", 
"Flocculonodular.lobe")

names.anatomical.structures.definitive <- c("Mesencephalon",
"Pons",
"Medulla oblongata", 
"Cerebellar peduncles", 
"Vermis", 
"Hemisphere", 
"Anterior lobe", 
"Medial lobe", 
"Posterior lobe", 
"Flocculonodular lobe")

Brainstem.Cerebellum.relative.plotdata1 <- gather(Brainstem.Cerebellum.relative1, "anatomical.structure", "relative.volume")
Brainstem.Cerebellum.relative.plotdata1$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.relative.plotdata1$Age <- All.Volumes$`Age (years)`

Brainstem.Cerebellum.relative.plotdata1$Gender <- factor(Brainstem.Cerebellum.relative.plotdata1$Gender, levels = c("f", "m"), c("f", "m"))
Brainstem.Cerebellum.relative.plotdata1$anatomical.structure <- factor(Brainstem.Cerebellum.relative.plotdata1$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Brainstem.Cerebellum.relative.plot1 <-  ggplot(Brainstem.Cerebellum.relative.plotdata1, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Relative volume (in %)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("BRAINSTEM & CEREBELLUM") +
  theme(plot.title = element_text(hjust = 0.5))

Brainstem.Cerebellum.relative.plot1
ggsave("Brainstem.Cerebellum.relative.plot1.pdf", plot = Brainstem.Cerebellum.relative.plot1, width = 12, height = 6, units = "in", dpi = 600)

```

```{r Brainstem and Cerebellum: Relative Volumes and Gender Plot 2}

names.anatomical.structures.temporary <- c(
"Central", 
"Culmen", 
"Declive", 
"Folium", 
"Tuber",
"Pyramid", 
"Uvula", 
"Nodule",
"Ala.lobuli.centralis",
"AQL",
"PQL",
"SSL",
"ISL.gracile", 
"Biventer",
"Tonsilla", 
"Flocculus")

names.anatomical.structures.definitive <- c(
"Central", 
"Culmen", 
"Declive", 
"Folium", 
"Tuber",
"Pyramid", 
"Uvula", 
"Nodule",
"Ala lobuli centralis",
"AQL",
"PQL",
"SSL",
"ISL/gracile", 
"Biventer",
"Tonsilla", 
"Flocculus")

Brainstem.Cerebellum.relative.plotdata2 <- gather(Brainstem.Cerebellum.relative2, "anatomical.structure", "relative.volume")
Brainstem.Cerebellum.relative.plotdata2$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.relative.plotdata2$Age <- All.Volumes$`Age (years)`

Brainstem.Cerebellum.relative.plotdata2$Gender <- factor(Brainstem.Cerebellum.relative.plotdata2$Gender, levels = c("f", "m"), c("f", "m"))
Brainstem.Cerebellum.relative.plotdata2$anatomical.structure <- factor(Brainstem.Cerebellum.relative.plotdata2$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Brainstem.Cerebellum.relative.plot2 <-  ggplot(Brainstem.Cerebellum.relative.plotdata2, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Relative volume (in %)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("CEREBELLAR LOBES & LOBULES") +
  theme(plot.title = element_text(hjust = 0.5))

Brainstem.Cerebellum.relative.plot2
ggsave("Brainstem.Cerebellum.relative.plot2.pdf", plot = Brainstem.Cerebellum.relative.plot2, width = 12, height = 6, units = "in", dpi = 600)

```

```{r Brainstem and Cerebellum: Relative Volumes and Age Plot}

#Brainstem.Cerebellum.relative$Gender <- All.Volumes$Gender
Brainstem.Cerebellum.relative$Age <- All.Volumes$`Age (years)`

Relative.Mesencephalon.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Mesencephalon, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.02, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME mesencephalon") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Mesencephalon.Age.plot
ggsave("Relative.Mesencephalon.Age.plot.pdf", plot = Relative.Mesencephalon.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Pons.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Pons, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.93, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME pons") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Pons.Age.plot
ggsave("Relative.Pons.Age.plot.pdf", plot = Relative.Pons.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Medulla.oblongata.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Medulla.oblongata, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.33, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME medulla oblongata") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Medulla.oblongata.Age.plot
ggsave("Relative.Medulla.oblongata.Age.plot.pdf", plot = Relative.Medulla.oblongata.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Cerebellar.peduncles.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Cerebellar.peduncles, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.83, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME cerebellar peduncle") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Cerebellar.peduncles.Age.plot
ggsave("Relative.Cerebellar.peduncles.Age.plot.pdf", plot = Relative.Cerebellar.peduncles.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Vermis.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Vermis, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.67, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME vermis") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Vermis.Age.plot
ggsave("Relative.Vermis.Age.plot.pdf", plot = Relative.Vermis.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Hemisphere.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Hemisphere, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 11.7, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME hemisphere") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Hemisphere.Age.plot
ggsave("Relative.Hemisphere.Age.plot.pdf", plot = Relative.Hemisphere.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Anterior.lobe.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Anterior.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3.6, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME anterior lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Anterior.lobe.Age.plot
ggsave("Relative.Anterior.lobe.Age.plot.pdf", plot = Relative.Anterior.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Medial.lobe.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Medial.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3.1, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME medial lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Medial.lobe.Age.plot
ggsave("Relative.Medial.lobe.Age.plot.pdf", plot = Relative.Medial.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Posterior.lobe.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Posterior.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 6.6, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME posterior lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Posterior.lobe.Age.plot
ggsave("Relative.Posterior.lobe.Age.plot.pdf", plot = Relative.Posterior.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Flocculonodular.lobe.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Flocculonodular.lobe, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.121, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME flocculonodular lobe") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Flocculonodular.lobe.Age.plot
ggsave("Relative.Flocculonodular.lobe.Age.plot.pdf", plot = Relative.Flocculonodular.lobe.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Central.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Central, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.072, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME CENTRAL") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Central.Age.plot
ggsave("Relative.Central.Age.plot.pdf", plot = Relative.Central.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Culmen.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Culmen, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.228, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME CULMEN") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Culmen.Age.plot
ggsave("Relative.Culmen.Age.plot.pdf", plot = Relative.Culmen.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Declive.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Declive, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.13, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME DECLIVE") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Declive.Age.plot
ggsave("Relative.Declive.Age.plot.pdf", plot = Relative.Declive.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Folium.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Folium, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.062, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME FOLIUM") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Folium.Age.plot
ggsave("Relative.Folium.Age.plot.pdf", plot = Relative.Folium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Tuber.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Tuber, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.052, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME TUBER") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Tuber.Age.plot
ggsave("Relative.Tuber.Age.plot.pdf", plot = Relative.Tuber.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Pyramid.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Pyramid, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.062, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME PYRAMID") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Pyramid.Age.plot
ggsave("Relative.Pyramid.Age.plot.pdf", plot = Relative.Pyramid.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Uvula.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Uvula, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.097, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME UVULA") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Uvula.Age.plot
ggsave("Relative.Uvula.Age.plot.pdf", plot = Relative.Uvula.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Nodule.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Nodule, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.023, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME NODULE") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Nodule.Age.plot
ggsave("Relative.Nodule.Age.plot.pdf", plot = Relative.Nodule.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Ala.lobuli.centralis.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Ala.lobuli.centralis, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.45, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME ALA LOBULI CENTRALIS") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Ala.lobuli.centralis.Age.plot
ggsave("Relative.Ala.lobuli.centralis.Age.plot.pdf", plot = Relative.Ala.lobuli.centralis.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.AQL.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=AQL, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.7, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.1, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME AQL") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.AQL.Age.plot
ggsave("Relative.AQL.Age.plot.pdf", plot = Relative.AQL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.PQL.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=PQL, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.55, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME PQL") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.PQL.Age.plot
ggsave("Relative.PQL.Age.plot.pdf", plot = Relative.PQL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.SSL.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=SSL, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.55, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME SSL") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.SSL.Age.plot
ggsave("Relative.SSL.Age.plot.pdf", plot = Relative.SSL.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.ISL.gracile.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=ISL.gracile, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4.2, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME ISL/GRACILE") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.ISL.gracile.Age.plot
ggsave("Relative.ISL.gracile.Age.plot.pdf", plot = Relative.ISL.gracile.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Biventer.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Biventer, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.7, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME BIVENTER") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Biventer.Age.plot
ggsave("Relative.Biventer.Age.plot.pdf", plot = Relative.Biventer.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Tonsilla.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Tonsilla, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.72, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME TONSILLA") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Tonsilla.Age.plot
ggsave("Relative.Tonsilla.Age.plot.pdf", plot = Relative.Tonsilla.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Flocculus.Age.plot <-  ggplot(Brainstem.Cerebellum.relative, aes(y=Flocculus, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", linetype = "longdash", size = 0.5, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.5, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.105, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME FLOCCULUS") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Flocculus.Age.plot
ggsave("Relative.Flocculus.Age.plot.pdf", plot = Relative.Flocculus.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```

# The Ventricular System

## Absolute Volumes

```{r The Ventricular System: Absolute Volumes}

Total.volume.ventricles <- All.Volumes$`Total volume ventricles`
LV.total <-  All.Volumes$`Total volume lateral ventricles`
LV.frontal.horn <- All.Volumes$`Total volume frontal horn`
LV.body <- All.Volumes$`Total volume body of LV`
LV.atrium <- All.Volumes$`Total volume atrium`
LV.occipital.horn <- All.Volumes$`Total volume occipital horn`
LV.temporal.horn <- All.Volumes$`Total volume temporal horn`
Third.ventricle <- All.Volumes$`3rd ventricle`
Fourth.ventricle.total <- All.Volumes$`4th ventricle`
Apex <- All.Volumes$`Apex of 4th`
Lateral.recess <- All.Volumes$`Total volume lateral recess`
Obex <- All.Volumes$`Obex of 4th`
Fastigium <- All.Volumes$Fastigium

Ventricles.absolute <- as.data.frame(cbind(
Total.volume.ventricles,
LV.total,
LV.frontal.horn,
LV.body, 
LV.atrium, 
LV.occipital.horn, 
LV.temporal.horn, 
Third.ventricle, 
Fourth.ventricle.total,
Apex,
Lateral.recess,
Obex,
Fastigium
))

Ventricles.absolute$Gender <- All.Volumes$Gender

Table.Ventricles.absolute <- CreateTableOne(
  vars = c("Total.volume.ventricles",
"LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
  data = Ventricles.absolute)

Table.Ventricles.absolute.stratified.gender <- CreateTableOne(
  vars = c("Total.volume.ventricles",
"LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
strata = c("Gender"),
  data = Ventricles.absolute)

Table.Ventricles.absolute <- print(Table.Ventricles.absolute, contDigits = 10)
Table.Ventricles.absolute.stratified.gender <- print(Table.Ventricles.absolute.stratified.gender, contDigits = 10)

write.csv(Table.Ventricles.absolute, "Table.Ventricles.absolute.csv")
write.csv(Table.Ventricles.absolute.stratified.gender, "Table.Ventricles.absolute.stratified.gender.csv")

```

```{r The Ventricular System: Absolute Volumes relative standard deviations}

Table.Ventricles.absolute.RSD <- as.data.frame(Table.Ventricles.absolute)
Table.Ventricles.absolute.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.absolute.RSD[-1,]),' (',fixed=TRUE)))
Table.Ventricles.absolute.RSD <- data.frame(cbind(str_replace_all(Table.Ventricles.absolute.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.absolute.RSD$X2, "[)]", "")))
Table.Ventricles.absolute.RSD$X1 <- as.character(Table.Ventricles.absolute.RSD$X1)
Table.Ventricles.absolute.RSD$X2 <- as.character(Table.Ventricles.absolute.RSD$X2)
Table.Ventricles.absolute.RSD <- as.data.frame(sapply(Table.Ventricles.absolute.RSD, as.numeric))
Table.Ventricles.absolute.RSD <- as.data.frame(Table.Ventricles.absolute.RSD$X2/Table.Ventricles.absolute.RSD$X1)
Table.Ventricles.absolute.RSD <- round(Table.Ventricles.absolute.RSD * 100, 1)


Table.Ventricles.absolute.stratified.gender.RSD <- as.data.frame(Table.Ventricles.absolute.stratified.gender)
Table.Ventricles.absolute.stratified.gender.RSD <- select(Table.Ventricles.absolute.stratified.gender.RSD, - c(p, test))

Table.Ventricles.absolute.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.absolute.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.Ventricles.absolute.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Ventricles.absolute.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.absolute.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Ventricles.absolute.stratified.gender.RSD.female$X1 <- as.character(Table.Ventricles.absolute.stratified.gender.RSD.female$X1)
Table.Ventricles.absolute.stratified.gender.RSD.female$X2 <- as.character(Table.Ventricles.absolute.stratified.gender.RSD.female$X2)
Table.Ventricles.absolute.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Ventricles.absolute.stratified.gender.RSD.female, as.numeric))
Table.Ventricles.absolute.stratified.gender.RSD.female <- as.data.frame(Table.Ventricles.absolute.stratified.gender.RSD.female$X2/Table.Ventricles.absolute.stratified.gender.RSD.female$X1)
Table.Ventricles.absolute.stratified.gender.RSD.female <- round(Table.Ventricles.absolute.stratified.gender.RSD.female * 100, 1)

Table.Ventricles.absolute.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.absolute.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.Ventricles.absolute.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Ventricles.absolute.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.absolute.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Ventricles.absolute.stratified.gender.RSD.male$X1 <- as.character(Table.Ventricles.absolute.stratified.gender.RSD.male$X1)
Table.Ventricles.absolute.stratified.gender.RSD.male$X2 <- as.character(Table.Ventricles.absolute.stratified.gender.RSD.male$X2)
Table.Ventricles.absolute.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Ventricles.absolute.stratified.gender.RSD.male, as.numeric))
Table.Ventricles.absolute.stratified.gender.RSD.male <- as.data.frame(Table.Ventricles.absolute.stratified.gender.RSD.male$X2/Table.Ventricles.absolute.stratified.gender.RSD.male$X1)
Table.Ventricles.absolute.stratified.gender.RSD.male <- round(Table.Ventricles.absolute.stratified.gender.RSD.male * 100, 1)

```

```{r The Ventricular System: Absolute Volumes Tables}

kable(Table.Ventricles.absolute)
kable(Table.Ventricles.absolute.RSD)
kable(Table.Ventricles.absolute.stratified.gender)
kable(Table.Ventricles.absolute.stratified.gender.RSD.female)
kable(Table.Ventricles.absolute.stratified.gender.RSD.male)

```

```{r The Ventricular System: Absolute Volumes and Gender Plot}

Ventricles.absolute <- select(Ventricles.absolute, - c(Gender))
Ventricles.absolute.red <- select(Ventricles.absolute, - c(Total.volume.ventricles))

names.anatomical.structures.temporary <- c("LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium")

names.anatomical.structures.definitive <- c("LV- Total",
"LV - Frontal horn",
"LV - Body", 
"LV - Atrium", 
"LV - Occipital horn", 
"LV - Temporal horn", 
"Third ventricle", 
"Fourth ventricle - Total",
"Apex",
"Lateral recess",
"Obex",
"Fastigium")

Ventricles.absolute.plotdata <- gather(Ventricles.absolute.red, "anatomical.structure", "relative.volume")
Ventricles.absolute.plotdata$Gender <- All.Volumes$Gender
Ventricles.absolute.plotdata$Age <- All.Volumes$`Age (years)`

Ventricles.absolute.plotdata$Gender <- factor(Ventricles.absolute.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Ventricles.absolute.plotdata$anatomical.structure <- factor(Ventricles.absolute.plotdata$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Ventricles.absolute.plot <-  ggplot(Ventricles.absolute.plotdata, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Absolute volume (in mm3)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("VENTRICULAR SYSTEM") +
  theme(plot.title = element_text(hjust = 0.5))

Ventricles.absolute.plot
ggsave("Ventricles.absolute.plot.pdf", plot = Ventricles.absolute.plot, width = 12, height = 5, units = "in", dpi = 600)

```

```{r The Ventricular System: Absolute Volumes and Age Plot}

Total.ventricles.Age.plot <-  ggplot(All.Volumes, aes(y=`Total volume ventricles`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 82000, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME total ventricular system") +
  theme(plot.title = element_text(hjust = 0.5))
Total.ventricles.Age.plot
ggsave("Total.Total.ventricles.Age.plot.pdf", plot = Total.ventricles.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Lateral.ventricle.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume lateral ventricles`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 72000, label.x = 65, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME lateral ventricles") +
  theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.Age.plot
ggsave("Total.Lateral.ventricle.Age.plot.pdf", plot = Lateral.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Lateral.ventricle.frontal.horn.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume frontal horn`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 21000, label.x = 65, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME frontal horn") +
  theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.frontal.horn.Age.plot
ggsave("Total.Lateral.ventricle.frontal.horn.Age.plot.pdf", plot = Lateral.ventricle.frontal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Lateral.ventricle.body.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume body of LV`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 19000, label.x = 65, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME body") +
  theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.body.Age.plot
ggsave("Total.Lateral.ventricle.body.Age.plot.pdf", plot = Lateral.ventricle.body.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Lateral.ventricle.atrium.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume atrium`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 24000, label.x = 65, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME atrium") +
  theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.atrium.Age.plot
ggsave("Total.Lateral.ventricle.atrium.Age.plot.pdf", plot = Lateral.ventricle.atrium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Lateral.ventricle.occipital.horn.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume occipital horn`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4100, label.x = 65, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME occipital horn") +
  theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.occipital.horn.Age.plot
ggsave("Total.Lateral.ventricle.occipital.horn.Age.plot.pdf", plot = Lateral.ventricle.occipital.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Lateral.ventricle.temporal.horn.Age.plot <- ggplot(All.Volumes, aes(y=`Total volume temporal horn`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 3100, label.x = 65, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME temporal horn") +
  theme(plot.title = element_text(hjust = 0.5))
Lateral.ventricle.temporal.horn.Age.plot
ggsave("Total.Lateral.ventricle.temporal.horn.Age.plot.pdf", plot = Lateral.ventricle.temporal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Third.ventricle.Age.plot <- ggplot(All.Volumes, aes(y =`3rd ventricle`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 4250, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME third ventricle") +
  theme(plot.title = element_text(hjust = 0.5))
Third.ventricle.Age.plot
ggsave("Total.Third.ventricle.Age.plot.pdf", plot = Third.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Fourth.ventricle.Age.plot <- ggplot(All.Volumes, aes(y =`4th ventricle`, x = `Age (years)`))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2700, label.x = 70, color = "dodgerblue4") +
  ylab("Volume in mm3") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("ABSOLUTE VOLUME fourth ventricle") +
  theme(plot.title = element_text(hjust = 0.5))
Fourth.ventricle.Age.plot
ggsave("Total.Fourth.ventricle.Age.plot.pdf", plot = Fourth.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```

## Relative Volumes 1: Normalized to Total Encephalic Volume

```{r The Ventricular System: Relative Volumes 1}

#Ventricles.absolute <- select(Ventricles.absolute, - c(Gender))
Total.ventricular.volume.relative <- (100 * (Ventricles.absolute$Total.volume.ventricles/All.Volumes$`Total encephalic volume (without ventricles)`))

Ventricles.relative <- (100 * (Ventricles.absolute[, -1]/All.Volumes$`Total encephalic volume (without ventricles)`))

Table.Ventricles.relative <- cbind(Total.ventricular.volume.relative, Ventricles.relative)

Table.Ventricles.relative$Gender <- All.Volumes$Gender

Table.Ventricles.relative1 <- CreateTableOne(
  vars = c("Total.ventricular.volume.relative",
"LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
  data = Table.Ventricles.relative)

Table.Ventricles.relative.stratified.gender <- CreateTableOne(
  vars = c("Total.ventricular.volume.relative",
"LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
strata = c("Gender"),
  data = Table.Ventricles.relative)

Table.Ventricles.relative1 <- print(Table.Ventricles.relative1, contDigits = 10)
Table.Ventricles.relative.stratified.gender <- print(Table.Ventricles.relative.stratified.gender, contDigits = 10)

```

```{r The Ventricular System: Relative Volumes 1 relative standard deviations}

Table.Ventricles.relative1.RSD <- as.data.frame(Table.Ventricles.relative1)
Table.Ventricles.relative1.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.relative1.RSD[-1,]),
                                                                       ' (',fixed=TRUE)))
Table.Ventricles.relative1.RSD <- data.frame(cbind(str_replace_all(Table.Ventricles.relative1.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.relative1.RSD$X2, "[)]", "")))
Table.Ventricles.relative1.RSD$X1 <- as.character(Table.Ventricles.relative1.RSD$X1)
Table.Ventricles.relative1.RSD$X2 <- as.character(Table.Ventricles.relative1.RSD$X2)
Table.Ventricles.relative1.RSD <- as.data.frame(sapply(Table.Ventricles.relative1.RSD, as.numeric))
Table.Ventricles.relative1.RSD <- as.data.frame(Table.Ventricles.relative1.RSD$X2/Table.Ventricles.relative1.RSD$X1)
Table.Ventricles.relative1.RSD <- round(Table.Ventricles.relative1.RSD * 100, 1)


Table.Ventricles.relative.stratified.gender.RSD <- as.data.frame(Table.Ventricles.relative.stratified.gender)
Table.Ventricles.relative.stratified.gender.RSD <- select(Table.Ventricles.relative.stratified.gender.RSD, - c(p, test))

Table.Ventricles.relative.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.relative.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.Ventricles.relative.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.relative.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Ventricles.relative.stratified.gender.RSD.female$X1 <- as.character(Table.Ventricles.relative.stratified.gender.RSD.female$X1)
Table.Ventricles.relative.stratified.gender.RSD.female$X2 <- as.character(Table.Ventricles.relative.stratified.gender.RSD.female$X2)
Table.Ventricles.relative.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Ventricles.relative.stratified.gender.RSD.female, as.numeric))
Table.Ventricles.relative.stratified.gender.RSD.female <- as.data.frame(Table.Ventricles.relative.stratified.gender.RSD.female$X2/Table.Ventricles.relative.stratified.gender.RSD.female$X1)
Table.Ventricles.relative.stratified.gender.RSD.female <- round(Table.Ventricles.relative.stratified.gender.RSD.female * 100, 1)

Table.Ventricles.relative.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.relative.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.Ventricles.relative.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.relative.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Ventricles.relative.stratified.gender.RSD.male$X1 <- as.character(Table.Ventricles.relative.stratified.gender.RSD.male$X1)
Table.Ventricles.relative.stratified.gender.RSD.male$X2 <- as.character(Table.Ventricles.relative.stratified.gender.RSD.male$X2)
Table.Ventricles.relative.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Ventricles.relative.stratified.gender.RSD.male, as.numeric))
Table.Ventricles.relative.stratified.gender.RSD.male <- as.data.frame(Table.Ventricles.relative.stratified.gender.RSD.male$X2/Table.Ventricles.relative.stratified.gender.RSD.male$X1)
Table.Ventricles.relative.stratified.gender.RSD.male <- round(Table.Ventricles.relative.stratified.gender.RSD.male * 100, 1)

```

```{r The Ventricular System: Relative Volumes 1 Tables}

kable(Table.Ventricles.relative1)
kable(Table.Ventricles.relative1.RSD)
kable(Table.Ventricles.relative.stratified.gender)
kable(Table.Ventricles.relative.stratified.gender.RSD.female)
kable(Table.Ventricles.relative.stratified.gender.RSD.male)

```

```{r The Ventricular System: Relative Volumes 1 and Gender Plot}

names.anatomical.structures.temporary <- c("LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium")

names.anatomical.structures.definitive <- c("LV- Total",
"LV - Frontal horn",
"LV - Body", 
"LV - Atrium", 
"LV - Occipital horn", 
"LV - Temporal horn", 
"Third ventricle", 
"Fourth ventricle - Total",
"Apex",
"Lateral recess",
"Obex",
"Fastigium")

Ventricles.relative.plotdata <- gather(Ventricles.relative, "anatomical.structure", "relative.volume")
Ventricles.relative.plotdata$Gender <- All.Volumes$Gender
Ventricles.relative.plotdata$Age <- All.Volumes$`Age (years)`

Ventricles.relative.plotdata$Gender <- factor(Ventricles.relative.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Ventricles.relative.plotdata$anatomical.structure <- factor(Ventricles.relative.plotdata$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Ventricles.relative1.plot <-  ggplot(Ventricles.relative.plotdata, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Relative volume (in %)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("VENTRICULAR SYSTEM") +
  theme(plot.title = element_text(hjust = 0.5))

Ventricles.relative1.plot
ggsave("Ventricles.relative1.plot.pdf", plot = Ventricles.relative1.plot, width = 12, height = 5, units = "in", dpi = 600)

```

```{r The Ventricular System: Relative Volumes 1 and Age Plot}

Ventricles.relative$Gender <- All.Volumes$Gender
Ventricles.relative$Age <- All.Volumes$`Age (years)`
Ventricles.relative$Total.ventricular <- Total.ventricular.volume.relative

Relative.Total.ventricular.Age.plot <-  ggplot(Ventricles.relative, aes(y=Total.ventricular, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 8.2, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME total ventricular system (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Total.ventricular.Age.plot
ggsave("Relative.Total.ventricular.Age.plot.pdf", plot = Relative.Total.ventricular.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.LV.total.Age.plot <-  ggplot(Ventricles.relative, aes(y=LV.total, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 7.2, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME lateral ventricles (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.LV.total.Age.plot
ggsave("Relative.LV.total.Age.plot.pdf", plot = Relative.LV.total.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.LV.frontal.horn.Age.plot <-  ggplot(Ventricles.relative, aes(y=LV.frontal.horn, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.9, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME frontal horn (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.LV.frontal.horn.Age.plot
ggsave("Relative.LV.frontal.horn.Age.plot.pdf", plot = Relative.LV.frontal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.LV.body.Age.plot <-  ggplot(Ventricles.relative, aes(y=LV.body, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 1.9, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME body (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.LV.body.Age.plot
ggsave("Relative.LV.body.Age.plot.pdf", plot = Relative.LV.body.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.LV.atrium.Age.plot <-  ggplot(Ventricles.relative, aes(y=LV.atrium, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 2.4, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME atrium (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.LV.atrium.Age.plot
ggsave("Relative.LV.atrium.Age.plot.pdf", plot = Relative.LV.atrium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.LV.occipital.horn.Age.plot <-  ggplot(Ventricles.relative, aes(y=LV.occipital.horn, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.37, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME occipital horn (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.LV.occipital.horn.Age.plot
ggsave("Relative.LV.occipital.horn.Age.plot.pdf", plot = Relative.LV.occipital.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.LV.temporal.horn.Age.plot <-  ggplot(Ventricles.relative, aes(y=LV.temporal.horn, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.28, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME temporal horn (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.LV.temporal.horn.Age.plot
ggsave("Relative.LV.temporal.horn.Age.plot.pdf", plot = Relative.LV.temporal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Third.ventricle.Age.plot <-  ggplot(Ventricles.relative, aes(y=Third.ventricle, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.47, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME third ventricle (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Third.ventricle.Age.plot
ggsave("Relative.Third.ventricle.Age.plot.pdf", plot = Relative.Third.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

Relative.Fourth.ventricle.total.Age.plot <-  ggplot(Ventricles.relative, aes(y=Fourth.ventricle.total, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 0.26, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME fourth ventricle (E)") +
  theme(plot.title = element_text(hjust = 0.5))
Relative.Fourth.ventricle.total.Age.plot
ggsave("Relative.Fourth.ventricle.total.Age.plot.pdf", plot = Relative.Fourth.ventricle.total.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```

## Relative Volumes 2: Normalized to Total Ventricular Volume

```{r The Ventricular System: Relative Volumes 2}

#Ventricles.absolute <- select(Ventricles.absolute, - c(Gender))
#Total.ventricular.volume.relative <- (100 * (Ventricles.absolute$Total.volume.ventricles/All.Volumes$`Total encephalic volume (without ventricles)`))

Ventricles.relative.V <- (100 * (Ventricles.absolute[, -1]/Total.volume.ventricles))

Table.Ventricles.relative.V <- Ventricles.relative.V

Table.Ventricles.relative.V$Gender <- All.Volumes$Gender

Table.Ventricles.relative.V.1 <- CreateTableOne(
  vars = c("LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
  data = Table.Ventricles.relative.V)

Table.Ventricles.relative.V.stratified.gender <- CreateTableOne(
  vars = c("LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium"),
strata = c("Gender"),
  data = Table.Ventricles.relative.V)

Table.Ventricles.relative.V.1 <- print(Table.Ventricles.relative.V.1, contDigits = 10)
Table.Ventricles.relative.V.stratified.gender <- print(Table.Ventricles.relative.V.stratified.gender, contDigits = 10)

```

```{r The Ventricular System: Relative Volumes 2 relative standard deviations}

Table.Ventricles.relative.V.1.RSD <- as.data.frame(Table.Ventricles.relative.V.1)
Table.Ventricles.relative.V.1.RSD <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.relative.V.1.RSD[-1,]),
                                                                       ' (',fixed=TRUE)))
Table.Ventricles.relative.V.1.RSD <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.V.1.RSD$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.relative.V.1.RSD$X2, "[)]", "")))
Table.Ventricles.relative.V.1.RSD$X1 <- as.character(Table.Ventricles.relative.V.1.RSD$X1)
Table.Ventricles.relative.V.1.RSD$X2 <- as.character(Table.Ventricles.relative.V.1.RSD$X2)
Table.Ventricles.relative.V.1.RSD <- as.data.frame(sapply(Table.Ventricles.relative.V.1.RSD, as.numeric))
Table.Ventricles.relative.V.1.RSD <- as.data.frame(Table.Ventricles.relative.V.1.RSD$X2/Table.Ventricles.relative.V.1.RSD$X1)
Table.Ventricles.relative.V.1.RSD <- round(Table.Ventricles.relative.V.1.RSD * 100, 1)


Table.Ventricles.relative.V.stratified.gender.RSD <- as.data.frame(Table.Ventricles.relative.V.stratified.gender)
Table.Ventricles.relative.V.stratified.gender.RSD <- select(Table.Ventricles.relative.V.stratified.gender.RSD, - c(p, test))

Table.Ventricles.relative.V.stratified.gender.RSD.female <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.relative.V.stratified.gender.RSD[-1, "f"]),
                                                                       ' (',fixed=TRUE)))
Table.Ventricles.relative.V.stratified.gender.RSD.female <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.V.stratified.gender.RSD.female$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.relative.V.stratified.gender.RSD.female$X2, "[)]", "")))
Table.Ventricles.relative.V.stratified.gender.RSD.female$X1 <- as.character(Table.Ventricles.relative.V.stratified.gender.RSD.female$X1)
Table.Ventricles.relative.V.stratified.gender.RSD.female$X2 <- as.character(Table.Ventricles.relative.V.stratified.gender.RSD.female$X2)
Table.Ventricles.relative.V.stratified.gender.RSD.female <- as.data.frame(sapply(Table.Ventricles.relative.V.stratified.gender.RSD.female, as.numeric))
Table.Ventricles.relative.V.stratified.gender.RSD.female <- as.data.frame(Table.Ventricles.relative.V.stratified.gender.RSD.female$X2/Table.Ventricles.relative.V.stratified.gender.RSD.female$X1)
Table.Ventricles.relative.V.stratified.gender.RSD.female <- round(Table.Ventricles.relative.V.stratified.gender.RSD.female * 100, 1)

Table.Ventricles.relative.V.stratified.gender.RSD.male <- data.frame(do.call('rbind', 
                                                              strsplit(as.character(Table.Ventricles.relative.V.stratified.gender.RSD[-1, "m"]),
                                                                       ' (',fixed=TRUE)))
Table.Ventricles.relative.V.stratified.gender.RSD.male <- data.frame(cbind(str_replace_all(Table.Ventricles.relative.V.stratified.gender.RSD.male$X1, "[ ]", ""),
                                                            str_replace_all(Table.Ventricles.relative.V.stratified.gender.RSD.male$X2, "[)]", "")))
Table.Ventricles.relative.V.stratified.gender.RSD.male$X1 <- as.character(Table.Ventricles.relative.V.stratified.gender.RSD.male$X1)
Table.Ventricles.relative.V.stratified.gender.RSD.male$X2 <- as.character(Table.Ventricles.relative.V.stratified.gender.RSD.male$X2)
Table.Ventricles.relative.V.stratified.gender.RSD.male <- as.data.frame(sapply(Table.Ventricles.relative.V.stratified.gender.RSD.male, as.numeric))
Table.Ventricles.relative.V.stratified.gender.RSD.male <- as.data.frame(Table.Ventricles.relative.V.stratified.gender.RSD.male$X2/Table.Ventricles.relative.V.stratified.gender.RSD.male$X1)
Table.Ventricles.relative.V.stratified.gender.RSD.male <- round(Table.Ventricles.relative.V.stratified.gender.RSD.male * 100, 1)

```

```{r The Ventricular System: Relative Volumes 2 Tables}

kable(Table.Ventricles.relative.V.1)
kable(Table.Ventricles.relative.V.1.RSD)
kable(Table.Ventricles.relative.V.stratified.gender)
kable(Table.Ventricles.relative.V.stratified.gender.RSD.female)
kable(Table.Ventricles.relative.V.stratified.gender.RSD.male)

```

```{r The Ventricular System: Relative Volumes 2 and Gender Plot}

names.anatomical.structures.temporary <- c("LV.total",
"LV.frontal.horn",
"LV.body", 
"LV.atrium", 
"LV.occipital.horn", 
"LV.temporal.horn", 
"Third.ventricle", 
"Fourth.ventricle.total",
"Apex",
"Lateral.recess",
"Obex",
"Fastigium")

names.anatomical.structures.definitive <- c("LV- Total",
"LV - Frontal horn",
"LV - Body", 
"LV - Atrium", 
"LV - Occipital horn", 
"LV - Temporal horn", 
"Third ventricle", 
"Fourth ventricle - Total",
"Apex",
"Lateral recess",
"Obex",
"Fastigium")

Ventricles.relative.V.plotdata <- gather(Ventricles.relative.V, "anatomical.structure", "relative.volume")
Ventricles.relative.V.plotdata$Gender <- All.Volumes$Gender
Ventricles.relative.V.plotdata$Age <- All.Volumes$`Age (years)`

Ventricles.relative.V.plotdata$Gender <- factor(Ventricles.relative.V.plotdata$Gender, levels = c("f", "m"), c("f", "m"))
Ventricles.relative.V.plotdata$anatomical.structure <- factor(Ventricles.relative.V.plotdata$anatomical.structure, 
                                                                levels = rev(c(names.anatomical.structures.temporary)), rev(c(names.anatomical.structures.definitive)))

Ventricles.relative2.plot <-  ggplot(Ventricles.relative.V.plotdata, aes(x=anatomical.structure, y = relative.volume))  +
  stat_summary(alpha = 0.3, fun = mean, geom = "bar", width = 0.3, fill = "gray50") + 
  geom_boxplot(aes(fill = Gender), alpha = 0.5, width = 0.4, size = 0.2, position = position_dodge(width = 0.6), 
               outlier.shape = NA, color = "gray30") +
  scale_fill_manual(values = c("chartreuse4", "orangered2")) +
  geom_quasirandom(aes(color = Age), size = 0.7, alpha = 0.8, shape = 16, position = "dodge") +
  scale_color_continuous(low = "steelblue1", high = "red4") +
  xlab("") + ylab("Relative volume (in %)") +
  theme_minimal() +
  coord_flip() +
  ggtitle("VENTRICULAR SYSTEM") +
  theme(plot.title = element_text(hjust = 0.5))

Ventricles.relative2.plot
ggsave("Ventricles.relative2.plot.pdf", plot = Ventricles.relative2.plot, width = 12, height = 5, units = "in", dpi = 600)

```

```{r The Ventricular System: Relative Volumes 2 and Age Plot}

Ventricles.relative.V$Gender <- All.Volumes$Gender
Ventricles.relative.V$Age <- All.Volumes$`Age (years)`

V.Relative.LV.total.Age.plot <-  ggplot(Ventricles.relative.V, aes(y=LV.total, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 102, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME lateral ventricles (V)") +
  theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.total.Age.plot
ggsave("V.Relative.LV.total.Age.plot.pdf", plot = V.Relative.LV.total.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

V.Relative.LV.frontal.horn.Age.plot <-  ggplot(Ventricles.relative.V, aes(y=LV.frontal.horn, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 39, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME frontal horn (V)") +
  theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.frontal.horn.Age.plot
ggsave("V.Relative.LV.frontal.horn.Age.plot.pdf", plot = V.Relative.LV.frontal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

V.Relative.LV.body.Age.plot <-  ggplot(Ventricles.relative.V, aes(y=LV.body, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 33, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME body (V)") +
  theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.body.Age.plot
ggsave("V.Relative.LV.body.Age.plot.pdf", plot = V.Relative.LV.body.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

V.Relative.LV.atrium.Age.plot <-  ggplot(Ventricles.relative.V, aes(y=LV.atrium, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 43, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME atrium (V)") +
  theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.atrium.Age.plot
ggsave("V.Relative.LV.atrium.Age.plot.pdf", plot = V.Relative.LV.atrium.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

V.Relative.LV.occipital.horn.Age.plot <-  ggplot(Ventricles.relative.V, aes(y=LV.occipital.horn, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 11, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME occipital horn (V)") +
  theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.occipital.horn.Age.plot
ggsave("V.Relative.LV.occipital.horn.Age.plot.pdf", plot = V.Relative.LV.occipital.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

V.Relative.LV.temporal.horn.Age.plot <-  ggplot(Ventricles.relative.V, aes(y=LV.temporal.horn, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 16, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME temporal horn (V)") +
  theme(plot.title = element_text(hjust = 0.5))
V.Relative.LV.temporal.horn.Age.plot
ggsave("V.Relative.LV.temporal.horn.Age.plot.pdf", plot = V.Relative.LV.temporal.horn.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

V.Relative.Third.ventricle.Age.plot <-  ggplot(Ventricles.relative.V, aes(y=Third.ventricle, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 9.2, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME third ventricle (E)") +
  theme(plot.title = element_text(hjust = 0.5))
V.Relative.Third.ventricle.Age.plot
ggsave("V.Relative.Third.ventricle.Age.plot.pdf", plot = V.Relative.Third.ventricle.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

V.Relative.Fourth.ventricle.total.Age.plot <-  ggplot(Ventricles.relative.V, aes(y=Fourth.ventricle.total, x = Age))  +
  geom_point(aes(color = Gender), size = 1.5, alpha = 1, shape = 16) +
  scale_color_manual(values = c("chartreuse4", "orangered2")) +
  geom_smooth(method='lm', alpha = 0.2, colour = "dodgerblue4", size = 0.8, weight = 0.3) +
  geom_smooth(aes(color = Gender), method='lm', se = F, alpha = 0.2, linetype = "longdash", size = 0.3, weight = 0.3) +
  stat_cor(method = "pearson", label.y = 17, label.x = 70, color = "dodgerblue4") +
  ylab("Relative Volume (in %)") + xlab("Age (in years)") +
  theme_minimal() +
  ggtitle("RELATIVE VOLUME fourth ventricle (E)") +
  theme(plot.title = element_text(hjust = 0.5))
V.Relative.Fourth.ventricle.total.Age.plot
ggsave("V.Relative.Fourth.ventricle.total.Age.plot.pdf", plot = V.Relative.Fourth.ventricle.total.Age.plot, width = 8, height = 6, units = "in", dpi = 600)

```