Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Apr 1.
Published in final edited form as: Biol Psychiatry Cogn Neurosci Neuroimaging. 2021 Oct 14;7(4):424–434. doi: 10.1016/j.bpsc.2021.10.003

The structural connectome and internalizing and externalizing symptoms at 7 and 13 years in individuals born very preterm and full-term

Courtney P Gilchrist 1,2,3, Deanne K Thompson 2,3,4, Claire E Kelly 2,3,5, Richard Beare 3,6, Christopher Adamson 3,7, Thijs Dhollander 3, Katherine Lee 2,4,8, Karli Treyvaud 2,9,10, Lillian G Matthews 2,5,11,12, Mary Tolcos 1, Jeanie LY Cheong 2,10,13, Terrie E Inder 2,12, Lex W Doyle 2,4,10,13, Angela Cumberland 1,*, Peter J Anderson 2,5,*
PMCID: PMC8995318  NIHMSID: NIHMS1749567  PMID: 34655805

Abstract

Background:

Children born very preterm (VP) are at higher risk of emotional and behavioral problems compared with full-term (FT) children. We investigated the neurobiological basis of internalizing and externalizing symptoms in individuals born VP and FT by applying a graph theory approach.

Methods:

Structural and diffusion MRI data were combined to generate structural connectomes and calculate measures of network integration and segregation at 7 (VP:72; FT:17) and 13 years (VP:125; FT:44). Internalizing and externalizing were assessed at 7 and 13 years using the Strengths and Difficulties Questionnaire. Linear regression models were used to relate network measures and internalizing and externalizing symptoms concurrently at 7 and 13 years.

Results:

Lower network integration (characteristic path length and global efficiency) was associated with higher internalizing symptoms in VP and FT children at 7 years, but not at 13 years. The association between network integration (characteristic path length) and externalizing symptoms at 7 years was weaker, but there was some evidence for differential associations between groups, with lower integration in the VP and higher integration in the FT group associated with higher externalizing symptoms. At 13 years, there was some evidence that associations between network segregation (average clustering coefficient, transitivity, local efficiency) and externalizing differed between the VP and FT groups, with stronger positive associations in the VP group.

Conclusions:

This study provides insights into the neurobiological basis of emotional and behavioral problems following preterm birth, highlighting the role of the structural connectome in internalizing and externalizing symptoms in childhood and adolescence.

Keywords: premature birth, connectivity, magnetic resonance imaging, psychopathology, childhood, adolescence

INTRODUCTION

Higher rates of clinically significant mental health problems appear to persist throughout life in those born very preterm (VP) compared with their full-term born counterparts (FT). Using dimensional scales of behavior problems, individuals born VP display more internalizing symptoms such as anxiety, depression and social withdrawal in early childhood (1–3) late childhood(2–4), and beyond (5–7) than individuals born FT. Conversely, rates and progression of externalizing symptoms, including conduct and hyperactivity problems, in this population are less clear. Some studies report elevated externalizing symptoms during early childhood, which decline relative to FT peers across childhood and into adolescence (2, 5) and others report no differences in externalizing problems in early or late childhood compared with FT peers (1, 4).

Socio-environmental factors may contribute to internalizing and externalizing symptoms in the preterm population (8–12), however, it is likely that there is a neurobiological basis for this psychopathology, given the risk of brain injury following VP birth and emerging body of literature linking early brain development with later mental health outcomes (13, 14). White matter injury is the primary neuropathology in preterm infants and is characterized by premyelinating oligodendrocyte injury and subsequent impaired oligodendrocyte maturation, as well as microgliosis and astrogliosis (15). Individuals born VP display initial white matter and secondary gray matter volume reductions compared with FT peers in infancy (16) and childhood (17). Altered cingulum and uncinate fasciculi microstructure (18–23) and reduced orbitofrontal cortex (24), amygdalae (25, 26), and hippocampal volumes (26–28) in those born VP suggest a potential neurobiological basis for the psychopathology. However, the role of the global organization of white matter in internalizing and externalizing symptoms in the VP population has not yet been assessed.

Graph theory provides a framework to assess complex white matter organization (29–31), providing insight into disorganization of structural brain networks in various disorders (32). In this context, the brain is modelled as a graph, referred to as a structural connectome, consisting of parcellated gray matter regions (nodes) which are interconnected by white matter tracts (edges). These can be derived from diffusion MRI (dMRI)-guided whole-brain tractography. The strength of connection between each pair of nodes can be quantified in different ways (33–38), and various properties of network topology can be calculated (29, 39–45).

Studies investigating structural connectomes in the wider preterm population suggest a reorganization of network architecture, consistent with preservation of a central core of highly interconnected nodes and modular organization at the expense of peripheral connectivity (46, 47). Features of the adult human connectome, including a rich-club architecture (defined by a central set of highly interconnected hub regions) (44, 45), modularity (division of the brain network into distinct sub-networks of highly interconnected regions) (29), and a small-world topology (simultaneous local specialization in sub-networks of high clustering nodes and global integration for efficient processing) (39) are present at 30 weeks of gestation and appear to be retained in the preterm brain (46, 48–50).

Despite this relative sparing of central connections following preterm birth (46, 48, 50), evidence suggests that the typical developmental course of the structural connectome is affected in those born VP (48, 51, 52). Reduced global efficiency has been reported in preterm-born neonates compared with full-term peers (51), and is consistent with more rapid development of short-range, within-module connections compared with long-range, between-module connections from birth to term-equivalent age in the preterm brain (53). With the basic structural layout of the connectome largely in place by 2 years of age (54), further connectome development is primarily driven by modulations in the strength of connections. Across childhood and adolescence, the connectome appears to follow a trajectory of increasing integration and decreasing segregation (54, 55), likely driven by increases in axon diameter and myelination of long-range connections during this period (54, 56). Such developmental changes in topology support more integrated information processing across the brain to facilitate higher-order cognitive functions (57). We have previously reported higher local efficiency (segregation) and lower global efficiency (integration) in VP children at 7 years of age compared with FT peers (52), which is suggestive of immature white matter organization (54).

Based on previous findings indicating that individuals born VP display a less integrated and more segregated topology (48, 51, 52), we have applied connectomics to determine whether aberrant brain organization may relate to concurrent internalizing and externalizing symptoms at 7 and 13 years. We hypothesized that lower integration and small-worldness would be associated with higher internalizing and externalizing symptoms at 7 and 13 years, while higher segregation would be associated with higher internalizing and externalizing symptoms at 7 and 13 years, respectively.

Previous studies have demonstrated that preterm and term children display differential associations between some white matter measures and executive functioning, cognitive functioning and motor outcome (58, 59). Therefore, as an exploratory analysis, we sought to assess whether associations between connectivity metrics and internalizing and externalizing symptoms differed by birth group (VP, FT).

METHODS AND MATERIALS

Participants

227 VP infants (< 30 weeks’ gestation and/or birthweight < 1250 g) admitted to the Royal Women’s Hospital in Melbourne, Australia between July 2001 and December 2003 were recruited into the Victorian Infant Brain Study (VIBeS; 65% of eligible VP infants admitted during recruitment period). 77 FT individuals (born between 37 and 42 weeks’ gestation and birth weight > 2500 g; 45 at birth and 31 at 2 years) were recruited from the Royal Women’s Hospital postnatal wards or via response to advertising in maternal and child health centers. Infants with congenital abnormalities likely to affect development were excluded from the study. Participant recruitment and loss at 7- and 13-year time points are presented in Figure 1. Data from 72 VP and 17 FT children at 7 years and 125 VP and 44 FT children at 13 years were included in the current study. The study was approved by the Royal Children’s Hospital and Royal Women’s Hospital Research Ethics committees and informed consent was obtained from children’s parents/legal guardians.

Figure 1. Flow chart of participant recruitment and loss at 7- and 13-year time points.

Figure 1.

Open in a new tab

89% of those initially recruited returned for 7-year follow up and 80% of those initially recruited returned for 13-year follow-up. Reasons for attrition included families moving out of state/internationally, withdrawing from the study or loss of contact. Imaging data that was not of sufficient quality were excluded primarily due to incomplete or incorrect acquisitions, non-uniform diffusion gradient directions (7-year only) or movement artefacts.

Behavior Problems

During the 7- and 13-year follow-up, parents completed the Strengths and Difficulties Questionnaire (SDQ; (60)) consisting of 25 items scored on a 3-point response scale (‘Not true’ = 0, ‘Somewhat true’ = 1, ‘Certainly true’ = 2). The 5-item emotional symptoms and 5-item peer problems subscales were combined to measure internalizing symptoms. Externalizing symptoms were measured by combining the 5-item conduct problems and 5-item hyperactivity subscales. Higher scores on both internalizing and externalizing scales indicate a greater degree of behavior problems, with each scale demonstrating good convergent validity, discriminant validity and internal consistency (61).

Social Risk

Family disadvantage was assessed at 7 and 13 years using a social risk index (62). Scores (0 – 2) were given for each domain (family structure, education of primary caregiver, primary income earner employment status and occupation, language spoken at home and maternal age at birth) and summed to provide an overall risk score (0 – 12). Overall scores ≥2 were categorized as higher social risk, with scores <2 indicating lower social risk (62–64).

MRI Acquisition

At both time points, MRI data were acquired using a 3T Siemens Trio Scanner. Participants were not sedated during scans. At 7 years, T1-weighted images were acquired using a MP-RAGE sequence (repetition time (TR) = 1900 ms, echo time (TE) = 2.27 ms, voxel size = 0.8 mm isotropic) and dMRI data were acquired using a twice-refocused echo planar imaging sequence (45 gradient directions at b = 3000 s/mm2, six b = 0 volumes, TR = 7400 ms, TE = 106 ms, voxel size = 2.3 mm isotropic). At 13 years, T1-weighted images were acquired using a 3D multi-echo MP-RAGE sequence with prospective motion correction (TR = 2530 ms, TEs = 1.77, 3.51, 5.32, 7.2 ms, voxel size = 0.9 mm isotropic) and dMRI data were acquired using a multiband accelerated echo planar imaging pulse sequence (60 gradient directions at b = 2800 s/mm2, four b = 0 volumes, TR = 3200 ms, TE = 110 ms, voxel size = 2.4 mm isotropic, multi-band acceleration factor = 3).

Image Pre-processing

An overview of the structural connectome workflow is presented in Figure S1. Bias-corrected (65) and brain-extracted (66) T1-weighted images were parcellated into 66 cortical regions based on the Desikan-Killiany atlas (67) and 14 subcortical regions based on the ‘aseg’ subcortical segmentation tool (68) using FreeSurfer version 6.0 (69) to define nodes in the connectome matrices. Intracranial volume (ICV; combined white matter + gray matter + cerebrospinal fluid volumes) used for secondary analysis was obtained with Statistical Parametric Mapping version 12 (http://www.fil.ion.ucl.ac.uk/spm/). All structural image output was visually inspected and participants with severe levels of movement artefact were excluded. Manual editing was performed according to FreeSurfer guidelines as required.

dMRI data were pre-processed predominantly using MRtrix3 (70), MRtrix3Tissue (https://3Tissue.github.io, a fork of MRtrix3), and the FSL package (71). Preprocessing included Gibbs-ringing correction (72), motion (between-volume, within-volume and outlier correction) and distortion correction (73–76) and brain extraction (66). Quality assessment was performed by visual inspection and automatically using the Quality Assessment for dMRI (QUAD) and Study-wise Quality Assessment for dMRI (SQUAD) tools (77). Participants whose diffusion images had severe levels of movement artefact were excluded. Quality control metrics generally did not differ between VP and FT groups (Table S1). 3-tissue response functions were estimated and averaged (78), images were upsampled to 1.5mm isotropic voxels, and Single-Shell 3-tissue Constrained Spherical Deconvolution (SS3T-CSD; (79)), and global intensity normalization and bias field correction (80) were performed.

The b = 0 data were aligned to the T1-weighted images (intensity inverted to better match the b=0 contrast for the purpose of registration) using the FSL Linear Image Registration Tool (FLIRT) (81) and Advanced Normalization Tool (ANTS) (82). Cortical and subcortical regions were aligned to the dMRI data by applying the inverse of the transformation matrices.

Whole-brain tractography was performed (83) (iFOD2, step size = 0.75 mm, curvature radius threshold = 1.9 mm [30° per mm;(84)], min streamline length = 10mm) based on white matter fiber orientation distributions from SS3T-CSD. Dynamic seeding within the white matter was performed (85) with cortical and subcortical regions as inclusion regions. A series of anatomical constraints were applied to improve the biological plausibility of reconstructed tracts to ensure streamlines did not enter the cerebrospinal fluid or propagate through and beyond the gray matter. Streamlines were terminated upon reaching the gray matter or exiting the brain mask. Spurious interhemispheric connections between deep gray matter regions were also discarded. Spherical deconvolution Informed Filtering of Tractograms (SIFT2) (85) was performed on the whole brain tractograms.

Connectivity Matrix Construction

Following pre-processing, streamlines were assigned to the relevant nodes. This resulted in an undirected, weighted 80 × 80 connectivity matrix for each subject (ωij) (7 and 13 years separately). In the matrix, each node was represented along rows (i) and columns (j) and each matrix element represented an edge or the connectivity between each node pair (sum of streamline weights). Connectome edge weights were multiplied by the SIFT2 proportionality coefficient for inter-subject normalization (85).

Graph Metrics

The Brain Connectivity toolbox (brain-connectivity-toolbox.net) in MATLAB was used to calculate weighted versions of global efficiency, local efficiency, characteristic path length, average clustering coefficient, transitivity and small-worldness. Descriptions and formulas for each metric used are presented in Table S2 (31, 39, 42, 86–89).

Statistical Analysis

Statistical analysis was conducted using Stata 15.0 (StataCorp, TX). At both 7 and 13 years, associations between connectivity measures and concurrent internalizing and externalizing symptoms were assessed using linear regressions fitted via generalized estimating equations (GEEs) to allow for clustering for multiple births (90). A group-by-connectivity interaction term was included to examine whether associations varied by group; if there was strong evidence that associations varied by group (interaction p < .05), associations were presented separately for each group, otherwise the groups were collapsed. All models were adjusted for sex, age at assessment and higher social risk based on the assumed causal diagram showed in Figure S2. Where there was strong evidence of an association, the following secondary analyses were conducted: i) adjusted for ICV to determine if associations were driven by brain size, and ii) excluding participants with an IQ < 70 (n = 9 at 13 years) to determine if findings were driven by a small proportion of children with intellectual impairment. A false discovery rate correction was applied via the Benjamini and Yekutieli method (91) for all analyses to account for multiple comparisons across graph theory metrics.

RESULTS

Participant characteristics.

Characteristics of participants included in the study are summarized in Table 1. As expected, VP participants at 7 and 13 years were less likely to be a singleton, more likely to have had bronchopulmonary dysplasia or infection, and more likely to have been administered antenatal corticosteroids than FT participants. Groups were similar in all other perinatal characteristics. On average, the VP group displayed slightly higher internalizing symptoms compared with the FT group at 7 and 13 years. VP children also displayed slightly higher externalizing symptoms compared with FT children at 7 years, while externalizing symptoms at 13 years were similar between birth groups. The VP group had smaller ICV and lower IQ at 7 and 13 years than the FT group. A greater number of the VP group had an IQ < 70 than the FT group at 13 years. On average, VP participants included at 7 years were less likely to be a singleton, have had infection or moderate/severe white matter abnormality (WMA) and had lower externalizing symptoms compared with VP children excluded at 7 years because they did not have high quality MRI data available for connectome analysis (Table S3). VP participants included at 13 years also had less infection and moderate/severe WMA than VP children excluded at 13 years (Table S3).

Table 1.

Sample Characteristics

7 years 13 years
VP n = 72 FT n = 17 VP n = 125 FT n = 44
GA at birth (weeks), M (SD), range 27.5 (1.7) 24–31 39 (1.2) 38–40 27.4 (1.9) 22–32 39 (1.4) 37–42
Birthweight (g), M (SD), range 1004 (209) 560–1390 3292 (539) 2482–4140 964 (232) 414–1425 3296 (542) 2220–4290
Male, n (%) 33 (46) 8 (47) 67 (54) 21 (48)
SGA,a n (%) 4 (6) 1 (6) 11 (9) 0b
Singleton, n (%) 32 (44) 15 (88) 65 (52) 40 (91)
BPD,c n (%) 19 (26) 0 40 (32) 0b
Antenatal corticosteroids,d n (%) 65 (90) 0 113 (90) 0b
Postnatal corticosteroids,e n (%) 2 (3)f 0 10 (8)g 0b
Infection,h n (%) 17 (25)i 0 40 (33)j 0b
Cystic PVL, n (%) 2 (3) 0 4 (3) 0b
Grade III/IV IVH,k n (%) 2 (3) 0 6 (5) 0b
Moderate/severe WMA,l n (%) 6 (9)i 0 16 (13) 0b
Age at assessment (years),m M (SD) 7.6 (.2) 7.6 (.2) 13.2 (.38) 13.2 (.46)
ICV (cm3), M (SD) 1350.8 (114) 1438.8 (110.9) 1443.7 (131.8) 1523.7 (157.8)
Internalizing score,n M (SD) 3.9 (3) 2.5 (2.6) 5.0 (3.4) 4.3 (2.6)
Externalizing score,o M (SD) 5.1 (3.5) 3.8 (2.8) 5.4 (3.6) 5.7 (3.4)
IQ,p M (SD) 99.9 (12.7) 110.4 (10.2) 99.5 (18.2) 110.1 (12.4)
IQ < 70,p n (%) 0 0 9 (7) 0
Higher social risk,q n (%) 38 (53) 4 (23.5) 77 (62) 16 (36.4)
Open in a new tab

Note:

a

Birthweight more than two standard deviations below the mean.

b

n = 24.

c

Oxygen requirement at 36 weeks.

d

Typical regime: Betamethasone Chronodose, 11.4 mg intramuscularly, full course: two doses 24 hours apart; part course: single dose.

e

Postnatal dexamethasone, usual dose 0.15 mg/kg per day for 3 days, reducing over 10 days: total dose 0.89 mg/kg.

f

n = 71.

g

n = 124.

h

Proven necrotizing enterocolitis and/or sepsis.

i

n = 68.

j

n = 121.

k

Graded according to Papile et al.

l

Scored using Kidokoro system.

m

Corrected for prematurity.

n

Calculated using the Strengths and Difficulties Questionnaire (SDQ; emotional symptoms + peer problems subscales).

o

Calculated using the SDQ (hyperactivity + conduct problems subscales).

p

Wechsler Abbreviated Scale of Intelligence (WASI) full scale IQ score administered at 7 years, Kaufman Brief Intelligence Test, 2nd Edition (K-BIT 2) composite standard score administered at 13 years (M = 100, SD =15).

q

Family social risk score ≥ 2.

BPD: bronchopulmonary dysplasia; FT: full-term; GA: gestational age; ICV: intracranial volume; IQ: intelligence quotient; M: mean; PVL: periventricular leukomalacia; SGA: small for gestational age; SD: standard deviation; VP: very preterm; WMA: white matter abnormality.

Relationship between the structural connectome and concurrent internalizing symptoms

At 7 years of age, there was strong evidence that lower global efficiency was associated with higher internalizing symptoms (Figure 2a; Table S4) and that higher characteristic path length was associated with higher internalizing symptoms (Figure 2b; Table S4). The evidence for these relationships persisted after adjusting for ICV (Table S4). There was also evidence that lower local efficiency was associated with higher internalizing at 7 years (Figure 2c; Table S4) which further weakened after adjustment for ICV (Table S4). There was little evidence that associations differed by group (group-by-connectivity interaction’s p > 0.466; Table S4).

Figure 2. Relationship between (a) global efficiency, (b) characteristic path length and (c) local efficiency and internalizing symptoms in very preterm and full-term groups combined at 7 years.

Figure 2.

Open in a new tab

There was little evidence that associations differed between very preterm and full-term groups, therefore combined group associations are presented. Adjusted for sex, age at assessment and higher social risk at 7 years. Shading represents 95% confidence intervals.

At 13 years of age, there was little evidence that structural connectivity metrics were associated with internalizing symptoms or that these relationships varied by birth group (combined p > 0.242; group-by-connectivity interaction’s p > 0.179; Table S5).

Relationship between structural connectome and concurrent externalizing symptoms

At 7 years, there was little evidence that connectivity metrics were associated with externalizing symptoms or that these relationships varied by groups (combined p > 0.625; group-by-connectivity interaction’s p > 0.057; Table S6). The exception was for characteristic path length and externalizing symptoms, whereby there was a weak positive association in the VP group and a weak negative association FT group (group-by-connectivity interaction p = 0.013; Figure 3 and Table S6). This finding was similar after adjusting for ICV (Table S6).

Figure 3. Relationship between characteristic path length and externalizing symptoms in very preterm and full-term children at 7 years.

Figure 3.

Open in a new tab

There was evidence that the association differed between very preterm and full-term groups, therefore associations are presented separately. Adjusted for sex, age at assessment and higher social risk at 7 years. Shading represents 95% confidence intervals.

At 13 years, there was strong evidence that associations between connectivity metrics (average clustering coefficient and transitivity) and externalizing symptoms differed by group (group-by-connectivity interaction: average clustering coefficient p = 0.034; transitivity p = 0.043). Higher average clustering coefficient and transitivity were related to higher externalizing symptoms in the VP group, but not in the FT group (Figure 4; Table S7). These findings were similar after adjusting for ICV and following exclusion of participants with an IQ < 70 (Table S7). There was weak evidence of a positive association between local efficiency and externalizing symptoms in the VP group and weak evidence of a negative association in the FT group (group-by-connectivity interaction p = 0.016; Figure 4; Table S7), however the evidence for this interaction did not persist after adjusting for ICV (Table S7).

Figure 4. Relationship between (a) average clustering coefficient, (b) transitivity and (c) local efficiency and externalizing symptoms in very preterm and full-term adolescents at 13 years.

Figure 4.

Open in a new tab

There was evidence that the association differed between very preterm and full-term groups, therefore associations are presented separately. Adjusted for sex, age at assessment and high social risk at 13 years. Shading represents 95% confidence intervals.

DISCUSSION

In a large prospective longitudinal cohort of infants born VP and FT, we found that regardless of birth group, lower network integration was associated with higher concurrent internalizing symptoms at 7 years. However, this relationship was not evident at 13 years. There was also evidence for associations between network integration and externalizing symptoms at 7 years, and between network segregation and concurrent externalizing at 13 years differing by group.

For all children (VP and FT), higher characteristic path length and lower global efficiency were associated with higher internalizing symptoms at 7 years, even after adjusting for brain size. Both measures assess the integration between sub-networks of local, functionally specialized regions and are influenced by long-range connections (31, 54). Maturation of long-range white matter fibers, which include increases in fiber density and myelin thickness, reshape the structural connectome across development, increasing integration between brain regions to support higher order functioning (54). Therefore greater path length between regions and lower global efficiency may reflect weaker, or less mature long-range connections in those with internalizing problems and thus less efficient information transfer across the brain. While VP and FT children displayed similar associations between measures of network integration and internalizing symptoms, poor network integration reported in VP children in the current cohort (52) and in other preterm cohorts (48, 51) may provide a mechanism by which internalizing symptoms are increased in this population. Whether network topology is associated with internalizing symptoms in children in the general population remains to be seen. In adults, one study has found lower network integration in those with generalized anxiety disorder compared to healthy controls (92); however this is not replicated in adults with major depressive disorder (93).

We also report an association between lower local efficiency and higher internalizing symptoms in VP and FT children at 7 years, which may reflect weakened short-range connectivity between local brain regions (42), and is consistent with findings in adults with major depressive disorder (93). However, this relationship weakened following adjustment for brain size and thus should be interpreted with caution.

Despite higher mean symptom scores at 13 years than 7 years in both groups, associations between network integration and internalizing symptoms were not evident at 13 years in VP or FT adolescents. It is possible that in a more mature brain, internalizing symptoms may be more strongly associated with the microstructure of specific long-range tracts with protracted maturation, rather than global measures of network integration.

The microstructure of the cingulum and uncinate fasciculus, which continue to mature well into adolescence and are involved in executive control and emotional processing, appear to be altered in individuals born preterm compared with FT peers in adolescence, and thus may provide a more sensitive marker of internalizing problem at this time (21, 23, 94). Alternatively, it is possible that socio-environmental factors may influence the presentation of internalizing symptoms to a greater extent in adolescence compared with childhood. The second follow-up in the current study at 13 years of age occurred during a sensitive period of development marked by major life events, including a transition to secondary education, relationship changes and increased independence from parents. Puberty may also play a role, with studies suggesting a positive relationship between stage of pubertal development and white maturation (95–97). Therefore, future research should investigate the role of white matter organization in psychopathology during adolescence in the context of socio-environmental factors and pubertal status.

At 7 years, there was some evidence that the association between characteristic path length and externalizing symptoms differed by group; high path length (lower integration) was associated with higher externalizing symptoms in the VP group, while lower path length (higher integration) was associated with higher externalizing symptoms in the FT group, although evidence for these relationships were weak. It is important to note that literature investigating links between brain measures and externalizing symptoms in the preterm population is scarce (98). Similar to VP children in the current study, children and adolescents with ADHD display lower network integration than healthy controls (99, 100). Perhaps lower integration and less efficient information transfer across the brain found in preterm cohorts (48, 52), which could reflect delayed or disrupted white matter maturation at a global level (54), is an indicator of shared biological mechanisms involved in internalizing and externalizing problems following VP birth.

At 13 years, increased clustering (average clustering coefficient, transitivity) was associated with higher externalizing symptoms in the VP group. These findings persisted beyond the influence of brain size and after excluding participants with low IQ, suggesting an independent role of network segregation in externalizing symptoms in VP adolescents. Both measures assess connectivity between nearby brain regions, thus VP adolescents with high externalizing symptoms appear to have increased local, short-range connections in the brain. The disparity in findings across the two time points assessed, that is, that lower network integration was associated with higher externalizing symptoms at 7 years, but higher network segregation was associated with higher externalizing symptoms in VP adolescents at 13 years warrants further investigation. Lower integration and higher segregation may be an indication of delayed development of the structural connectome, with the maturation of the human connectome postulated to develop from local to distributed organization across childhood and adolescence (55, 101). Our findings at 7 and 13 years are consistent with studies in clinical populations, with similar increases in clustering and decreases in global efficiency found in youth with ADHD compared with healthy controls in two cohorts aged 8–14 years (99) and 9–17 years (100). There was also evidence that associations between local efficiency and externalizing symptoms also differed by group at 13 years and persisted beyond the influence of brain size, however evidence of associations within VP and FT groups was weak. Small-worldness, or the extent to which the brain network reflects a small world topology of highly clustered sub-networks with robust connections linking sub-networks (39), is preserved in the VP brain (48, 52) and was not associated with externalizing or internalizing problems in the current study. Thus, the measure of small-worldness may not be sensitive to the alterations to network integration and segregation observed in the VP brain (48, 52).

A major strength of our study is the use of a large prospective longitudinal cohort of VP and FT-born individuals, which enabled investigation into the role of the structural connectome in psychopathology in childhood and early adolescence. However, the FT group in the current study was relatively small, which may have affected statistical power to find group differences in associations. Longitudinal structural connectome analyses (such as group × time interactions) were not performed in the current study due to updated dMRI sequences over time; future work incorporating identical sequences over time or scan harmonization techniques may be beneficial to improve understanding of longitudinal changes in the structural connectome. Currently, there is no consensus on the optimal brain parcellation scheme, however studies have indicated that the parcellation scheme used may influence connectome findings (102, 103) and thus should be considered when comparing connectome findings across studies. Subject motion is an inherent challenge in pediatric neuroimaging studies, however we minimized this through quality control procedures, which reduced the sample size particularly at 7 years. Our finding that VP children excluded at 7 years (due to poor quality or no MRI data available) had greater externalizing symptoms compared with VP children included in the current study at 7 years reflects the challenges of scanning this subset of children at a young age and may have limited our ability to fully characterize relationships with externalizing symptoms at 7 years. Finally, in addition to the parent-report of the SDQ, future studies should include the self-report version in adolescence to gain greater insight into an individual’s emotional state. It would be interesting to continue to explore associations between connectivity measures and self-reported internalizing symptoms in adolescence.

CONCLUSION

In the current study, we applied a graph theory approach to provide novel insights into the neurobiological basis of psychopathology in childhood and adolescence. We found that lower network integration, potentially reflecting weaker long-range connections and less efficient information transfer across the brain, was associated with higher internalizing symptoms at 7 years, but not at 13 years. There was also disparity in associations with externalizing symptoms at 7 and 13 years. Integration again appeared to be the most strongly associated with externalizing symptoms at 7 years, however there was little evidence for associations within either birth group. At 13 years, high clustering was associated with higher externalizing symptoms, but only in the VP group, suggesting a distinct biological basis of externalizing problems in this group. Future studies incorporating stratification techniques and assessment of socio-environmental factors may help to clarify the extent to which variability in degree of compromise and/or socio-environmental factors may contribute to the likely multifactorial relationship between brain connectivity and psychopathology in individuals born VP.

Supplementary Material

1

Acknowledgements:

We acknowledge the contributions of the Victorian Infant Brain Studies (VIBeS) and Developmental Imaging groups at the Murdoch Children’s Research Institute and Michael Kean and Radiographers at the Royal Children’s Hospital for the magnetic resonance images. We thank families involved in this study.

Financial Support:

This study was funded in part by an Australian Government Research Training Program Scholarship (to CG), the Australian National Health and Medical Research Council (NHMRC; Centre for Clinical Research Excellence 546519, 1060733, 1153176; Project Grants 237117, 491209 & 1066555; Career Development Fellowships 1085754, 1160003 to DKT and 1141354 to JLYC; Early Career Fellowship 1012236 to DKT; Senior Research Fellowship 1081288 and Leadership Fellowship 1176077 to PJA), Financial Markets Foundation for Children Project Grant 2019-083 (to AC, PJA, DKT, MT), RMIT Vice Chancellor’s Senior Research Fellowship and Australian Research Council Future Fellowship (to MT), Brigham and Women’s Hospital Program for Interdisciplinary Neuroscience Fellowship (to LGM), US National Institutes of Health (HD058056), Murdoch Children’s Research Institute, the Royal Children’s Hospital, The Royal Children’s Hospital Foundation, Department of Paediatrics, The University of Melbourne and the Victorian Government’s Operational Infrastructure Support Program.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Disclosures: The authors report no biomedical financial interests or potential conflicts of interest.

References

  • 1.Spittle AJ, Treyvaud K, Doyle LW, Roberts G, Lee KJ, Inder TE, et al. (2009): Early emergence of behavior and social-emotional problems in very preterm infants. J Am Acad Child Adolesc Psychiatry 48(9):909–918. [DOI] [PubMed] [Google Scholar]
  • 2.Linsell L, Johnson S, Wolke D, Morris J, Kurinczuk JJ, Marlow N. (2019): Trajectories of behavior, attention, social and emotional problems from childhood to early adulthood following extremely preterm birth: a prospective cohort study. Eur Child Adolesc Psychiatry 28(4):531–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hall J, Wolke D. (2012): A comparison of prematurity and small for gestational age as risk factors for age 6–13 year emotional problems. Early Hum Dev 88(10):797–804. [DOI] [PubMed] [Google Scholar]
  • 4.Samuelsson M, Holsti A, Adamsson M, Serenius F, Hägglöf B, Farooqi A. (2017): Behavioral patterns in adolescents born at 23 to 25 weeks of gestation. Pediatrics 140(1). [DOI] [PubMed] [Google Scholar]
  • 5.Mathewson KJ, Chow CH, Dobson KG, Pope EI, Schmidt LA, Van Lieshout RJ. (2017): Mental health of extremely low birth weight survivors: A systematic review and meta-analysis. Psychol Bull 143(4):347–83. [DOI] [PubMed] [Google Scholar]
  • 6.Robinson R, Lahti-Pulkkinen M, Schnitzlein D, Voit F, Girchenko P, Wolke D, et al. (2020): Mental health outcomes of adults born very preterm or with very low birth weight: A systematic review. Semin Fetal Neonatal Med 25(3):101113. [DOI] [PubMed] [Google Scholar]
  • 7.Pyhälä R, Wolford E, Kautiainen H, Andersson S, Bartmann P, Baumann N, et al. (2017): Self-reported mental health problems among adults born preterm: a meta-analysis. Pediatrics 139(4). [DOI] [PubMed] [Google Scholar]
  • 8.Lean RE, Lessov-Shlaggar CN, Gerstein ED, Smyser TA, Paul RA, Smyser CD, et al. (2020): Maternal and family factors differentiate profiles of psychiatric impairments in very preterm children at age 5-years. J Child Psychol Psyc 61(2):157–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Rogers CE, Lenze SN, Luby JL. (2013): Late preterm birth, maternal depression, and risk of preschool psychiatric disorders. J Am Acad Child Psy 52(3):309–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Hack M, Youngstrom EA, Cartar L, Schluchter M, Taylor GH, Flannery DJ, et al. (2005): Predictors of internalizing symptoms among very low birth weight young women. J Devl Behav Pediatr 26(2):93–104. [DOI] [PubMed] [Google Scholar]
  • 11.Treyvaud K, Doyle LW, Lee KJ, Roberts G, Cheong JL, Inder TE, et al. (2011): Family functioning, burden and parenting stress 2 years after very preterm birth. Early Hum Dev 87(6):427–31. [DOI] [PubMed] [Google Scholar]
  • 12.Treyvaud K, Ure A, Doyle LW, Lee KJ, Rogers CE, Kidokoro H, et al. (2013): Psychiatric outcomes at age seven for very preterm children: rates and predictors. J Child Psychol Psyc 54(7):772–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Van den Bergh BRH, Van den Heuvel MI, Lahti M, Braeken M, de Rooij SR, Entringer S, et al. (2020): Prenatal developmental origins of behavior and mental health: The influence of maternal stress in pregnancy. Neurosci Biobehav Rev 117:26–64. [DOI] [PubMed] [Google Scholar]
  • 14.Williams JH, Ross L. (. 2007): Consequences of prenatal toxin exposure for mental health in children and adolescents: a systematic review. Eur Child Adolesc Psychiatry 16(4):243–53. [DOI] [PubMed] [Google Scholar]
  • 15.Volpe JJ. (2009): Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. Lancet Neurol. 8(1):110–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Inder TE, Warfield SK, Wang H, Huppi PS, Volpe JJ. (2005): Abnormal cerebral structure is present at term in premature infants. Pediatrics 115(2):286–94. [DOI] [PubMed] [Google Scholar]
  • 17.de Kieviet JF, Zoetebier L, van Elburg RM, Vermeulen RJ, Oosterlaan J. (2012): Brain development of very preterm and very low-birthweight children in childhood and adolescence: a meta-analysis. Dev Med Child Neurol 54(4):313–23. [DOI] [PubMed] [Google Scholar]
  • 18.Young JM, Vandewouw MM, Morgan BR, Smith ML, Sled JG, Taylor MJ. (2018): Altered white matter development in children born very preterm. Brain Struct Funct 223(5):2129–41. [DOI] [PubMed] [Google Scholar]
  • 19.Eikenes L, Lohaugen GC, Brubakk AM, Skranes J, Haberg AK. (2011): Young adults born preterm with very low birth weight demonstrate widespread white matter alterations on brain DTI. Neuroimage 54(3):1774–85. [DOI] [PubMed] [Google Scholar]
  • 20.Brenner RG, Smyser CD, Lean RE, Kenley JK, Smyser TA, Cyr PEP, et al. (2020): Microstructure of the Dorsal Anterior Cingulum Bundle in Very Preterm Neonates Predicts the Preterm Behavioral Phenotype at 5 Years of Age. Biol Psychiatry 89(5):433–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Constable RT, Ment LR, Vohr BR, Kesler SR, Fulbright RK, Lacadie C, et al. (2008): Prematurely born children demonstrate white matter microstructural differences at 12 years of age, relative to term control subjects: an investigation of group and gender effects. Pediatrics 121(2):306–16. [DOI] [PubMed] [Google Scholar]
  • 22.Pannek K, Fripp J, George JM, Fiori S, Colditz PB, Boyd RN, et al. (2018): Fixel-based analysis reveals alterations is brain microstructure and macrostructure of preterm-born infants at term equivalent age. Neuroimage Clin 18:51–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Kelly CE, Thompson DK, Genc S, Chen J, Yang JY, Adamson C, et al. (2020): Long-term development of white matter fibre density and morphology up to 13 years after preterm birth: A fixel-based analysis. Neuroimage 220:117068. [DOI] [PubMed] [Google Scholar]
  • 24.Thompson DK, Matthews LG, Alexander B, Lee KJ, Kelly CE, Adamson CL, et al. (2020): Tracking regional brain growth up to age 13 in children born term and very preterm. Nat Commun 11(1):696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Cismaru AL, Gui L, Vasung L, Lejeune F, Barisnikov K, Truttmann A, et al. (2016): Altered Amygdala Development and Fear Processing in Prematurely Born Infants. Front Neuroanat 10:55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Peterson BS, Vohr B, Staib LH, Cannistraci CJ, Dolberg A, Schneider KC, et al. (2000): Regional brain volume abnormalities and long-term cognitive outcome in preterm infants. JAMA. 284(15):1939–47. [DOI] [PubMed] [Google Scholar]
  • 27.Omizzolo C, Thompson DK, Scratch SE, Stargatt R, Lee KJ, Cheong J, et al. (2013): Hippocampal volume and memory and learning outcomes at 7 years in children born very preterm. J Int Neuropsychol Soc 19(10):1065–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Nosarti C, Al-Asady MH, Frangou S, Stewart AL, Rifkin L, Murray RM. (2002): Adolescents who were born very preterm have decreased brain volumes. Brain 125(7):1616–23. [DOI] [PubMed] [Google Scholar]
  • 29.Bullmore E, Sporns O. (2009): Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–98. [DOI] [PubMed] [Google Scholar]
  • 30.Bullmore E, Sporns O. (2012): The economy of brain network organization. Nat Rev Neurosci 13(5):336–49. [DOI] [PubMed] [Google Scholar]
  • 31.Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69. [DOI] [PubMed] [Google Scholar]
  • 32.Griffa A, Baumann PS, Thiran JP, Hagmann P. (2013): Structural connectomics in brain diseases. Neuroimage 80:515–26. [DOI] [PubMed] [Google Scholar]
  • 33.Basser PJ. (1995): Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed 8(7):333–44. [DOI] [PubMed] [Google Scholar]
  • 34.Basser PJ, Mattiello J, LeBihan D. (1994): Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson, Series B 103(3):247–54. [DOI] [PubMed] [Google Scholar]
  • 35.Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. (2012): NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4):1000–16. [DOI] [PubMed] [Google Scholar]
  • 36.Raffelt D, Tournier JD, Rose S, Ridgway GR, Henderson R, Crozier S, et al. (2012): Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images. Neuroimage 59(4):3976–94. [DOI] [PubMed] [Google Scholar]
  • 37.Tournier JD, Yeh CH, Calamante F, Cho KH, Connelly A, Lin CP. (2008): Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. Neuroimage 42(2):617–25. [DOI] [PubMed] [Google Scholar]
  • 38.Tournier J-D, Calamante F, Connelly A. (2007): Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4):1459–72. [DOI] [PubMed] [Google Scholar]
  • 39.Watts DJ, Strogatz SH.(1998): Collective dynamics of ‘small-world’networks. Nature 393(6684):440. [DOI] [PubMed] [Google Scholar]
  • 40.Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol. 2007;3(2):e17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Bassett DS, Bullmore E. (2006): Small-world brain networks. Neuroscientist 12(6):512–23. [DOI] [PubMed] [Google Scholar]
  • 42.Latora V, Marchiori M. (2001): Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701. [DOI] [PubMed] [Google Scholar]
  • 43.Fortunato S (2010): Community detection in graphs. Phys Rep 486(3–5):75–174. [Google Scholar]
  • 44.van den Heuvel MP, Kahn RS, Goni J, Sporns O. (2012): High-cost, high-capacity backbone for global brain communication. Proc Natl Acad Sci U S A 109(28):11372–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.van den Heuvel MP, Sporns O. (2011): Rich-Club Organization of the Human Connectome. J Neurosci 31(44):15775–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Ball G, Aljabar P, Zebari S, Tusor N, Arichi T, Merchant N, et al. (2014): Rich-club organization of the newborn human brain. Proc Natl Acad Sci U S A 111(20):7456–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Karolis VR, Froudist-Walsh S, Brittain PJ, Kroll J, Ball G, Edwards AD, et al. (2016): Reinforcement of the Brain’s Rich-Club Architecture Following Early Neurodevelopmental Disruption Caused by Very Preterm Birth. Cereb Cortex 26(3):1322–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Fischi-Gomez E, Munoz-Moreno E, Vasung L, Griffa A, Borradori-Tolsa C, Monnier M, et al. (2016): Brain network characterization of high-risk preterm-born school-age children. Neuroimage Clin 11:195–209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Fischi-Gómez E, Vasung L, Meskaldji DE, Lazeyras F, Borradori-Tolsa C, Hagmann P, et al. (2015): Structural brain connectivity in school-age preterm infants provides evidence for impaired networks relevant for higher order cognitive skills and social cognition. Cereb Cortex 25(9):2793–805. [DOI] [PubMed] [Google Scholar]
  • 50.van den Heuvel MP, Kersbergen KJ, de Reus MA, Keunen K, Kahn RS, Groenendaal F, et al. (2015): The Neonatal Connectome During Preterm Brain Development. Cereb Cortex 25(9):3000–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Lee JY, Park HK, Lee HJ.(2019): Accelerated Small-World Property of Structural Brain Networks in Preterm Infants at Term-Equivalent Age. Neonatology 115(2):99–107. [DOI] [PubMed] [Google Scholar]
  • 52.Thompson DK, Chen J, Beare R, Adamson CL, Ellis R, Ahmadzai ZM, et al. (2016): Structural connectivity relates to perinatal factors and functional impairment at 7 years in children born very preterm. Neuroimage 134:328–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Zhao T, Mishra V, Jeon T, Ouyang M, Peng Q, Chalak L, et al. (2019): Structural network maturation of the preterm human brain. Neuroimage 185:699–710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Hagmann P, Sporns O, Madan N, Cammoun L, Pienaar R, Wedeen VJ, et al. (2010): White matter maturation reshapes structural connectivity in the late developing human brain. Proc Natl Acad Sci U S A 107(44):19067–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, Miezin FM, et al. (2009): Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 5(5):e1000381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Lebel C, Walker L, Leemans A, Phillips L, Beaulieu C. (2008): Microstructural maturation of the human brain from childhood to adulthood. Neuroimage 40(3):1044–55. [DOI] [PubMed] [Google Scholar]
  • 57.Collin G, van den Heuvel MP. (2013): The ontogeny of the human connectome: development and dynamic changes of brain connectivity across the life span. Neuroscientist 19(6):616–28. [DOI] [PubMed] [Google Scholar]
  • 58.Vollmer B, Lundequist A, Mårtensson G, Nagy Z, Lagercrantz H, Smedler A-C, et al. (2017): Correlation between white matter microstructure and executive functions suggests early developmental influence on long fibre tracts in preterm born adolescents. PLoS One 12(6):e0178893. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.de Kieviet JF, Lustenhouwer R, Königs M, van Elburg RM, Pouwels PJ, Oosterlaan. (2021): Altered structural connectome and motor problems of very preterm born children at school-age. Early Hum Dev 152:105274. [DOI] [PubMed] [Google Scholar]
  • 60.Goodman A, Goodman R. (2009): Strengths and difficulties questionnaire as a dimensional measure of child mental health. J Am Acad Child Psych 48(4):400–3. [DOI] [PubMed] [Google Scholar]
  • 61.Goodman A, Lamping DL, Ploubidis GB. (2010): When to use broader internalising and externalising subscales instead of the hypothesised five subscales on the Strengths and Difficulties Questionnaire (SDQ): data from British parents, teachers and children. J Abnorm Child Psychol 38(8):1179–91. [DOI] [PubMed] [Google Scholar]
  • 62.Roberts G, Howard K, Spittle AJ, Brown NC, Anderson PJ, Doyle LW. (2008): Rates of early intervention services in very preterm children with developmental disabilities at age 2 years. J Paediatr Child Health 44(5):276–80. [DOI] [PubMed] [Google Scholar]
  • 63.Whitaker AH, Feldman JF, Van Rossem R, Schonfeld IS, Pinto-Martin JA, Torre C, et al. (1996): Neonatal cranial ultrasound abnormalities in low birth weight infants: relation to cognitive outcomes at six years of age. Pediatrics 98(4):719–29. [PubMed] [Google Scholar]
  • 64.Hack M, Breslau N, Aram D, Weissman B, Klein N, Borawski-Clark, et al. (1992): The effect of very low birth weight and social risk on neurocognitive abilities at school age. J Dev. Behav 13(6), 412–420. [PubMed] [Google Scholar]
  • 65.Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. (2010): N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Jenkinson M, Pechaud M, Smith S. (2005): BET2: MR-based estimation of brain, skull and scalp surfaces. Proceedings of the Eleventh Annual Meeting of the Organization for Human Brain Mapping, Toronto Canada. [Google Scholar]
  • 67.Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. (2006): An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3):968–80. [DOI] [PubMed] [Google Scholar]
  • 68.Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. (2002): Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–55. [DOI] [PubMed] [Google Scholar]
  • 69.Fischl B (2012): FreeSurfer. Neuroimage 62(2):774–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. (2019): MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202:116137. [DOI] [PubMed] [Google Scholar]
  • 71.Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. (2012): Fsl. Neuroimage 62(2):782–90. [DOI] [PubMed] [Google Scholar]
  • 72.Kellner E, Dhital B, Kiselev VG, Reisert M. (2016): Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 76(5):1574–81. [DOI] [PubMed] [Google Scholar]
  • 73.Andersson JLR, Graham MS, Drobnjak I, Zhang H, Filippini N, Bastiani M. (2017): Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. Neuroimage 152:450–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Andersson JLR, Graham MS, Zsoldos E, Sotiropoulos SN. (2016): Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. Neuroimage 141:556–72. [DOI] [PubMed] [Google Scholar]
  • 75.Andersson JLR, Sotiropoulos SN. (2016): An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125:1063–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Schilling KG, Blaber J, Hansen C, Cai L, Rogers B, Anderson AW, et al. (2020): Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps. PLoS One 15(7):e0236418. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, et al. (2019): Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage 184:801–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Dhollander T, Mito R Raffelt D Connelly A (2019): Improved white matter response function estimation for 3-tissue constrained spherical deconvolution. Proceedings of the 27th International Society of Magnetic Resonance in Medicine, Montréal, Québec, Canada. [Google Scholar]
  • 79.Dhollander T, Connelly A. (2016): A novel iterative approach to reap the benefits of multi-tissue CSD from just single-shell (+b=0) diffusion MRI data. Proceedings of the 24th International Society of Magnetic Resonance in Medicine, Singapore. [Google Scholar]
  • 80.Dhollander T, Tabbara R, Rosnarho-Tornstrand J, Tournier JD, Raffelt D, & Connelly A. (2021): Multi-tissue log-domain intensity and inhomogeneity normalisation for quantitative apparent fibre density. Proceedings of the 29th International Society of Magnetic Resonance in Medicine. [Google Scholar]
  • 81.Jenkinson M, Bannister P, Brady M, Smith S. (2002): Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2):825–41. [DOI] [PubMed] [Google Scholar]
  • 82.Avants BB, Epstein CL, Grossman M, Gee JC. (2008): Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Tournier JD, Calamante F, Connelly A. (2010): Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proceedings of the International Society for Magnetic Resonance in Medicine, Stockholm, Sweden. [Google Scholar]
  • 84.Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. (2008): Mapping the structural core of human cerebral cortex. PLoS Biol 6(7):e159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Smith RE, Tournier JD, Calamante F, Connelly A. (2015): SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119:338–51. [DOI] [PubMed] [Google Scholar]
  • 86.Wang Y, Ghumare E, Vandenberghe R, Dupont P. (2017): Comparison of different generalizations of clustering coefficient and local efficiency for weighted undirected graphs. Neural Comput 29(2):313–31. [DOI] [PubMed] [Google Scholar]
  • 87.Onnela J-P, Saramäki J, Kertész J, Kaski K. (2005): Intensity and coherence of motifs in weighted complex networks. Phys Rev. E 71(6):065103. [DOI] [PubMed] [Google Scholar]
  • 88.Newman ME. (2003): The structure and function of complex networks. SIAM Review 45(2):167–256. [Google Scholar]
  • 89.Humphries MD, Gurney K. Network. (2008): ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One 3(4):e0002051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Carlin JB, Gurrin LC, Sterne JA, Morley R, Dwyer T. (2005): Regression models for twin studies: a critical review. Int J Epidemiol 34(5):1089–99. [DOI] [PubMed] [Google Scholar]
  • 91.Benjamini Y, Yekutieli D. (2001): The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics 29(4):1165–88. [Google Scholar]
  • 92.Makovac E, Mancini M, Fagioli S, Watson DR, Meeten F, Rae CL, et al. (2018): Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory. Psychiatric Res. Neuroimaging 281:107–16. [DOI] [PubMed] [Google Scholar]
  • 93.Chen VC, Shen CY, Liang SH, Li ZH, Hsieh MH, Tyan YS, et al. (2017): Assessment of brain functional connectome alternations and correlation with depression and anxiety in major depressive disorders. PeerJ 5:e3147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Lebel C, Beaulieu C. (2011): Longitudinal development of human brain wiring continues from childhood into adulthood. J Neurosci 31(30):10937–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Genc S, Malpas CB, Gulenc A, Sciberras E, Efron D, Silk TJ, et al. (2020): Longitudinal patterns of white matter fibre density and morphology in children are associated with age and pubertal stage. Dev Cogn Neurosci 45:100853. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Herting MM, Maxwell EC, Irvine C, Nagel BJ. (2012): The impact of sex, puberty, and hormones on white matter microstructure in adolescents. Cereb Cortex 22(9):1979–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Menzies L, Goddings AL, Whitaker KJ, Blakemore SJ, Viner RM. (2015): The effects of puberty on white matter development in boys. Dev Cogn Neurosci 11:116–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Loe IM, Lee ES, Feldman HM. (2013): Attention and internalizing behaviors in relation to white matter in children born preterm. J Dev Behav Pediatr 34(3):156–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Cao Q, Shu N, An L, Wang P, Sun L, Xia MR, et al. (2013): Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder. J Neurosci 33(26):10676–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Beare R, Adamson C, Bellgrove MA, Vilgis V, Vance A, Seal ML, et al. (2017): Altered structural connectivity in ADHD: a network based analysis. Brain Imaging Behav 11(3):846–58. [DOI] [PubMed] [Google Scholar]
  • 101.Supekar K, Musen M, Menon V. (2009): Development of large-scale functional brain networks in children. PLoS Biol 7(7):e1000157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Bassett DS, Brown JA, Deshpande V, Carlson JM, Grafton ST. (2011): Conserved and variable architecture of human white matter connectivity. Neuroimage 54(2):1262–79. [DOI] [PubMed] [Google Scholar]
  • 103.Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M, Pantelis C, et al. (2010): Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50(3):970–83. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1749567-supplement-1.pdf (1.9MB, pdf)

RESOURCES