Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2015 Jun 8;10(6):e0129939. doi: 10.1371/journal.pone.0129939

Ontogenetic Shape Change in the Chicken Brain: Implications for Paleontology

Soichiro Kawabe 1,2,*, Seiji Matsuda 3, Naoki Tsunekawa 4, Hideki Endo 2
Editor: Peter Dodson5
PMCID: PMC4460028  PMID: 26053849

Abstract

Paleontologists have investigated brain morphology of extinct birds with little information on post-hatching changes in avian brain morphology. Without the knowledge of ontogenesis, assessing brain morphology in fossil taxa could lead to misinterpretation of the phylogeny or neurosensory development of extinct species. Hence, it is imperative to determine how avian brain morphology changes during post-hatching growth. In this study, chicken brain shape was compared at various developmental stages using three-dimensional (3D) geometric morphometric analysis and the growth rate of brain regions was evaluated to explore post-hatching morphological changes. Microscopic MRI (μMRI) was used to acquire in vivo data from living and post-mortem chicken brains. The telencephalon rotates caudoventrally during growth. This change in shape leads to a relative caudodorsal rotation of the cerebellum and myelencephalon. In addition, all brain regions elongate rostrocaudally and this leads to a more slender brain shape. The growth rates of each brain region were constant and the slopes from the growth formula were parallel. The dominant pattern of ontogenetic shape change corresponded with interspecific shape changes due to increasing brain size. That is, the interspecific and ontogenetic changes in brain shape due to increased size have similar patterns. Although the shape of the brain and each brain region changed considerably, the volume ratio of each brain region did not change. This suggests that the brain can change its shape after completing functional differentiation of the brain regions. Moreover, these results show that consideration of ontogenetic changes in brain shape is necessary for an accurate assessment of brain morphology in paleontological studies.

Introduction

The organ shape of extant and extinct vertebrate animals changes during maturation or with increases in size, and the cranial part is of particular interest to many researchers [1–8]. The embryonic growth patterns of the brain and other organs in Aves are well described [9–11]. Early development of the chicken brain was first described by Kamon [12] and gross development of the chicken embryonic brain was described by Rogers [9]. Although many studies focused on the changes in brain volume and/or brain regions during maturation, including both embryonic and post-hatching growth [13–18], post-hatching changes in the shape of the avian brain due to growth are still poorly understood.

Recent paleoneurological studies have made positive progress and brains (cranial endocasts) of many species from various taxa have been analyzed from various angles using CT [19–28]. Nevertheless, interspecific or ontogenetic variations in the brains of fossil taxa have been rarely examined, since multiple specimens of one species or a close taxon are rarely obtained. Hence, paleontologists are usually forced to discuss the phylogeny or neurosensory development based on the brain morphology of an extinct species without consideration of the size or developmental stage of the specimen [24, 29–38]. Can we develop arguments on the brain morphology of extinct species without knowledge of the variation in the shape of the brain during growth? It is noted that brain shape changes considerably based on brain size in birds [39]. This indicates that brain size is important in assessing brain morphology in birds. That is, taking the developmental stage into account is essential when we evaluate the brain morphology of an extinct species. Smaller birds tend to have round, modern, avian-type brains, while larger birds show anteroposteriorly elongated, reptilian-type brains [39]. Given the pattern of shape change based on size, the brain of some species should change shape from the round to the anteroposteriorly elongated type during ontogeny. If that shape change is observed in a single species, then the brain changes its shape considerably during growth. We could incorrectly interpret two size differentiated brains from the same species as distinct species, leading to a misinterpretation of the morphology of the avian brain. Additionally, since the morphological characters that show ontogenetic change contain phylogenetic signals and can affect the results of phylogenetic analyses [40–43], it is imperative we know how avian brain shape changes during growth.

Since, in general, though not always the mass of the neural tissue of a particular region of the brain is correlated with the ability and/or sensory development of animals [44–48], comparisons of brain sizes of extant animals are used to estimate the degree of evolution of different sensory systems [44, 46]. Based on this principle, the relationships between the brain regional volume and sensory abilities of extinct birds and other animals were discussed in many studies [36, 49, 50]. As noted above, some previous studies measured the volumetric changes in brain regions based on growth in birds [13–18]. However, none investigated the covariation between the volume and shape of avian brains. In paleontological studies, the sensory and locomotor capability of extinct species are often assessed from the volume or area of brain regions [35, 37, 51]. However, all the primary data about brain that we can obtain from fossil specimens is external appearance of entire brain, and we had to judge the degree of development in brain regions from external information of brain.

The aims of this study were to quantitatively describe developmental shape changes in the chicken brain and to compare the growth pattern of each brain region with changes in brain shape during development. We also investigated the relationship between the volume and shape of brain regions. Since the relationship between the volume and shape of avian brains has not yet been investigated, it is unclear whether we can assess the cognitive abilities of extinct birds from the appearance of the brain. To achieve these aims, brain shape was compared among various developmental stages in the chicken using 3D geometric morphometric analysis and the growth rates of brain regions (telencephalon, diencephalon and mesencephalon, cerebellum, and myelencephalon) were evaluated using simple regression analysis to explore post-hatching morphological changes in the chicken brain.

Materials and Methods

Chicken eggs

This study was carried out in strict accordance with the recommendations of the Guidelines of the Animal Care Committee of Ehime University. The protocol was approved by the Animal Care Committee of Ehime University (Permit Number: 05A-27-10). The broiler eggs were incubated at 38°C in a rocking, humidified incubator for 24 days. They were then manually turned several times per day. Forty-four eggs hatched in August 2011 and the chicks were raised for a maximum of 118 days (Table 1).

Table 1. Volume of the brain, including eyes, brain, brain and eyes, telencephalon, dien- and mesencephalon, and cerebellum of sampled specimens.

Specimen ID Day Body weight (g) Eyes (mm3) Telencephalon (mm3) Dien- Mesencephalon (mm3) Cerebellum (mm3) Myerencephalon (mm3) Whole brain (mm3) Eyes + Brain (mm3)
#1 119 5300 - 2216.875 994.5811 588.4555 201.2268 4001.139 -
20 282 - 685.255 343.3328 175.4841 55.11907 1259.191 -
#2 26 752 1533.867 1286.919 477.0751 273.0119 81.52103 2118.527 3652.394
15 286 1095.275 737.231 362.7126 265.9438 115.0294 1480.917 2576.192
#3 34 1140 1606.663 1229.209 617.3489 331.4657 137.6827 2315.706 3922.369
16 264 1234.698 799.3646 391.8207 242.8153 107.762 1541.763 2776.461
#4 22 436 1334.28 1176.198 429.1162 275.672 79.7655 1960.752 3295.032
17 286 - 906.6819 431.7303 237.4183 82.42563 1658.256 -
7 88 - 559.0485 262.747 146.744 62.24085 1030.78 -
#5 91 5500 - 2240.134 1042.686 711.5726 204.6842 4199.077 -
15 328 - 794.4812 441.9108 303.8449 92.30719 1632.544 -
11 202 987.7967 739.7763 334.4708 228.5717 108.13 1410.949 2398.745
2 48.2 - 517.3658 257.5618 145.2118 49.6468 969.7863 -
#6 34 1010 - 1352.357 491.6407 405.451 85.3164 2334.765 -
16 312 1092.37 722.5275 334.2332 227.7131 111.8327 1396.306 2488.676
#7 116 7400 5142.16 2312.18 1126.583 744.4218 340.458 4523.643 9665.802
13 268 1335.116 628.5874 355.1002 201.8324 115.7501 1301.27 2636.386
11 208 964.1316 598.1609 335.3371 218.9968 109.4562 1261.951 2226.083
1 51 - 489.6003 265.8774 148.6873 54.2146 958.3795 -
#8 20 470 1286.858 993.4312 427.0233 273.4105 96.30122 1790.166 3077.024
7 132 795.7233 582.1387 289.1413 200.3375 109.3489 1180.966 1976.69
2 56.6 595.4765 510.5206 243.49 143.5814 80.04221 977.6342 1573.111
1 55.2 711.1449 541.3035 249.6146 145.7596 73.52684 1010.204 1721.349
#9 35 1380 1971.116 1628.029 569.2136 358.8872 153.7968 2709.926 4681.043
#10 38 1910 1891.167 1449.371 657.1665 407.3291 143.8615 2657.728 4548.895
#11 40 1450 1898.25 1455.067 571.1915 420.9978 155.9203 2603.176 4501.427
#12 86 4960 5542.183 2395.319 967.382 629.653 308.4215 4300.775 9842.958
#13 94 6000 5486.382 2397.726 1008.74 593.4998 251.9072 4251.873 9738.255
#14 15 258 1259.551 811.0093 411.7908 238.2079 133.4894 1594.497 2854.049
#15 46 2160 2897.04 1696.694 698.5325 483.3612 206.9533 3085.541 5982.58
#16 20 450 1274.362 898.3335 392.0201 244.0494 137.3454 1671.748 2946.111
#17 42 1580 1931.73 1756.03 724.75 611.26 223.66 3315.7 5247.43
#18 26 698 1563.181 1176.528 474.415 292.4377 143.0413 2086.422 3649.603
#19 119 5200 2386.089 1864.205 854.2535 628.6717 124.7347 3471.865 5857.954
#20 32 1060 1603.704 1326.507 450.4125 348.0704 146.6827 2271.672 3875.377
#21 84 4700 - 1991.776 1060.793 710.4534 115.7807 3878.803 -
#22 13 124 793.0706 553.0153 224.7003 153.9424 78.00996 1009.668 1802.739
#23 30 780 1765.857 1249.248 526.8356 304.4888 124.0984 2204.671 3970.528
#24 33 990 1960.522 1389.039 576.2204 381.7245 135.6895 2482.673 4443.195
#25 24 562 - 946.8904 365.1811 206.7157 66.54919 1585.336 -
#26 8 150 834.3523 655.7941 327.9623 192.7327 92.4835 1268.973 2103.325
#27 14 292 1228.236 646.4417 378.0371 176.2891 84.07384 1284.842 2513.077
#28 12 250 - 755.5146 396.7193 235.7088 87.64622 1475.589 -
#29 5 66.2 723.5254 553.1523 260.8034 162.1793 48.1637 1024.299 -
#30 20 460 - 950.5855 406.9995 249.0784 77.49634 1684.16 -
#31 16 300 - 674.9442 298.2716 135.4672 45.2145 1153.897 -
#32 10 148 - 538.2504 264.4796 168.3853 71.91544 1043.031 -
#33 18 342 - 705.9381 301.3073 172.8546 49.79115 1229.891 -
#34 30 950 1726.883 1230.681 518.3032 293.258 143.6545 2185.896 3912.779
#35 32 970 - 1205.705 528.6908 421.8794 205.3434 2361.618 -
#36 22 560 1587.874 1168.908 478.7617 251.2326 89.5934 1988.495 3576.369
#37 28 686 - 1145.948 472.1611 340.159 85.89069 2044.159 -
#38 36 1160 2038.11 1372.135 509.3799 337.3302 114.0405 2332.886 4370.996
#39 35 1390 2272.37 1710.025 570.8695 434.0684 216.6279 2931.591 5203.96
#40 75 3000 3159.385 2212.237 938.9714 739.5308 177.9679 4068.707 7228.092
#41 30 940 - 1353.783 502.8561 349.8643 117.8582 2324.361 -
#42 4 68 589.8324 510.5782 276.2558 145.01 62.36681 994.2108 1584.043
#43 24 606 - 1052.859 418.9663 240.1014 90.32935 1802.256 -
#44 28 714 - 1183.389 516.2258 318.3184 97.02183 2114.955 -
Open in a new tab

μMRI and image acquisition

μMRI is a non-invasive method that allows for the differentiation of major brain regions from each other. Furthermore, it permits repeated viewing of the same living specimen. Hence, μMRI was used to acquire in vivo volumetric data and data from post-mortem chicken brain and eyes. μMRI was performed using a 1.5-T, MRmini SA (MRTechnology, Inc) at Ehime University (Toon, Japan). Both 30 and 38.5 mm diameter RF coils were used based on the size of the chicken. During scanning, the birds were anesthetized with isoflurane using a gas anesthesia system. When they reached the opening of the MRI apparatus, they were beheaded, and only the heads were scanned by MRI. That is, the decapitation had been at the final growth stage in each development. Images used to create the 3D chicken brain model were recorded using 3D spin-echo sequence mode acquisition, with a data matrix of 512 × 256 × 128 points (Fig 1).

Fig 1. MRI images acquired by μMRI, MRmini SA (MRTechnology, Inc).

Fig 1

Open in a new tab

Identified brain regions (telencephalon, diencephalon and mesencephalon, cerebellum, and myelencephalon) and eyes were manually labeled using the segmentation tools available in the Amira visualization software (v 5.3.2, Mercury Computer Systems, San Diego, CA, USA). The 3D models were created and brain volumes were calculated by using Amira visualization software. Details of the methods used to prepare and examine the 3D models were previously described by Corfield et al. [52].

3D geometric morphometrics

The 3D coordinates from 20 homologous landmarks of the brain were digitized (Table 2, Fig 2) from 43 specimens using Amira. Five of them (#1 to #8; Fig 3) were scanned several times at different developmental stages. The resulting 3D coordinate data set was subjected to generalized Procrustes analysis (GPA; [53]) using the MorphoJ software package [54]. In GPA, distances between homologous landmarks are minimized by translating, rotating, and scaling all objects to a common reference. That is, the effects of size, position, and orientation are eliminated so that remaining data reflect shape variation (Procrustes shape coordinates). Information on the absolute size of the specimen is preserved as centroid size (CS), which is calculated as the square root of the sum of squared distances of landmarks from their centroids [55].

Table 2. Landmarks used (Fig 2) and anatomical descriptions (refer to Fig 2) for profiled anatomical structures.

Number Anatomical description
1 Median anterior tip of the telencephalon
2 Median junction between the telencephalon and cerebellum
3 Median dorsal point of the foramen magnum
4 Median ventral point of the foramen magnum
5 Median junction between the mesencephalon and myelencephalon
6 Median junction between the hypophysis and mesencephalon
7 Median ventral tip of the hypophysis
8 Median point where the two optic nerves intersect
9 Median junction between the telencephalon and mesencephalon
10 Perpendicular at midpoint between landmarks 2 and 3 to dorsal margin of cerebellum in lateral view
11, 12 Perpendicular at midpoint between landmarks 1 and 2 to dorsal margin of telencephalon in lateral view, right and left
13, 14 Most lateral point of the widest part of the telencephalon, right and left
15, 16 Most lateral point of the widest part of the floccular lobe, right and left
17, 18 Intersection of the telencephalon, cerebellum, and optic lobe, right and left
19, 20 Intersection of the cerebellum, myelencephalon, and optic lobe, right and left
Open in a new tab

Fig 2. The three-dimensional brain landmarks used for shape analysis shown in dorsal (upper) and right lateral (lower) views.

Fig 2

Open in a new tab

Fig 3. Ontogenetic shape variations (#1 to #8) (not to scale).

Fig 3

Open in a new tab

Principal component analysis (PCA)

Procrustes shape coordinates were subjected to PCA to explore the patterns of major variation among chicken brains at various developmental stages (sizes) (Fig 4). The proportion of total variance contributed by each PC and the cumulative total for the first 10 PCs from the PCA are provided in Table 3. PCA was performed using MorphoJ, and Morphologika was used to illustrate the 3D profiles [55]. The scores of specimens along the PC axes and log CS were subjected to correlation and regression analyses to examine the effect of aging, namely, the effect of increasing size on brain shape (Table 4, Fig 5). Regression analysis was performed to determine whether size alone was responsible for the differences in shape observed along the PC axes. Regression coefficients are vectors representing the correlations between changes in shape and size. To explore how shape varies with growth, multivariate regressions of brain shape onto brain volume were performed (Fig 6). Statistical significance was tested using a permutation test against the null hypothesis of size independence.

Fig 4. PCA and variation in brain shape for each principal component (PC) score.

Fig 4

Open in a new tab

Table 3. Eigenvalues and explanatory proportion for the first 10 principal components (PCs) from the principal component analysis.

PC Eigenvalue Proportion (%) Cumulative (%)
1 0.00280665 23.383 23.383
2 0.00186082 15.503 38.885
3 0.00103005 8.581 47.467
4 0.00094377 7.863 55.33
5 0.0008114 6.76 62.089
6 0.0006503 5.418 67.507
7 0.00052655 4.387 71.894
8 0.00045568 3.796 75.69
9 0.0004347 3.622 79.312
10 0.00033285 2.773 82.085
Open in a new tab

Table 4. Results of the correlation (r) and regression (R 2, P) analysis between the PC scores and log centroid size (CS).

PC1 PC2 PC3 PC4
CS r 0.5259 0.2128 0.2296 -0.0224
R 2 0.2766 0.0453 0.0527 0.0005
P <0.0001 0.1709 0.0313 0.9847
Open in a new tab

Fig 5. Regression analysis of PC1 on log centroid size.

Fig 5

Open in a new tab

Fig 6. Multivariate regression of brain shape coordinates on log centroid size.

Fig 6

Open in a new tab

Growth rate

To explore the relationship between the growth of the brain and shape, the logarithmic volume of each brain region (telencephalon, diencephalon and mesencephalon, cerebellum, and myelencephalon) and both eyes were regressed on the log of body weight (Fig 7). Chickens were weighed on a scale before MRI scanning.

Fig 7. Relationship between body weight and eyes + brain (dark green circles), whole brain volume (brown triangles), eyes (light green circles), and brain regions (telencephalon, red triangles; cerebellum, yellow squares; diencephalon and mesencephalon, blue diamonds).

Fig 7

Open in a new tab

Results

The first three PCs from the PCA analysis accounted for 47.47% of the total shape variation and provided a reasonable approximation of the total variation in shape (Table 3; Fig 4). Since PC1 and PC2 were the only PC axes that accounted for more than 10% of the variance (Table 3; Fig 4), the following descriptions and discussions will be based on these two PCs.

The telencephalon rotates caudoventrally with increasing PC1 score (Fig 4). This change in shape leads to a relative caudodorsal rotation of the cerebellum and myelencephalon. With increasing PC1, all brain regions elongate rostrocaudally, which results in a more slender brain shape. In particular, the optic nerves elongate rostrodorsally. This extension of the optic nerve indicates that the orbit has a tendency to be located more caudally.

The PC2 axis mainly corresponds to lowering of the cerebellum (Fig 4). The telencephalon and myelencephalon change their posterior orientation under the influence of the rotation of the cerebellum.

Since PC1 explained a considerable degree of the shape variation, the shape change accompanying brain growth can be summarized as shape change with increasing PC1 score (Fig 4). The correlation between the PC1 score and log centroid size (CS) was significant (r = 0.5289, P < 0.001; Table 4, Fig 5). Thus, brain shape along the first dimension was affected by increases in size (Fig 5). The multivariate regression analysis of shape against size explained 20.20% of the shape variation and it revealed that the correlation between shape and size is significant (p < 0.001; Fig 6). Comparing larger brains to smaller brains (lower right to upper left in Fig 6), the length of the telencephalon and myelencephalon varies quite substantially. In addition, the optic nerve tends to elongate and rotate rostrodorsally with increasing brain size. These shape changes correspond to positive changes along the PC1 axis (Figs 3 and 5). That is, the ontogenetic changes of the brain are accurately reflected in these two patterns of shape change.

The growth rates for each brain region were constant and the slopes of the growth formula were parallel (Fig 7). However, eye size was relatively large, indicating that the eye exhibits tachyauxesis against the entire brain (Fig 7).

Discussion

Shape change

Changes in brain shape during post-hatching development in chickens can be described by positive shape changes in the PC1 axis and multivariate regression. The dominant pattern of shape change was as follows: (1) caudoventral rotation of the telencephalon, (2) caudodorsal rotation of the cerebellum and myelencephalon, (3) rostrocaudal elongation of the entire brain, and (4) extension of the optic nerve in the rostrodorsal direction (Figs 3 and 5). We explored the relationship between brain shape and brain size in Aves using various taxa (60 species from 22 orders) and discovered the dominant allometric shape change [39]. Brain posture and relative brain length dramatically change based on brain size in Aves [39]. The ontogenetic pattern of shape change described above (1)–(3) corresponds to interspecific shape changes with increasing brain size [39]. That is, the intraspecific and interspecific ontogenetic changes in brain shape with increasing size display similar patterns.

The anteriorly elongated optic nerve orients the eyeball and orbit more rostrally. The covariation pattern between brain shape and orbital shape was also discussed in Kawabe et al. [39]; the rostrally elongated orbit has a rostrocaudally elongated brain. In light of the covariation between brain shape and orbital shape described in Kawabe et al. [39], the orbital shape changes from a round type to an elongated type during growth.

Although PC1 explained most of the variation in shape change, the contribution from PC2 was relatively high (Fig 4) and was concentrated mainly in the cerebellum. The shape of the cerebellum is relatively variable due to factors other than size. Plots of each male and female were not deflected to one side of the PC2 axis (Fig 4). Hence, sexually dimorphic variation among brain shape in chickens is vanishingly small. Since the PC2 score did not significantly correlate with size (Table 4) and did not reflect sexual dimorphism (Fig 4), the shape change based on the PC2 axis reflects individual variability other than ontogeny and sexual dimorphism.

Size change

The chicken is precocial bird [56–58] and negative allometry was found in the brains of the broiler chickens used in this study (Fig 7). Previous studies also found negative allometry in many precocial bird species, including some domestic chickens [17, 18, 59–63]. This negative allometry is due to slow post-hatching brain growth [63]. Although the brains of the broiler chickens had a slope (exponent of 0.335) similar to many precocial birds, the slope of the birds in this study is relatively low compared to mallards, ducks [61, 63], and other precocial birds [59, 60, 61]. It is thought that the rapid growth and heavy body of the broiler [64, 65, 66] lowers the allometric slope of the brain and each brain region. However, compared to white leghorns [17], and other domestic chickens [18], the allometric slope of the broiler is relatively high for the brain and each brain region, even considering the slight differences in the brain regional borders between previous works and this study. The slopes of the brain and each brain region are ~0.25 or lower in every domestic chicken [17, 18], except the broiler. This indicates that the brain and each brain region in the boiler grows relatively rapidly compared to other chickens, but slowly compared to many other precocial birds. Further studies are needed to determine the effect of such differences in growth rate on brain morphology in various domestic chickens and other avian taxa.

The similar brain growth rate in the brain regions of chickens is due to differences in growth pattern, i.e. the differences between the precocial and altricial patterns. One of the most striking observations is the substantial difference in chick and adult brain sizes of altricial and precocial birds [62]. The difference in brain volume between precocial and altricial species is well known [13, 62, 67, 68]. All precocial species and some altricial species almost complete brain growth during embryogenesis, hatch with relatively large brains, and undergo little brain growth development from chick to adult [62]. On the other hand, hence the brains of many altricial birds considerably increase in size after hatching compared with those of precocial birds [63], the brain regions of altricial birds are supposed to develop at a different rate after hatching. It is thought that functional differentiation of each brain region in chickens is nearly complete by hatching and this leads to similar growth rates among each brain region.

There was a significant positive relationship between eye size and body size (Fig 7). Additionally, it was found that eyes grow at a faster rate than the brain. Garamszegi et al. [69] calculated the allometric relationship of eye size and brain size to body size from 141 and 159 bird species, respectively. Interspecific allometric equations for eye size and brain size on body mass showed that the avian eye exhibits tachyauxesis against the brain [69] and are consistent with our results from the ontogenetic analysis. This indicates that the rapid growth of the chicken eye is a simple matter of allometric relationships, not functional development through growth.

Relationships between shape and size

The shape of the brain and each brain region changed considerably; however, the volume ratio of each brain region did not change (Fig 7). That is, the brain can change its shape without variation in the proportional and relative sizes of brain regions in post-hatching growth. Distinct functions are localized within the brain [70] and the relative size of each brain region is indicative of their importance in the life of the animal [44]. Therefore, it would appear that functional changes of the brain already modestly advanced at hatching, since the relative sizes of the brain regions do not change throughout its life. In other words, the brains of chickens change shape after measurable functional differentiation of the brain regions, although we could not identify changes in the morphological characters that relate to function based on the ontogenetic shape differences.

Implications for paleontology

As discussed above, we assumed that functional differentiation of the brain in precocial birds, including chickens, is nearly complete by hatching, since the ratios of the brain regions were constant throughout growth. Many non-avian dinosaurs, which are avian ancestors and relatives, are thought to have been precocial [71, 72] and differentiation of their brains is probably complete at hatching, judging from the developing pattern. Thus, the brains of dinosaurs should show roughly similar ontogenetic changes to extant precocial birds, including chickens. The growth process of non-avian theropods leads to a more rostrocaudally elongated brain; indeed, this has been observed in some fossil specimens [73]. Hence, when we compare brain endocasts of the same extinct species, we have to pay due consideration to allometric relationships. Otherwise, we could develop fallacious arguments about fossil animals.

We also concluded that it is difficult to recognize functional development from brain and eye shape in the development of chickens. Brain regions showed no relative volumetric change, though their shape changed considerably. Many studies have discussed the sensory and locomotor abilities of extinct animals by assessing the relative size of individual brain regions based on their appearance in endocasts of fossil specimens [24, 29–38]. However, according to our results, it is not necessarily appropriate to suggest that the relative sizes of brain regions can be determined from external morphology. Since the interior boundaries of the brain regions are indefinable from extinct brain endocasts, we cannot determine the exact value of the regional volume in the brain of an extinct species. Despite these limitations, paleontologists have attempted to calculate the volume or area of the brain regions of extinct avian species [51, 74, 75]. Although we need to establish whether the volume of brain regions significantly correlates with area, assessing the brain morphology of extinct species in these quantitative ways has enormous implications for paleoneurological studies.

Conclusions

We assessed the ontogenetic changes in the brain shape of chickens using μ MRI. The dominant pattern of shape change was as follows: (1) rostrodorsal rotation of the telencephalon, (2) caudoventral rotation of the cerebellum and myelencephalon, (3) rostrocaudal elongation of the entire brain, and (4) extension of the optic nerve in a rostroventral direction. The pattern of these shape changes corresponds to interspecific shape changes due to increases in size. The interspecific and ontogenetic shape changes with increasing size exhibit similar patterns. Not all of the shape variation can be explained by size. The variations that cannot be explained by size are concentrated in the cerebellum. Changes in brain shape were also observed with no change in the ratio of individual brain regions. Growth of brain regions at the same rate as other regions is due to the nearly complete functional differentiation of the brain at hatching. Therefore, we concluded that it is difficult to recognize functional development from brain and eye shape in the development of chickens. A detailed analyses of the brain morphology in other taxon including palaeognathous birds is needed to address the universal rule of the ontogenetic shape change in avian brain, and this study is the starting point for understanding changes in brain shape during post-hatching development in birds. This work, however, provides an important particular case of the ontogenetic shape change, and is critical for future work on the avian brain morphology.

Acknowledgments

We thank Dr. Eiichi Izawa (Keio University) for his helpful advice, Koji Nomaru (Nagoya University) and the staff of the Integrated Center for Sciences Shigenobu Station, Ehime University for access to the MRI scanner, and Kumiko Matsui (The University of Tokyo) for providing useful information. We wish to thank Stig A. Walsh (National Museum Scotland) and an anonymous reviewer, and the editor Peter Dodson (University of Pensylvania), for greatly improving this manuscript.

Data Availability

All relevant data are within the paper.

Funding Statement

This work was supported by the Japan Society for the Promotion of Science 11J08692 & 26916003 (http://www.jsps.go.jp/english/e-grants/index.html). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1. Strauss RE, Fuiman LA (1984) Quantitative comparisons of body form and allometry in larval and adult pacific sulpins (Teleostei: Cottidae). Canadian Journal of Zoology 63, 1582–1589. [Google Scholar]
  • 2. Wayne RK (1986) Cranial morphology of domestic and wild canids: the influence of development on morphological change. Evolution 40, 243–261. [DOI] [PubMed] [Google Scholar]
  • 3. Monteiro LR, Cavalcanti MJ, Sommer HJS III (1997) Comparative ontogenetic shape changes in the skull of Caiman species (Crocodylia, Alligatoridae). Journal of Morphology 231, 53–62. [DOI] [PubMed] [Google Scholar]
  • 4. Ackermann RR, Krovitz GE (2002) Common patterns of facial ontogeny in the hominid lineage. Anatomical Record 269, 142–147. [DOI] [PubMed] [Google Scholar]
  • 5. Goodwin MB, Clemens WA, Horner JR, Padian K (2006) The smallest known Triceratops skull: new observations on ceratopsid cranial anatomy and ontogeny. Journal of Vertebrate Paleontology 26, 103–112. [Google Scholar]
  • 6. Horner JR, Goodwin MB (2009) Extreme cranial ontogeny in the Upper Cretaceous dinosaur Pachycephalosaurus. PLos One 4: e7626 10.1371/journal.pone.0007626 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Galatius A, Berta A, Frandsen MS, Goodall RNP (2011) Interspecific variation of ontogeny and skull shape among porpoises (Phocoenidae). Journal of Morphology 272, 136–148. 10.1002/jmor.10900 [DOI] [PubMed] [Google Scholar]
  • 8. Tsuihiji T, Watabe M, Tsogtbaatar T, Tsubamoto T, Barsbold R, Suzuki S, et al. (2011) Cranial osteology of a juvenile specimen of Tarbosaurus bataar (Theropoda, Tyrannosauridae) from the Nemegt Formation (Upper Cretaceous) of Bugin Tsav, Mongolia. Journal of Vertebrate Paleontology 31: 497–517. [Google Scholar]
  • 9. Rogers KT (1960) Studies on chick brain of biochemical differentiation related to morphological differentiation and onset of function. I. Morphological development. Journal of Experimental Zoology 144, 77–87. [DOI] [PubMed] [Google Scholar]
  • 10. Pearson R (1972) The avian brain New York: Academic Press. [Google Scholar]
  • 11. Rogers LJ (1995) The development of brain and behaviour in the chicken Cambridge, U.K.: CAB International. [Google Scholar]
  • 12. Kamon K (1906) Zur Entwickelungsgeschichte des Gehirns des Hühnchens. Anatomische Hefte 30, 562–650. [Google Scholar]
  • 13. Portmann A (1947): Étude sur la cérébralisation chez les oiseaux. II. Les indices intracérébraux. Alauda 15, 1–15. [Google Scholar]
  • 14. Graber RR, Graber JW (1965) Variation in avian brain weights with special reference to age. Condor 67, 300–318. [Google Scholar]
  • 15. Rehkämper G, Frahm HD, Zilles K (1991) Quantitative development of brain and brain structures in birds (Galliformes and Passeriformes) compared to that in mammals (insectivores and primates). Brain, Behavior and Evolution 37, 125–143. [DOI] [PubMed] [Google Scholar]
  • 16. Iwaniuk AN, Nelson JE (2003) Developmental differences are correlated with relative brain size in birds: a comparative analysis. Canadian Journal of Zoology 81, 1913–1928. [Google Scholar]
  • 17. Latimer HB (1925) The postnatal growth of the central nervous system of the chicken. The Journal of Comparative Neurology 33: 251–297. [Google Scholar]
  • 18. Frahm HD, Rehkamper G (1998) Allometric comparison of the brain and brain structures in the white crested polish chicken with uncrested domestic chicken breeds. Brain, Behavior and Evolution 52: 292–307. [DOI] [PubMed] [Google Scholar]
  • 19. Rogers SW (1998) Exploring dinosaur neuropaleobiology: viewpoint computed tomography scanning and analysis of an Allosaurus fragilis endocast. Neuron 21, 673–679. [DOI] [PubMed] [Google Scholar]
  • 20. Larsson HCE (2001) Endocranial anatomy of Carcharodontosaurus saharicus (Theropoda: Allosauroidea) and its implications for theropod brain evolution In: Tanke DH, Carpenter K (eds) Mesozoic vertebrate life. Indiana University Press, Bloomington, IN, pp 19–33. [Google Scholar]
  • 21. Brochu CA (2003) Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull. Journal of Vertebrate Paleontology Memoir 7, 1–140. [Google Scholar]
  • 22. Carlson WD, Rowe T, Ketcham RA, Colbert MW (2003). Geological applications of high-resolution x-ray computed tomography in petrology, meteoritics and palaeontology In: Mees F, Swennen R, Van Geet M, Jacobs P (eds) Applications of x-ray computed tomo- graphy in the geosciences. Geological Society, London, pp 7–22. [Google Scholar]
  • 23. Marino L, Uhen MD, Pyenson ND, Frohlich B (2003) Reconstructing cetacean brain evolution using computed tomography. Anatomical Record (Part B) 272B, 107–117. [DOI] [PubMed] [Google Scholar]
  • 24. Domínguez Alonso P, Milner AC, Ketcham RA, Cookson MJ, Rowe TB (2004) The avian nature of the brain and inner ear of Archaeopteryx . Nature 430, 666–669. [DOI] [PubMed] [Google Scholar]
  • 25. Maisey JG (2005) Braincase of the Upper Devonian shark Cladoides wildungensis (Chondrichthyes, Elasmo- branchii), with observations on the braincase in early chondrichthyans. Bulletin of American Musseum of Natural History 288, 1–103. [Google Scholar]
  • 26. Macrini TE, Rowe T, VandeBerg JL (2007) Cranial endocasts from a growth series of Monodelphis domestica (Didelphidae, Marsupialia): a study of individual and ontogenetic variation. Journal of Morphology 268, 844–865. [DOI] [PubMed] [Google Scholar]
  • 27. Sampson SD, Witmer LM (2007) Craniofacial anatomy of Majungasaurus crenatissimus (Theropoda: Abelisauridae) from the Late Cretaceous of Madagascar. Society of Vertebrate Paleontology Memoir 8. Journal of Vertebrate Paleontology 27 (Suppl. 2), 32–102. [Google Scholar]
  • 28. Witmer LM, Ridgely RC, Dufeau DL, Semones MC (2008) Using CT to peer into the past: 3D visualization of the brain and ear regions of birds, crocodiles, and nonavian dinosaurs In: Endo H, Frey R, editors. Anatomical imaging: towards a new morphology. Tokyo: Springer-Verlag, pp 67–87. [Google Scholar]
  • 29. Edinger T (1926) The brain of Archaeopteryx . Annals and Magazine of Natural History: Including Zoology, Botany and Geology 18, 151–156. [Google Scholar]
  • 30. Edinger T (1941) The brain of Pterodactylus . American Journal of Science 239, 665–682. [Google Scholar]
  • 31. de Beer G (1954) Archaeopteryx lithographica A study based on the British Museum specimen. London: British Museum. [Google Scholar]
  • 32. Jerison HJ (1968) Brain evolution and Archaeopteryx . Nature 219, 1381–1382. [DOI] [PubMed] [Google Scholar]
  • 33. Edinger T (1969) Midbrain exposure and overlap in mammals. American Zoologist 4, 5–19. [DOI] [PubMed] [Google Scholar]
  • 34. Elzanowski A, Galton PM (1991) Braincase of Enaliornis, an Early Cretaceous bird from England. Journal of Vertebrate Paleontology 11, 90–107. [Google Scholar]
  • 35. Witmer LM, Chatterjee S, Franzosa J, Rowe T (2003) Neuroanatomy of flying reptiles and implications for flight, posture and behavior. Nature 425, 950–953. [DOI] [PubMed] [Google Scholar]
  • 36. Milner AC, Walsh SA (2009) Avian brain evolution: new data from Palaeogene birds (Lower Eocene) from England. Zoological Journal of the Linnean Society 155, 198–219. [Google Scholar]
  • 37. Ksepka DT, Balanoff AM, Walsh SA, Revan A, Ho A (2012) Evolution of the brain and sensory organs in Sphenischiformes: new data from the stem penguin Paraptenodytes antarcticus. Zoological Journal of the Linnean Society 166, 202–219. [Google Scholar]
  • 38. Smith NA, Clarke JA (2012) Endocranial anatomy of the Charadriiformes: sensory system variation and the evolution of wing-propelled diving. PLos One 7, e49584 10.1371/journal.pone.0049584 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Kawabe S, Shimokawa T, Miki H, Matsuda S, Endo H (2013) Variation in avian brain shape: relationship with size and orbital shape. Journal of Anatomy 223: 495–508. 10.1111/joa.12109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Wiens J (1995) Polymorphic characters in phylogenetic systematics. Systematic Biology 44, 482–500. [Google Scholar]
  • 41. Wiens J (1998) Testing phylogenetic methods with tree congruence: Phylogenetic analysis of polymorphic morphological characters in phrynosomatid lizards. Systematic Biology 47, 427–444. [Google Scholar]
  • 42. Wiens J (1999) Polymorphism in systematics and comparative biology. Annual Review of Ecology and Systematics 30, 327–362. [Google Scholar]
  • 43. Macrini TE, Rowe T, VandeBerg JL (2007) Cranial endocasts from a growth series of Monodelphis domestica (Didelphidae, Marsupialia): a study of individual and ontogenetic variation. Journal of Morphology 268, 844–865. [DOI] [PubMed] [Google Scholar]
  • 44. Jerison HJ (1973) Evolution of the Brain and Intelligence. New York: Academic Press. [Google Scholar]
  • 45. Bang BG, Cobb S (1968) The size of the olfactory bulb in 108 species of birds. The auk 85, 55–61. [Google Scholar]
  • 46. Iwaniuk AN, Dean KM, Nelson JE (2005) Interspecific allometry of the brain and brain regions in parrots (Psittaciformes): Comparisons with other birds and primates. Brain, Behavior and Evolution 65, 40–59. [DOI] [PubMed] [Google Scholar]
  • 47. Iwaniuk AN, Wylie DRW (2006) The evolution of stereopsis and the Wulst in caprimulgiform birds: a comparative analysis. Journal of Comparative Physiology A 192, 1313–1326. [DOI] [PubMed] [Google Scholar]
  • 48. Iwaniuk AN, Hurd PL, Wylie DRW (2007) Comparative morphology of the avian cerebellum: II. Size of folia. Brain, Behavior and Evolution 69, 196–219. [DOI] [PubMed] [Google Scholar]
  • 49. Zelenitsky DK, Therrien F, Kobayashi Y (2009) Olfactory acuity in theropods: palaeobiological and evolutionary implications. Proceedings of the Royal Society B 276, 667–673. 10.1098/rspb.2008.1075 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Zelenitsky DK, Therrien F, Ridgely RC, McGee AR, Witmer LM (2011) Evolution of olfaction in non-avian theropod dinosaurs and birds. Proceedings of the Royal Society B 278, 3625–3634. 10.1098/rspb.2011.0238 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Walsh S, Milner A (2011) Halcyornis toliapicus (Aves: Lower Eocene, England) indicates advanced neuromorphology in Mesozoic Neornithes. Journal of Systematic Palaeontology 9, 173–181. [Google Scholar]
  • 52. Corfield JR, Wild JM, Cowan BR, Parsons S, Kubke MF (2008) MRI of postmortem specimens of endangered species for comparative brain anatomy. Nature Protocol 3, 597–605. 10.1038/nprot.2008.17 [DOI] [PubMed] [Google Scholar]
  • 53. Rohlf FJ, Slice D (1990) Extensions of the Procrustes method for the optimal superimposition of landmarks. Systematic Zoology 29, 40–59. [Google Scholar]
  • 54. Klingenberg CP (2011) MorphoJ: an integrated software package for geometric morphometrics. Molecular Ecology Resources 11, 353–357. 10.1111/j.1755-0998.2010.02924.x [DOI] [PubMed] [Google Scholar]
  • 55. Bookstein FL (1991) Morphometric Tools for Landmark Data. Cambridge University Press, Cambridge. [Google Scholar]
  • 56. Nice MM (1962) Development of behavior in precocial birds. Transactions, Linnsean Society of New York 8: 1–211. [Google Scholar]
  • 57. Skutch AF (1976) Parent Birds and their Young. University of Texas Press, Austin. [Google Scholar]
  • 58. Starck JM (1993) Evolution of avian ontogenies. Current Ornithology 10: 275–366. [Google Scholar]
  • 59. Portmann A (1945) Die postembryonale Entwicklung des Graureihers (Ardea cinerea L.) und ihre vergleichendbiologische Bedeutung. Schweizerisches Archiv für Ornithologie 2: 181–184. [Google Scholar]
  • 60. Schlabritzky E (1953) Die Bedeutung der Wachstumsgradienten für die Proportionierung der Organe verschieden grosser Haushuhnrassen. Zeitschrift für Morphologie und Ökologie der Tiere 41: 278–310. 10.1107/S0108767309007235 [DOI] [PubMed] [Google Scholar]
  • 61. Neff M (1973) Untersuchungen über das embryonale und postembryonale Organwachstum bei Vogelarten mit verschiedenen Ontogenesemodus. Revue Suisse de Zoologie 79: 1471–1597. [PubMed] [Google Scholar]
  • 62. Starck JM (1989) Zeitmuster der Ontogenesen bei nestflüchtenden und nesthockenden Vögeln. Curier Forsch–Inst Senckenberg 114, 1–319. [Google Scholar]
  • 63. Gille U, Salomon FV (2000) Brain growth in mallards, Pekin and Muscovy ducks (Anatidae). Journal of Zoology, London 252: 399–404. [Google Scholar]
  • 64. Peacock AJ, Pickett C, Morris K, Reeves JT (1989) The relationship between rapid growth and pulmonary hemodynamics in the fast-growing broiler chicken. American Review of Respiratory Disease 139: 1524–1530. [DOI] [PubMed] [Google Scholar]
  • 65. Hancock CE, Bradford GD, Emmans GC, Gous RM (1995) The evaluation of the growth parameters of six strains of commercial broiler chickens. British Poultry Science 36: 247–264. [DOI] [PubMed] [Google Scholar]
  • 66. Julian RJ (1998) Rapid growth problems: ascites and skeletal deformities in broilers. Poultry Science 77: 1773–1780. [DOI] [PubMed] [Google Scholar]
  • 67. Portmann A (1947): Étude sur la cérébralisation chez les oiseaux. III. Cérébralisation et mode ontogénétique. Alauda 15, 161–171. [Google Scholar]
  • 68. Charvet CJ, Striedter GF (2009) Developmental basis for telencephalon expansion in waterfowl: enlargement prior to neurogenesis. Proceedings of the Royal Society B 276: 3421–3427. 10.1098/rspb.2009.0888 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69. Garamszegi LZ, Møller AP, Erritzøe J (2002) Coevolving avian eye size and brain size in relation to prey capture and nocturnality. Proc R Soc Lond B 269, 961–967. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70. Young RM (1990) Mind, brain and adaptation in the nineteenth century Oxford: Clarendon Press. [Google Scholar]
  • 71. Geist NR, Jones TD (1996) Juvenile skeletal structure and the reproductive habits of dinosaurs. Science 272: 712–714. [DOI] [PubMed] [Google Scholar]
  • 72. Erickson GM, Rogers KC, Yerby SA (2001) Dinosaurian growth patterns and rapid avian growth rates. Nature 412: 429–433. [DOI] [PubMed] [Google Scholar]
  • 73. Bhullar B-AS, Marugán-Lobón J, Racimo F, Bever GS, Rowe TB, Norell MA, Abzhanov A (2012) Birds have paedomorphic dinosaur skulls. Nature 487, 223–226. 10.1038/nature11146 [DOI] [PubMed] [Google Scholar]
  • 74. Balanoff AM, Bever GS, Rowe TB, Norell MA (2013) Evolutionary origins of the avian brain. Nature 501: 93–93. 10.1038/nature12424 [DOI] [PubMed] [Google Scholar]
  • 75. Walsh SA, Iwaniuk AN, Knoll MA, Bourdon E, Barrett PM, Milner AC, et al. (2013) Avian cerebellar floccular fossa size is not a proxy for flying ability in birds. PLoS One 8: e67176 10.1371/journal.pone.0067176 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

All relevant data are within the paper.

Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES