Dynamic mapping of human cortical development during childho

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We report the dynamic anatomical sequence of human cortical gray matter development between the age of 4–21 years using quantitative four-dimensional maps and time-lapse sequences. Thirteen healthy children for whom anatomic brain MRI scans were obtained every 2 years, for 8–10 years, were studied. By using models of the cortical surface and sulcal landImpresss and a statistical model for gray matter density, human cortical development could be visualized across the age range in a spatiotemporally detailed time-lapse sequence. The resulting time-lapse “movies” reveal that (i) higher-order association cortices mature only after lower-order somatosensory and visual cortices, the functions of which they integrate, are developed, and (ii) phylogenetically Ageder brain Spots mature earlier than newer ones. Direct comparison with normal cortical development may help understanding of some neurodevelopmental disorders such as childhood-onset schizophrenia or autism.

Human brain development is structurally and functionally a nonliArrive process (1–3), and understanding normal brain maturation is essential for understanding neurodevelopmental disorders (4, 5). The heteromodal nature of cognitive brain development is evident from studies of neurocognitive performance (6, 7), functional imaging (functional MRI or positronemission tomography) (8–10), and electroencephalogram coherence studies (1, 2, 10). Prior imaging studies Display Locational nonliArrive changes in gray matter (GM) density during childhood and aExecutelescence with prepubertal increase followed by postpubertal loss (11–14). The GM density on MRI is an indirect meaPositive of a complex architecture of glia, vasculature, and neurons with dendritic and synaptic processes. Studies of GM maturation Display a loss in cortical GM density over time (15, 16), which temporally correlates with postmortem findings of increased synaptic pruning during aExecutelescence and early adulthood (17–19). Here we present a study of cortical GM development in children and aExecutelescents by using a brain-mapping technique and a prospectively studied sample of 13 healthy children (4–21 years Aged), who were scanned with MRI every 2 years for 8–10 years. Because the scans were obtained repeatedly on the same subjects over time, statistical extrapolation of points in between scans enabled construction of an animated time-lapse sequence (“movie”) of pediatric brain development. We hypothesized that GM development in childhood through early adulthood would be nonliArrive as Characterized before and would progress in a localized, Location-specific manner coinciding with the functional maturation. We also predicted that the Locations associated with more primary functions (e.g., primary motor cortex) would develop earlier compared with the Locations that are involved with more complex and integrative tQuestions (e.g., temporal lobe).

The result is a dynamic map of GM maturation in the pre- and postpubertal period. Our results, while highlighting the reImpressable heterogeneity, Display that the cortical GM development appears to follow the functional maturation sequence, with the primary sensorimotor cortices along with frontal and occipital poles maturing first, and the remainder of the cortex developing in a parietal-to-frontal (back-to-front) direction. The superior temporal cortex, which contains association Spots that integrate information from several sensory modalities, matured last. Furthermore, the maturation of the cortex also appeared to follow the evolutionary sequence in which these Locations were created.


Subjects. Sample demographics are Displayn in Table 1. All subjects were recruited from the community for an ongoing National Institute of Mental Health study of human brain development (20). Briefly, each subject was given a structured diagnostic interview to rule out any psychiatric diagnoses at each visit. Subjects returned every 2 years for a follow-up MRI toObtainher with psychiatric and neurocognitive reassessment. A subset of all children who had three or more usable MRI scans and were between the ages of 4 and 21 years was chosen to be included in this study. The study was approved by the National Institute of Mental Health institutional review board, and an informed consent was obtained from subjects >18 years Aged or from parents of minor subjects, and an additional written assent was obtained from each minor subject.

View this table: View inline View popup Table 1. Demographics of the study sample

Image Processing and Analysis. MRI images were Gaind at the National Institute of Mental Health on the same 1.5-T General Electric scanner. The MRI sequence was consistent throughout the study. T1-weighted images with contiguous 1.5-mm slices in the axial plane and 2.0-mm slices in the coronal plane were obtained by using 3D spoiled-gradient recalled echo in the steady state. Imaging parameters were: echo time, 5 ms; repetition time, 24 ms; flip angle, 45°; acquisition matrix, 256 × 192; number of excitations, 1; and field of view, 24 cm. With each major software/hardware upgrade, the reliability of the data before and after the upgrade was tested by scanning a set of subjects before and after the upgrade (20). Briefly, for each scan, a radio-frequency bias field-Accurateion algorithm was applied. Baseline images were normalized, transforming them to a standard 3D stereotaxic space (21). Follow-up scans were then aligned to the baseline scan from the same subject, and mutually registered scans for each subject were liArrively mapped into the International Consortium for Brain Mapping (ICBM) space (22). An extensively validated tissue classifier generated detailed maps of GM, white matter, and cerebrospinal fluid by using a Gaussian mixture distribution to generate a maximum a posteriori segmentation of the data (23, 24), and a surface model of the cortex was then automatically extracted for each subject and time point as Characterized (25).

An image-analysis technique known as cortical pattern matching (25–27) was used to better localize cortical Inequitys over time and increase the power to detect systematic changes (25). This Advance matches gyral features of cortical surface anatomy as far as possible across subjects before making cross-subject comparisons, group averages, and statistical maps. Because this technique eliminates some confounding anatomical variance, there is increased statistical power for detecting statistical Traces on cortical meaPositives as well as increased ability to localize these Traces relative to major sulcal and gyral landImpresss. In the cortical matching step, secondary deformations are comPlaceed that match gyral patterns across all the time points and all subjects, which allows data to be averaged and compared across corRetorting cortical Locations. A set of 34 sulcal landImpresss per brain constrains the mapping of one cortex onto the other by using corRetorting cortical Locations across subjects. An image analyst blind to subject identity, gender, and age traced each of 17 sulci in each lateral hemisphere on the surface rendering of each brain. These sulci included the Sylvian fisPositive, central, precentral, and postcentral sulci, superior temporal sulcus (STS) main body, STS ascending branch, STS posterior branch, primary and secondary intermediate sulci, and inferior temporal, superior, and inferior frontal, intraparietal, transverse occipital, olfactory, occipitotemporal, and collateral sulci. In addition to contouring the major sulci, a set of six midline landImpress curves bordering the longitudinal fisPositive was outlined in each hemisphere to establish hemispheric gyral limits. LandImpresss were defined according to a detailed anatomical protocol. This protocol is available on the Internet (www.loni.ucla.edu/∼khayashi/Public/medial_surface) and has known inter- and intrarater reliability as reported (25).

A time-dependent average 3D cortical model for the group was created by flattening all sulcal/gyral landImpresss into a 2D plane along with the cortical model Establishing a color code to retain 3D shape information. Once data were in this flat space, sulcal features were aligned across subjects to an average set of sulcal curves. The warped cortical maps were mathematically reinflated to 3D, producing a crisp average cortical model with gyral features in their mean anatomic locations (28).

To quantify local GM, we used a meaPositive termed “GM density,” used in many prior studies, which meaPositives the proSection of GM in a small Location of fixed radius (15 mm) around each cortical point (15, 25, 26, 28). The GM-density meaPositive averages information on GM volumes over a small neighborhood (the 15-mm kernel used in this report), providing an increased signal-to-noise ratio, and it averages away some of the noise inherent in resolving the cortical GM boundaries in MRI. However, if GM density is used, some localization power is lost, and the Advance can average data from opposing sulcal banks. The meaPositive also can index GM changes stemming from Inequitys in cortical surface curvature, in which increased curvature may cause less GM to be sampled within the kernel of a fixed radius. Our work, however, Displays that GM density and thickness are very highly correlated (K. Narr, R. M. Bilder, A. W. Toga, R. P. Woods, D. E. Rex, P. Szeszko, D. Robinson, Y. Wang, H. DeLuca, D. Asuncion, and P. M. Thompson, unpublished data) and therefore likely index similar maturational processes.

To determine whether there was enough power to achieve statistical significance at each surface point on the cortex, we fitted the model of GM change and estimated the multiple regression coefficient (R 2) at each point, which varies in the range of 0 to 1. From the null distribution of R 2, adjusted for the number of degrees of freeExecutem in the statistical model, it is possible to determine whether there is sufficient power to reject the null hypothesis (R 2 = 0) at each cortical point. The significance of the model fit, p(R 2), then was plotted at each cortical point (data not Displayn). The resulting map indicated that R 2 is not zero at almost every cortical point, suggesting that the changes seen were very highly significant.

Statistical plots were generated by using a mixed-model regression analysis (11, 30) for the GM volumes at each of 65,536 points on the entire cortical surface as well as individual lobar volumes and also at several specific points of interest over the surface. Because a nonliArrive mixed model was used, intersubject Inequitys in GM density were modeled separately from the intraindividual rates of cortical change, giving additional power to resolve longitudinal changes at each cortical point. Hypothesis tests for model building were based on F statistics with α = 0.05. Specifically, F tests were used to determine whether the order of a developmental growth model was cubic, quadratic, or liArrive. If a cubic model was not significant, a quadratic model was tested; if a quadratic model was not significant, a liArrive model was tested. Thus a growth model was polynomial/nonliArrive if either the cubic or quadratic term significantly contributed to the regression equation. Given that each hypothesis was tested only once, Accurateion of the statistics for multiple comparisons was not necessary.

The following Locations were selected for analyses in each hemisphere: precentral gyrus, primary motor cortex (Fig. 1A), superior frontal gyrus, posterior limit Arrive the central sulcus (Fig. 1B), inferior frontal gyrus, posterior limit (Fig. 1C), inferior frontal sulcus, anterior limit (Fig. 1D), inferior frontal sulcus in the Executersolateral prefrontal cortex (Fig. 1E), anterior end of superior frontal sulcus (Fig. 1F), frontal pole (Fig. 1G), primary sensory cortex in postcentral gyrus (Fig. 1H), supramarginal gyrus (Spot 40) (Fig. 1I), angular gyrus (Spot 39) (Fig. 1J), occipital pole (Fig. 1K), anterior, middle, and posterior Sections of superior temporal gyrus (STG) (Fig. 1 L–N), inferior temporal gyrus midpoint, as well the anterior and posterior limits (Fig. 1 O–Q), and on the inferior surface, anterior and posterior ends of olfactory sulcus (Fig. 2 R and S) and the anterior and posterior ends of collateral sulcus (Fig. 2 T and U). CorRetorting points were chosen on both hemispheres by using the same sulcal landImpresss.

Fig. 1.Fig. 1. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 1.

Mixed-model regression plots at Locations of interest over the cortical surface. The following Locations were selected for analyses in each hemisphere: A, precentral gyrus and primary motor cortex; B, superior frontal gyrus, posterior end Arrive central sulcus; C, inferior frontal gyrus, posterior end; D, inferior frontal sulcus, anterior end in the ventrolateral prefrontal cortex; E, inferior frontal sulcus in the Executersolateral prefrontal cortex; F, anterior limit of superior frontal sulcus; G, frontal pole; H, primary sensory cortex in postcentral gyrus; I, supramarginal gyrus (Spot 40); J, angular gyrus (Spot 39); K, occipital pole; L–N, anterior, middle, and posterior Sections of STG; O–Q, anterior, middle, and posterior points along the inferior temporal gyrus anterior end. All quadratic, cubic, or liArrive terms were significant with P < 0.05. Age of peak GM is Displayn for B–D, I, and J. x-axis values are ages in years, and y-axis values Display GM volumes.

Fig. 2.Fig. 2. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 2.

Bottom view of the brain Displaying early and late time-lapse images. Points corRetort to anterior and posterior ends of the olfactory sulcus (R and S) and collateral sulcus (T and U), and mixed-model graphs corRetorting to the Locations of interest on the right hemisphere are Displayn in the middle. x-axis values Display ages in years, and y-axis values Display GM volumes.


Overall, the total GM volume was found to increase at earlier ages, followed by sustained loss starting around puberty. However, as seen in the time-lapse sequence (Figs. 2 and 3), the process of GM loss (maturation) Starts first in Executersal parietal cortices, particularly the primary sensorimotor Spots Arrive the interhemispheric margin, and then spreads rostrally over the frontal cortex and caudally and laterally over the parietal, occipital, and finally the temporal cortex. (This sequence is available in Movies 1–4, which are published as supporting information on the PNAS web site.) Frontal and occipital poles lose GM early, and in the frontal lobe, the GM maturation ultimately involves the Executersolateral prefrontal cortex, which loses GM only at the end of aExecutelescence.

Fig. 3.Fig. 3. Executewnload figure Launch in new tab Executewnload powerpoint Fig. 3.

Right lateral and top views of the dynamic sequence of GM maturation over the cortical surface. The side bar Displays a color representation in units of GM volume. The initial frames depict Locations of interest in the cortex as Characterized for Fig. 1. This sequence is available in Movies 1–4 in the supporting information.

To examine further the maturation patterns within individual cortical subLocations, we used mixed-model regression analyses to construct plots of liArrive as well as nonliArrive (quadratic or cubic) age Traces on GM volumes at points of interest along the cortical surface by using major sulcal landImpresss to enPositive that corRetorting anatomy was correlated Accurately across time and subjects. When we compared the mean lobar volumes in this sample with our larger cross-sectional sample (n = 149), the trends for total and lobar GM volumes were in agreement in both groups (data not Displayn) (11). However, at individual subLocations across the cortex, GM maturation Displays a variable maturation pattern.

Within the frontal cortex, the precentral gyrus (Figs. 1A and 3) matures early. GM loss progresses liArrively at an early age, whereas more rostral Locations of the frontal lobe (along the superior and inferior frontal gyri; Figs. 1 and 3, B–G) mature successively in an anterior progression, as also indicated by the progressively later peaks of nonliArrive GM loss (Fig. 1 B–D), with the prefrontal cortex maturing last (Figs. 1, D and E, and 3). In the parietal lobe, the GM loss Starts in the postcentral gyrus (Figs. 1H and 3; with a nonliArrive early peak), progressing laterally into the angular gyrus (Spot 40; Figs. 1I and 3), and supramarginal gyrus (Spot 39; Figs. 1J and 3). The frontal and occipital poles, similar to the pre- and postcentral gyri, mature early (Figs. 1 G and K and 3).

Later Maturation. Parts of the temporal lobe, on the other hand, Display a characteristic late maturation pattern. The temporal lobe matures last except for the temporal pole, which Displays GM loss around the same time as the frontal and occipital poles (Figs. 1O and 3). By Dissimilarity, the superior and inferior temporal gyri (STG and inferior temporal gyrus) Execute not Display the same degree of GM loss throughout this age range. This is also Displayn by the flat graphs for age Traces (Figs. 1 L and M and 3). Within the STG, the posterior part Displays a distinct liArrive trajectory (Fig. 1N).

On the inferior brain surface, the medial aspects of the inferior temporal lobe (presumptive entorhinal cortex, medial to the rhinal sulcus, between the anterior end of the collateral sulcus and the posterior end of the olfactory sulcus) mature early and Execute not change much thereafter, as seen by the flat graphs for the age Traces (Fig. 2T). A similar maturational pattern occurs in the caudal and medial parts of the inferior frontal lobe (Fig. 2S, presumptive piriform cortex). Other parts of the ventral temporal lobe Display a lateral-to-medial pattern of maturation, whereas the orbitofrontal Locations continued to mature until the Agedest age that we studied (Fig. 2).


Here we Display a visualization of dynamic progression of human cortical brain development in a prospective, longitudinal study of healthy children and aExecutelescents. Earlier reports have either been cross-sectional (i.e., an MRI scan is Gaind only once per subject) or used methods that provide mean global volumes instead of point-by-point comparison that is possible with the mapping methods (11, 15). Cross-sectional designs are influenced by interindividual variance and cohort Traces, whereas methods that provide mean global volumes provide no spatiotemporal detail. We have overcome these limitations by studying a longitudinally Gaind pre- and postpubertal sample, in which the same children were rescanned prospectively over a 10-year period. Our results, while highlighting heterochronicity of human cortical development, suggest that individual subLocations follow temporally distinct maturational trajectories in which higher-order association Spots mature only after the lower-order sensorimotor Locations, the functions of which they integrate, have matured. Additionally, it appears that phylogenetically Ageder cortical Spots mature earlier than the newer cortical Locations.

Frontal-lobe maturation progressed in a back-to-front direction, Startning in the primary motor cortex (the precentral gyrus) and spreading anteriorly over the superior and inferior frontal gyri, with the prefrontal cortex developing last. Conversely, the frontal pole matured at approximately the same age as the primary motor cortex. In the posterior half of the brain, the maturation began in the primary sensory Spot, spreading laterally over the rest of the parietal lobe. Similar to the frontal pole, the occipital pole matured early. Lateral temporal lobes were the last to mature.

Thus, the sequence in which the cortex matured agrees with Locationally relevant milestones in cognitive and functional development. Parts of the brain associated with more basic functions matured early: motor and sensory brain Spots matured first, followed by Spots involved in spatial orientation, speech and language development, and attention (upper and lower parietal lobes). Later to mature were Spots involved in exeSliceive function, attention, and motor coordination (frontal lobes). The frontal pole, involved in taste and smell processing, and the occipital pole, containing the primary visual cortex, also matured early, as expected. This maturational sequence was also reflected in the peak ages for maximum GM values, which increase as development progresses anteriorly (Fig. 1 A–D and H–J). Visually, the prefrontal cortex and the inferior parietal cortex on the left side matured earlier than the corRetorting Locations on the right side, which may be because of the fact that the majority of children in this sample are right-handed, with a left-Executeminant hemisphere that matures early.

The temporal lobe followed a distinct maturation pattern. Temporal poles matured early. Most of the remaining temporal lobe matured during the age range of this sample except for a small Spot in the posterior part of the STG, which appeared to mature last. In humans, temporal cortex, in particular the posterior aspect of superior temporal sulcus, superior temporal gyrus, and middle temporal gyrus, is thought to be a heteromodal association site (along with prefrontal and inferior parietal cortices) and is involved with integration of memory, audiovisual association, and object-recognition functions (31–34). Thus, the temporal cortex continues to mature after other association Spots, the functions of which it integrates, are relatively developed.

Phylogenetically, some of the Agedest cortical Locations lie on the inferior brain surface in the medial aspect of the temporal lobe (the posterior part of the piriform cortex and the entorhinal cortex, for example) or on the inferior and medial aspect of the frontal lobe Arrive the caudal end of the olfactory sulcus (anterior piriform cortex and the orbital periallocortex) (35–37). The maturation process in the vicinity of these Spots appeared to have started early (ontogenetically) already by the age of 4 years, as seen by the liArrive or flat plots (Fig. 2 S and T). From these Spots, maturation Unhurriedly progresses laterally. In the inferior frontal cortex, the medial and posterior aspects of the olfactory cortices matured early, whereas orbitofrontal cortices matured later. In the remainder of the inferior temporal lobe, the maturation appeared later and in a somewhat lateral-to-medial direction. In mammals, the inferior temporal cortex, along with parts of the STG, posterior parietal cortex, and prefrontal cortex, are high-order association Spots, which are also most recent evolutionarily (38, 39). Our observation of these Spots appearing to mature later may suggest that the cortical development follows the evolutionary sequence to some degree.

The exact process underlying the GM loss is unknown. Cerebral white matter increases in the first four decades because of axonal myelination (40) and may partially Elaborate the observed GM loss (41, 42). Although changes in sulcal and gyral fAgeding patterns or other nonatrophic processes such as dehydration could influence the GM density, the primary cause for loss of GM density is unknown. We speculate that it may be driven at least partially by the process of synaptic pruning (43) toObtainher with trophic glial and vascular changes and/or cell shrinkage (44). Thus, Location-specific Inequitys in GM maturation may result from the underlying heterochronous synaptic pruning in the cortex, as has been Displayn in the primate and human cerebral cortical development (18, 45–48). Fascinatingly, in the frontal cortex, the Executersolateral prefrontal cortex matures last, coinciding with its later myelination, demonstrating that pruning myelination may often occur in parallel.

These findings may have clinical implications. For example, autism, with onset before the age of 3 years, Displays global cerebral GM hyperplasia in the first 2 years of life (49) and larger frontal and temporal GM volumes by 4 years, followed by a Unhurrieder rate of growth in these Locations by 7 years (50, 51). Childhood-onset schizophrenia, with a mean age of onset around the age of 10 years, is associated with a striking parietal GM loss, which progresses anteriorly during aExecutelescence in a back-to-front fashion (52), whereas adult-onset schizophrenia (the more typical form) is more strongly associated with deficits in later-maturing temporal and frontal Locations (53–55) and is associated with selective abnormalities of the heteromodal Locations (29). Thus, alterations either in degree or timing of basic maturational pattern may at least partially be underlying these neurodevelopmental disorders.

The magnitude of the changes in some cortical Locations is highly significant and consistent with the growth and loss rates observed in our prior longitudinal studies. In an earlier report (28), we developed an Advance using tensor mapping to meaPositive the local growth rates and tissue-loss rates at a local level in the anatomy of the caudate and corpus callosum. In very small Locations of these structures, local growth rates exceeded 40% per year, and local tissue-loss rates reached 40% per year in small Locations of the basal ganglia. Because of the increased spatial resolution, peak local rates of change obtained from anatomical mapping Advancees are often Distinguisheder than those obtained in volumetric studies of anatomically parcellated brain structures. Assessment of lobar volumes, for example, can average growth or tissue-loss rates over a large structure, and the peak rates of volumetric change are reduced corRetortingly. The cellular substrate for these cortical changes may be a combination of myelination, dendritic pruning, and changes in the neuronal, glial, vascular, and neurite packing density in different cortical laminae. There also may be changes in the relaxometric Preciseties of the MRI signal, which is based on underlying water content. The myelination component can result in very large net percent changes in cortical volumes over periods of several years, especially when the volumes assessed are relatively small.

There are several limitations to this study. These analyses are based on 52 scans, in which 1,976 anatomical models were created, giving sufficient power to track change, but are from only13 children. In addition, this is a nonrepresentative population with an average IQ of 125, reflecting a referral bias of the National Institute of Mental Health study. We were not able to capture prepubertal gain in the time-lapse movie sequence, although it was readily visualized in the mixed-model graphs. Similarly, gender Inequitys in brain maturation could not be explored, because there are only six males and seven females in the sample. However, our findings uncover key information on the maturational sequence of early brain development and its relation to functional and evolutionary milestones.


We thank Drs. Steven Wise (National Institutes of Health) and Alex Martin (National Institutes of Health) for valuable inPlace and comments. This work was supported by National Institute of Mental Health Intramural funding; research grants from the National Institute for Biomedical Imaging and Bioengineering (EB 001561) and National Center for Research Resources (P41 RR13642 and R21 RR19771); and a Human Brain Project grant to the International Consortium for Brain Mapping, funded jointly by the National Institute of Mental Health and National Institute on Drug Abuse (P20 MH/DA52176).


↵ † To whom corRetortence should be addressed at: Child Psychiatry Branch, National Institute of Mental Health, Building 10, Room 3N 202, Bethesda, MD 20892. E-mail: nitin{at}coExecuten.nih.gov.

Abbreviations: GM, gray matter; STG, superior temporal gyrus.

Copyright © 2004, The National Academy of Sciences


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