Dynamic mapping of human cortical development during childhood through early adulthood

N. Gogtay, J. N. Giedd, L. Lusk, K. M. Hayashi, D. Greenstein, A. C. Vaituzis, T. F. Nugent, D. H. Herman, L. S. Clasen, A. W. Toga, J. L. Rapoport, P. M. Thompson
2004 Proceedings of the National Academy of Sciences of the United States of America  
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 landmarks and a statistical model for gray matter density, human cortical development could be visualized across the age range in a
more » ... y 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 older brain areas 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. H uman brain development is structurally and functionally a nonlinear 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 show regional nonlinear changes in gray matter (GM) density during childhood and adolescence with prepubertal increase followed by postpubertal loss (11) (12) (13) (14) . The GM density on MRI is an indirect measure of a complex architecture of glia, vasculature, and neurons with dendritic and synaptic processes. Studies of GM maturation show a loss in cortical GM density over time (15, 16) , which temporally correlates with postmortem findings of increased synaptic pruning during adolescence and early adulthood (17) (18) (19) . Here we present a study of cortical GM development in children and adolescents by using a brain-mapping technique and a prospectively studied sample of 13 healthy children (4-21 years old), 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 nonlinear as described before and would progress in a localized, region-specific manner coinciding with the functional maturation. We also predicted that the regions associated with more primary functions (e.g., primary motor cortex) would develop earlier compared with the regions that are involved with more complex and integrative tasks (e.g., temporal lobe). The result is a dynamic map of GM maturation in the pre-and postpubertal period. Our results, while highlighting the remarkable heterogeneity, show 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 areas 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 regions were created. Methods Subjects. Sample demographics are shown 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 together 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 Ͼ18 years old or from parents of minor subjects, and an additional written assent was obtained from each minor subject. Image Processing and Analysis. MRI images were acquired 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 ϫ 192; number of excitations, 1; and field of view, 24 cm. With each major software͞hardware 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-correction 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 linearly 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 described (25) . An image-analysis technique known as cortical pattern matching (25-27) was used to better localize cortical differences over time and increase the power to detect systematic changes (25). This approach matches gyral features of cortical Abbreviations: GM, gray matter; STG, superior temporal gyrus.
doi:10.1073/pnas.0402680101 pmid:15148381 pmcid:PMC419576 fatcat:uulpbeniojf33mahfi6zhvbsiu