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Dynamic patterns of cortical expansion during folding of the preterm human brain

Research output: Contribution to journalArticle

Kara Garcia, Emma Claire Robinson, Dimitrios Alexopoulos, Donna L. Dierker, Matthew F. Glasser, Timothy S. Coalson, Cynthia M. Ortinau, Daniel Rueckert, Larry A. Taber, David Van Essen, Cyntia E. Rogers, Christopher D. Smyser, Philip Bayley

Original languageEnglish
Pages (from-to)3156-3161
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number12
Early online date5 Mar 2018
DOIs
Publication statusE-pub ahead of print - 5 Mar 2018

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Abstract

During the third trimester of human brain development, the cerebral
cortex undergoes dramatic surface expansion and folding. Physical
models suggest that relatively rapid growth of the cortical gray mat-
ter helps drive this folding, and structural data suggests that growth
may vary in both space (by region on the cortical surface) and time.
In this study, we propose a new method to estimate local growth from
sequential cortical reconstructions. Using anatomically-constrained Multimodal Surface Matching (aMSM), we obtain accurate, physically-guided point correspondence between younger and older cortical re-constructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with un-precedented precision. By considering 30 preterm infants scanned 2-4 times during the period of rapid cortical expansion (28 to 38 weeks
postmenstrual age), we observe significant regional differences in
growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with non-injured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully-automated surface analysis, based
on tools freely available to the brain mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables

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