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

Research output: Contribution to journalArticlepeer-review

Kara Garcia, Emma 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
Accepted/In press22 Jan 2018
E-pub ahead of print5 Mar 2018
Published20 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 matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique 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 reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28–38 wk 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 noninjured 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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