NEOCIVET: Extraction of cortical surface and analysis of neonatal gyrification using a modified CIVET pipeline

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Abstract

Cerebral cortical gyration becomes dramatically more complex in the fetal brain during the 3rd trimester of gestation; the process proceeds in a similar fashion ex utero in children who are born prematurely. To quantify this morphological development , it is necessary to extract the interface between gray matter and white matter. We employed the well-established CIVET pipeline to extract this cortical surface, with point correspondence across subjects, using a surface-based spherical registration. We developed a variant of the pipeline, called NEOCIVET, that addresses the well-known problems of poor and temporally-varying gray/white contrast in neonatal MRI. NEOCIVET includes a tissue classification strategy that combines i) local and global contrast features, ii) neonatal template construction based on age-specific sub-groups, and iii) masking of non-interesting structures using label-fusion approaches. These techniques replaced modules that might be suboptimal for regional analysis of poor-contrast neonatal cortex. In the analysis of 43 pretermborn neonates, many with multiple scans (n=65; 28-40 wks PMA at scan), NEOCIVET identified increases in cortical folding over time in numerous cortical regions (mean curvature: +0.004/wk) while folding did not change in major sulci that are known to develop early (corrected p<0.05). Cortical folding increase was disrupted in the presence of severe types of perinatal WM injury. The proposed pipeline successfully mapped cortical structural development, supporting current models of cerebral morphogenesis.

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Kim, H., Lepage, C., Evans, A. C., Barkovich, A. J., & Xu, D. (2015). NEOCIVET: Extraction of cortical surface and analysis of neonatal gyrification using a modified CIVET pipeline. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9351, pp. 571–579). Springer Verlag. https://doi.org/10.1007/978-3-319-24574-4_68

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