Open AccessFetal cortical surface atlas parcellation based on growth patterns
Author(s) -
Xia Jing,
Wang Fan,
Benkarim Oualid M.,
Sanroma Gerard,
Piella Gemma,
González Ballester Miguel A.,
Hahner Nadine,
Eixarch Elisenda,
Zhang Caiming,
Shen Dinggang,
Li Gang
Publication year - 2019
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24637
Subject(s) - pattern recognition (psychology) , neuroimaging , neuroscience , spectral clustering , similarity (geometry) , gyrification , cerebral cortex , artificial intelligence , computer science , cluster analysis , psychology , image (mathematics)
Defining anatomically and functionally meaningful parcellation maps on cortical surface atlases is of great importance in surface‐based neuroimaging analysis. The conventional cortical parcellation maps are typically defined based on anatomical cortical folding landmarks in adult surface atlases. However, they are not suitable for fetal brain studies, due to dramatic differences in brain size, shape, and properties between adults and fetuses. To address this issue, we propose a novel data‐driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic “growth patterns” of cortical properties (e.g., surface area) from a population of fetuses. Our motivation is that the growth patterns of cortical properties indicate the underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, growth patterns are well suitable for defining distinct cortical regions in development, structure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices, and the other is based on the correlation profiles of vertices' growth trajectories in relation to a set of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better capture both their common and complementary information than by simply averaging them. Finally, based on this fused similarity matrix, we perform spectral clustering to divide the fetal cortical surface atlases into distinct regions. By applying our method on 25 normal fetuses from 26 to 29 gestational weeks, we construct age‐specific fetal cortical surface atlases equipped with biologically meaningful parcellation maps based on cortical growth patterns. Importantly, our generated parcellation maps reveal spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortical surface development.