RT Journal Article
SR Electronic
T1 BAI-Net: Individualized Anatomical Cerebral Cartography using Graph Convolutional Network
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.07.15.452577
DO 10.1101/2021.07.15.452577
A1 Liang Ma
A1 Yu Zhang
A1 Hantian Zhang
A1 Luqi Cheng
A1 Junjie Zhuo
A1 Weiyang Shi
A1 Yuheng Lu
A1 Wen Li
A1 Zhengyi Yang
A1 Jiaojian Wang
A1 Lingzhong Fan
A1 Tianzi Jiang
YR 2021
UL http://biorxiv.org/content/early/2021/11/21/2021.07.15.452577.abstract
AB Individualized human cerebral cartography has drawn considerable attention in neuroscience research. Many challenges remain, mainly due to large individual variability in brain morphology, connectivity and function. We developed a new tool called brain atlas individualization network (BAI-Net) that automatically parcels individual cerebral cortex into segregated areas using structural and diffusion MRIs. The presented method integrates the group priors into the loss function of graph convolution network, learns the connectivity context of anatomical fingerprints for each area, and consequently provides reliable and explainable individual-specific topography across multiple sessions and different scanners. The BAI-Net model not only captures high variations across individual brains while maintaining high consistency with the priors in the group level, demonstrating high heritability in a twin study, but also shows high associations with behaviors in a cognitive battery. Our method shows high potentials in the field of precision medicine including the diagnosis and treatment of neurological disorders.Competing Interest StatementThe authors have declared no competing interest.