TY - GEN
T1 - Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties
AU - Wang, Fan
AU - Lian, Chunfeng
AU - Wu, Zhengwang
AU - Wang, Li
AU - Lin, Weili
AU - Gilmore, John H.
AU - Shen, Dinggang
AU - Li, Gang
N1 - Funding Information:
This work was supported in part by NIH grants (MH107815, MH116225,
Funding Information:
This work was supported in part by NIH grants (MH107815, MH116225, and MH117943).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - The human brain develops dynamically and regionally heterogeneously during the first two postnatal years. Cortical developmental regionalization, i.e., the landscape of cortical heterogeneity in development, reflects the organization of underlying microstructures, which are closely related to the functional principles of the cortex. Therefore, prospecting early cortical developmental regionalization can provide neurobiologically meaningful units for precise region localization, which will advance our understanding on brain development in this critical period. However, due to the absence of dedicated computational tools and large-scale datasets, our knowledge on early cortical developmental regionalization still remains intact. To fill both the methodological and knowledge gaps, we propose to explore the cortical developmental regionalization using a novel method based on nonnegative matrix factorization (NMF), due to its ability in analyzing complex high-dimensional data by representing data using several bases in a data-driven way. Specifically, a novel multi-view NMF (MV-NMF) method is proposed, in which multiple distinct and complementary cortical properties (i.e., multiple views) are jointly considered to provide comprehensive observation of cortical regionalization process. To ensure the sparsity of the discovered regions, an orthogonal constraint defined in Stiefel manifold is imposed in our MV-NMF method. Meanwhile, a graph-induced constraint is also included to improve the compactness of the discovered regions. Capitalizing on an unprecedentedly large dataset with 1,560 longitudinal MRI scans from 887 infants, we delineate the first neurobiologically meaningful representation of early cortical regionalization, providing a valuable reference for brain development studies.
AB - The human brain develops dynamically and regionally heterogeneously during the first two postnatal years. Cortical developmental regionalization, i.e., the landscape of cortical heterogeneity in development, reflects the organization of underlying microstructures, which are closely related to the functional principles of the cortex. Therefore, prospecting early cortical developmental regionalization can provide neurobiologically meaningful units for precise region localization, which will advance our understanding on brain development in this critical period. However, due to the absence of dedicated computational tools and large-scale datasets, our knowledge on early cortical developmental regionalization still remains intact. To fill both the methodological and knowledge gaps, we propose to explore the cortical developmental regionalization using a novel method based on nonnegative matrix factorization (NMF), due to its ability in analyzing complex high-dimensional data by representing data using several bases in a data-driven way. Specifically, a novel multi-view NMF (MV-NMF) method is proposed, in which multiple distinct and complementary cortical properties (i.e., multiple views) are jointly considered to provide comprehensive observation of cortical regionalization process. To ensure the sparsity of the discovered regions, an orthogonal constraint defined in Stiefel manifold is imposed in our MV-NMF method. Meanwhile, a graph-induced constraint is also included to improve the compactness of the discovered regions. Capitalizing on an unprecedentedly large dataset with 1,560 longitudinal MRI scans from 887 infants, we delineate the first neurobiologically meaningful representation of early cortical regionalization, providing a valuable reference for brain development studies.
UR - http://www.scopus.com/inward/record.url?scp=85075650143&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32245-8_93
DO - 10.1007/978-3-030-32245-8_93
M3 - Conference contribution
AN - SCOPUS:85075650143
SN - 9783030322441
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 841
EP - 849
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -