TY - GEN
T1 - Gyral Growth Patterns of Macaque Brains Revealed by Scattered Orthogonal Nonnegative Matrix Factorization
AU - Zhang, Songyao
AU - Du, Lei
AU - Lv, Jinglei
AU - He, Zhibin
AU - Jiang, Xi
AU - Guo, Lei
AU - Wang, Li
AU - Liu, Tianming
AU - Shen, Dinggang
AU - Li, Gang
AU - Zhang, Tuo
N1 - Funding Information:
Acknowledgements. T Zhang, L Du, X Jiang and L Guo were supported by the National Natural Science Foundation of China (31971288, 61973255, 61703073, 61976045, 61936007 and U1801265).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Cerebral cortex development undergoes a variety of alternate processes, providing valuable information to study the developmental mechanism of the cortical folding and the structural and functional architectures. Many longitudinal studies are performed on the development of sulci using features like sulcal depth, but the gyral system is less studied. To fill the gap, we propose a novel feature, termed gyral height, to quantify the longitudinal developmental patterns of gyri. Another practical problem is the difficulty of obtaining data for all time points for all subjects, even in animal datasets, such as the macaque neurodevelopment dataset in this work. Therefore, we develop a novel method by introducing a scattered factor to the orthogonal nonnegative matrix factorization to align data both longitudinally and cross-sectionally. By this method, the gyral height feature maps are decomposed into orthogonal cortical clusters which encode spatiotemporal patterns. Close relations are found between these clusters and anatomical, structural connective and functional metrics, suggesting the potential of the novel cortical feature and the method in investigating the brain development.
AB - Cerebral cortex development undergoes a variety of alternate processes, providing valuable information to study the developmental mechanism of the cortical folding and the structural and functional architectures. Many longitudinal studies are performed on the development of sulci using features like sulcal depth, but the gyral system is less studied. To fill the gap, we propose a novel feature, termed gyral height, to quantify the longitudinal developmental patterns of gyri. Another practical problem is the difficulty of obtaining data for all time points for all subjects, even in animal datasets, such as the macaque neurodevelopment dataset in this work. Therefore, we develop a novel method by introducing a scattered factor to the orthogonal nonnegative matrix factorization to align data both longitudinally and cross-sectionally. By this method, the gyral height feature maps are decomposed into orthogonal cortical clusters which encode spatiotemporal patterns. Close relations are found between these clusters and anatomical, structural connective and functional metrics, suggesting the potential of the novel cortical feature and the method in investigating the brain development.
KW - Gyral height
KW - Orthogonal nonnegative matrix factorization
UR - http://www.scopus.com/inward/record.url?scp=85092743416&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59861-7_40
DO - 10.1007/978-3-030-59861-7_40
M3 - Conference contribution
AN - SCOPUS:85092743416
SN - 9783030598600
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 394
EP - 403
BT - Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
A2 - Cao, Xiaohuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 4 October 2020
ER -