TY - GEN
T1 - Group-wise optimization and individualized prediction of structural connectomes
AU - Chen, Hanbo
AU - Li, Kaiming
AU - Zhu, Dajiang
AU - Guo, Lei
AU - Jiang, Xi
AU - Liu, Tianming
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/7/29
Y1 - 2014/7/29
N2 - Construction and modeling of structural and functional connectomes from neuroimaging data have shown great promise in elucidating the fundamental architectures of the human brain. In this paper, we present a novel framework to optimize large-scale cortical landmarks by maximizing the structural connectome agreement across a group of subjects and then use group-wise consistent connectome as an effective constraint to predict these optimized landmarks on new individuals. This cortical landmark optimization and prediction framework have been developed, validated and applied to the publicly available Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system as a test-bed with large testing samples (N=120). The experimental results suggest that our framework can substantially increase the group-wise connection consistency between DICCCOL landmarks across individuals' brains. After applying our framework, the anatomical and connectional profiles of those landmarks are remarkably improved, thus offering a solid structural foundation for future investigation of a variety of brain sciences questions.
AB - Construction and modeling of structural and functional connectomes from neuroimaging data have shown great promise in elucidating the fundamental architectures of the human brain. In this paper, we present a novel framework to optimize large-scale cortical landmarks by maximizing the structural connectome agreement across a group of subjects and then use group-wise consistent connectome as an effective constraint to predict these optimized landmarks on new individuals. This cortical landmark optimization and prediction framework have been developed, validated and applied to the publicly available Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system as a test-bed with large testing samples (N=120). The experimental results suggest that our framework can substantially increase the group-wise connection consistency between DICCCOL landmarks across individuals' brains. After applying our framework, the anatomical and connectional profiles of those landmarks are remarkably improved, thus offering a solid structural foundation for future investigation of a variety of brain sciences questions.
KW - Brain landmarks prediction
KW - Connectome
KW - DICCCOL
KW - Group-wise optimization
UR - http://www.scopus.com/inward/record.url?scp=84927938358&partnerID=8YFLogxK
U2 - 10.1109/isbi.2014.6867977
DO - 10.1109/isbi.2014.6867977
M3 - 会议稿件
AN - SCOPUS:84927938358
T3 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
SP - 742
EP - 745
BT - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Y2 - 29 April 2014 through 2 May 2014
ER -