TY - GEN
T1 - Individualized ROI optimization via maximization of group-wise consistency of structural and functional profiles
AU - Li, Kaiming
AU - Guo, Lei
AU - Faraco, Carlos
AU - Zhu, Dajiang
AU - Deng, Fan
AU - Zhang, Tuo
AU - Jiang, Xi
AU - Zhang, Degang
AU - Chen, Hanbo
AU - Hu, Xintao
AU - Miller, Steve
AU - Liu, Tianming
PY - 2010
Y1 - 2010
N2 - Functional segregation and integration are fundamental characteristics of the human brain. Studying the connectivity among segregated regions and the dynamics of integrated brain networks has drawn increasing interest. A very controversial, yet fundamental issue in these studies is how to determine the best functional brain regions or ROIs (regions of interests) for individuals. Essentially, the computed connectivity patterns and dynamics of brain networks are very sensitive to the locations, sizes, and shapes of the ROIs. This paper presents a novel methodology to optimize the locations of an individual's ROIs in the working memory system. Our strategy is to formulate the individual ROI optimization as a group variance minimization problem, in which group-wise functional and structural connectivity patterns, and anatomic profiles are defined as optimization constraints. The optimization problem is solved via the simulated annealing approach. Our experimental results show that the optimized ROIs have significantly improved consistency in structural and functional profiles across subjects, and have more reasonable localizations and more consistent morphological and anatomic profiles.
AB - Functional segregation and integration are fundamental characteristics of the human brain. Studying the connectivity among segregated regions and the dynamics of integrated brain networks has drawn increasing interest. A very controversial, yet fundamental issue in these studies is how to determine the best functional brain regions or ROIs (regions of interests) for individuals. Essentially, the computed connectivity patterns and dynamics of brain networks are very sensitive to the locations, sizes, and shapes of the ROIs. This paper presents a novel methodology to optimize the locations of an individual's ROIs in the working memory system. Our strategy is to formulate the individual ROI optimization as a group variance minimization problem, in which group-wise functional and structural connectivity patterns, and anatomic profiles are defined as optimization constraints. The optimization problem is solved via the simulated annealing approach. Our experimental results show that the optimized ROIs have significantly improved consistency in structural and functional profiles across subjects, and have more reasonable localizations and more consistent morphological and anatomic profiles.
UR - http://www.scopus.com/inward/record.url?scp=85162358118&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:85162358118
SN - 9781617823800
T3 - Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
BT - Advances in Neural Information Processing Systems 23
PB - Neural Information Processing Systems
T2 - 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
Y2 - 6 December 2010 through 9 December 2010
ER -