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B0606
Title: Modeling populations of networks from multi-subject neuroimaging data Authors:  Subhadeep Paul - The Ohio State University (United States) [presenting]
Abstract: To analyze data from multi-subject experiments in neuroimaging studies, we develop a modeling framework for joint community detection in a population of networks. The proposed random effects stochastic block model facilitates the study of group differences and subject-specific variations in the community structure. The model proposes a putative mean community structure that is representative of the population under consideration but is not the community structure of any individual component network. Instead, the community memberships of nodes vary in each component network with a transition matrix, thus modeling the variation in community structure across a group of subjects. To estimate the quantities of interest, we propose two methods: a variational EM algorithm, and a non-negative matrix factorization based method called Co-OSNTF. We also develop a resampling-based hypothesis test for differences between community structure in two populations both at the whole network level and node level. The methodology is applied to data on multi-subject experiments involving schizophrenia and Tinnitus patients in two separate works.