A RANDOM EFFECTS STOCHASTIC BLOCK MODEL FOR JOINT COMMUNITY DETECTION IN MULTIPLE NETWORKS WITH APPLICATIONS TO NEUROIMAGING

• 成果类型: Article
• 署名作者: Paul, Subhadeep; Chen, Yuguo
• 署名单位: University System of Ohio; Ohio State University; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1339
• 发表日期: 2020
• 页码: 993-1029
• 关键词: brain networks; functional connectivity; dynamic reconfiguration; maximum-likelihood; schizophrenia; ORGANIZATION; Consistency; algorithms; DISCOVERY; science

摘要

To analyze data from multisubject experiments in neuroimaging studies, we develop a modeling framework for joint community detection in a group of related networks that can be considered as a sample from 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, which is representative of the group or 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 model-free two-step method called Co-OSNTF which is based on nonnegative matrix factorization. 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 the COBRE dataset, a publicly available fMRI dataset from multisubject experiments involving schizophrenia patients. Our methods reveal an overall putative community structure representative of the group as well as subject-specific variations within each of the two groups, healthy controls and schizophrenia patients. The model has good predictive ability for predicting community structure in subjects from the same population but outside the training sample. Using our network level hypothesis tests, we are able to ascertain statistically significant difference in community structure between the two groups, while our node level tests help determine the nodes that are driving the difference.