A Random Effects Stochastic Block Model for Community Detection in Multiple Networks with Applications to Neuroimaging Studies

• Activity Number: 399 - ASA Statistics in Imaging Section Student Paper Competition
• Type: Topic Contributed
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Subhadeep Paul* and Yuguo Chen
• Companies: University of Illinois At Urbana Champaign and University of Illinois at Urbana-Champaign
• Keywords: Community detection ; Functional MRI ; Calcium imaging ; Random effects stochastic block model ; Schizophrenia ; Autism

Abstract

Motivated by multi-subject and multi-trial experiments in neuroimaging studies, we develop a modeling framework for community detection in groups of related networks. The proposed model, which we call the random effects stochastic block model, is flexible and facilitates the study of group differences and subject specific variations in the community structure. We propose two methods to estimate the parameters of the model, a variational-EM algorithm and two nonparametric two-step methods based on spectral and matrix factorization. The methodology is applied to publicly available fMRI datasets from multi-subject experiments involving Schizophrenia, ADHD and Autism patients along with healthy controls. Our methods reveal an overall putative community structure representative of the groups as well as subject-specific variations within each group.