Bayesian Network Clustering with Brain-Imaging Data

• Activity Number: 379 - Single and Multi-Object Regression and Clustering with Applications in Neuro-Imaging Data
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Mental Health Statistics Section
• Author(s): Sharmistha Guha*
• Companies: Texas A&M University
• Keywords: Bayesian mixture modeling; Network clustering; Network node selection; Spike and slab prior; Brain connectome data

Abstract

This article focuses on model-based clustering of subjects based on the shared relationships of subject-specific networks and covariates in scenarios when there are differences in the relationship between networks and covariates for different groups of subjects. We propose a novel nonparametric Bayesian mixture modeling framework with an undirected network response and scalar predictors. The symmetric matrix coefficients corresponding to the scalar predictors of interest in each mixture component involve low-rankness and group sparsity within the low-rank structure. Our framework allows precise characterization of uncertainty in identifying significant network nodes in each cluster. Empirical results in various simulation scenarios illustrate substantial inferential gains of the proposed framework in comparison with competitors. Analysis of a real brain connectome dataset using the proposed method provides interesting insights into the brain regions of interest (ROIs) significantly related to creative achievement in each cluster of subjects.