Bayesian Inference and Testing of Group Differences in Brain Networks

Abstract

Network data are increasingly collected along with other variables of interest. Our motivation is drawn from neurophysiology studies measuring brain connectivity networks for a sample of individuals along with their membership to a low or high creative reasoning group. It is of paramount importance to develop statistical methods for testing of global and local changes in the structural interconnections among brain regions across groups. We develop a general Bayesian procedure for inference and testing of group differences in the network structure, which relies on a nonparametric representation for the conditional probability mass function associated with a network-valued random variable. By leveraging a mixture of low-rank factorizations, we allow simple global and local hypothesis testing adjusting for multiplicity. An efficient Gibbs sampler is defined for posterior computation. We provide theoretical results on the flexibility of the model and assess testing performance in simulations. The approach is applied to provide novel insights on the relationships between human brain networks and creativity.

References in corpus (5)

• Modeling homophily and stochastic equivalence in symmetric relational data
• Object oriented data analysis: Sets of trees
• Bayesian Inference and Testing of Group Differences in Brain Networks
• MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics
• Classification of Complex Networks Based on Topological Properties

Cited by in corpus (6)

• Bayesian Inference and Testing of Group Differences in Brain Networks
• Network classification with applications to brain connectomics
• Statistical inference for network samples using subgraph counts
• Removing the influence of a group variable in high-dimensional predictive modelling
• Link prediction in dynamic networks using random dot product graphs
• The Phantom Alignment Strength Conjecture: Practical use of graph matching alignment strength to indicate a meaningful graph match