Bayesian Inference and Testing of Group Differences in Brain Networks

TL;DR

This paper introduces a Bayesian method for testing differences in brain network structures across groups, using nonparametric models and mixture of low-rank factorizations, with applications to creativity studies.

Contribution

It develops a novel Bayesian framework for global and local network comparison that adjusts for multiplicity and provides theoretical and computational tools.

Findings

Effective in detecting group differences in simulated data.

Provides new insights into brain networks related to creativity.

Flexible modeling approach for network data.

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.