Bayesian Topological Analysis of Functional Brain Networks

TL;DR

This paper presents a Bayesian inference framework for analyzing brain network topology, offering a probabilistic approach that detects subtle differences in neuroimaging data more effectively than traditional methods.

Contribution

It introduces a novel Bayesian topological analysis method for brain networks, improving sensitivity to subtle alterations in clinical neuroimaging.

Findings

Detected brain network differences in Alzheimer's data missed by traditional tests

Framework provides graded evidence through posterior distributions and Bayes factors

Confirmed statistical consistency with permutation testing

Abstract

Subtle alterations in brain network topology often evade detection by traditional statistical methods. To address this limitation, we introduce a Bayesian inference framework for topological comparison of brain networks that probabilistically models within- and between-group dissimilarities. The framework employs Markov chain Monte Carlo sampling to estimate posterior distributions of test statistics and Bayes factors, enabling graded evidence assessment beyond binary significance testing. Simulations confirmed statistical consistency to permutation testing. Applied to fMRI data from the Duke-UNC Alzheimer's Disease Research Center, the framework detected topology-based network differences that conventional permutation tests failed to reveal, highlighting its enhanced sensitivity to early or subtle brain network alterations in clinical neuroimaging.