Bayesian network mediation analysis with application to brain functional connectome

TL;DR

This paper introduces a Bayesian mediation analysis method for brain functional connectomes, modeling network-variate mediators with stochastic block models and feature selection, to better understand neural pathways mediating effects between exposures and outcomes.

Contribution

It develops a novel Bayesian framework incorporating network mediators with stochastic block models and feature selection, addressing a key gap in mediation analysis for network data.

Findings

Model accurately estimates mediation effects in simulated data.

Effectively identifies active mediating network structures.

Applied to brain data, reveals neural pathways mediating therapeutic effects.

Abstract

Brain functional connectome, the collection of interconnected neural circuits along functional networks, is one of the most cutting edge neuroimaging traits, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network-variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the paper, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub-networks.