BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks

TL;DR

This paper introduces BVAR-connect, a scalable variational Bayes method for multi-subject brain connectivity analysis using fMRI and DTI data, enabling efficient inference of effective brain networks.

Contribution

It presents a novel variational inference framework for Bayesian multi-subject VAR models that integrates multi-modal data and addresses computational scalability.

Findings

Achieves comparable accuracy to MCMC with lower computational cost

Effectively handles imbalanced group sample sizes

Successfully applied to pediatric brain injury data

Abstract

In this paper we propose BVAR-connect, a variational inference approach to a Bayesian multi-subject vector autoregressive (VAR) model for inference on effective brain connectivity based on resting-state functional MRI data. The modeling framework uses a Bayesian variable selection approach that flexibly integrates multi-modal data, in particular structural diffusion tensor imaging (DTI) data, into the prior construction. The variational inference approach we develop allows scalability of the methods and results in the ability to estimate subject- and group-level brain connectivity networks over whole-brain parcellations of the data. We provide a brief description of a user-friendly MATLAB GUI released for public use. We assess performance on simulated data, where we show that the proposed inference method can achieve comparable accuracy to the sampling-based Markov Chain Monte Carlo approach but at a much lower computational cost. We also address the case of subject groups with imbalanced sample sizes. Finally, we illustrate the methods on resting-state functional MRI and structural DTI data on children with a history of traumatic injury.