Bayesian Varying-Effects Vector Autoregressive Models for Inference of Brain Connectivity Networks and Covariate Effects in Pediatric Traumatic Brain Injury

TL;DR

This paper introduces a Bayesian model for estimating brain connectivity networks that accounts for individual differences and covariate effects, using scalable variational inference, applied to pediatric TBI data.

Contribution

It extends multi-subject Bayesian vector autoregressive models with a nonparametric prior for covariate effects and scalable inference, enabling detailed analysis of brain connectivity in children.

Findings

Identified differences in brain network connectivity between TBI and healthy children.

Revealed age-related peak edge strength differences in TBI children.

Detected sex-related variations in effective connectivity.

Abstract

In this paper, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) non-linear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state fMRI data from children with a history of traumatic brain injury and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with traumatic brain injury (TBI), and differences in effective connectivity strength between males and females.