Bayesian varying‐effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury

• Published in: Human brain mapping Vol. 45; no. 10; pp. e26763 - n/a
• Main Authors: Ren, Yangfan, Osborne, Nathan, Peterson, Christine B., DeMaster, Dana M., Ewing‐Cobbs, Linda, Vannucci, Marina
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.07.2024
• Subjects: Adolescent; Bayes Theorem; Brain - diagnostic imaging; Brain - physiopathology; brain connectivity; Brain Injuries, Traumatic - diagnostic imaging; Brain Injuries, Traumatic - physiopathology; Child; Connectome - methods; Female; fMRI data; Gaussian process; Humans; Magnetic Resonance Imaging; Male; Models, Neurological; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; spike‐and‐slab prior; traumatic brain injury; variational inference; brain connectivity; traumatic brain injury; Gaussian process; variational inference; fMRI data; spike‐and‐slab prior
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26763

In this article, 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) nonlinear 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 functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) 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 TBI, and differences in effective connectivity strength between males and females. We propose a novel Bayesian vector autoregressive model for multiple subjects that identify group‐level directed networks and enables possibly nonlinear effects of covariates on the network edges. We analyze resting‐state functional magnetic resonance imaging data from children with traumatic brain injury and healthy controls to characterize age and sex effects on neural circuitry.