  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.
