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

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 fle...

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Bibliographic Details
Published in:Neuroinformatics (Totowa, N.J.) N.J.), 2021, Vol.19 (1), p.39-56
Main Authors: Kook, Jeong Hwan, Vaughn, Kelly A., DeMaster, Dana M., Ewing-Cobbs, Linda, Vannucci, Marina
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Bayesian analysis
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Brain - physiology
Brain Injuries, Traumatic - diagnostic imaging
Brain Mapping - methods
Causality
Child
Computational Biology/Bioinformatics
Computational neuroscience
Computer Appl. in Life Sciences
Diffusion Tensor Imaging - methods
Functional magnetic resonance imaging
Humans
Image Interpretation, Computer-Assisted - methods
Magnetic resonance imaging
Markov analysis
Markov chains
Mathematical models
Methods
Neural networks
Neuroimaging
Neurology
Neurosciences
Software Original Article
Structure-function relationships
Traumatic brain injury
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Summary: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.
ISSN:1539-2791
1559-0089
DOI:10.1007/s12021-020-09472-w