ArticleNeuroinformatics2021
BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks.
Article in Neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Bayesian varying-effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury.Human brain mapping · 2024Article
- Flexible Bayesian Product Mixture Models for Vector Autoregressions.Journal of machine learning research : JMLR · 2024Article
- Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series.Psychophysiology · 2022Article
- Effective connectivity in the default mode network after paediatric traumatic brain injury.The European journal of neuroscience · 2022Article
- Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Identifiers
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Registered trials
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