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Bayesian modelling of effective and functional brain connectivity using hierarchical vector autoregressions
Stockholm University, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0003-2786-2519
Number of Authors: 42024 (English)In: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876Article in journal (Refereed) Epub ahead of print
Abstract [en]

Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression hierarchical model for analysing brain connectivity within resting-state functional magnetic resonance imaging, and apply it to simulated data and a real data set with subjects in different groups. Our approach models functional and effective connectivity simultaneously and allows for both group- and single-subject inference. We combine analytical marginalization with Hamiltonian Monte Carlo to obtain highly efficient posterior sampling. We show that our model gives similar inference for effective connectivity compared to models with a common covariance matrix to all subjects, but more accurate inference for functional connectivity between regions compared to models with more restrictive covariance structures. A Stan implementation of our model is available on GitHub.

Place, publisher, year, edition, pages
2024.
Keywords [en]
Bayesian inference, effective connectivity, functional connectivity, Hamiltonian Monte Carlo, hierarchical modelling, resting-state fMRI
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:su:diva-227737DOI: 10.1093/jrsssc/qlae014ISI: 001186104900001OAI: oai:DiVA.org:su-227737DiVA, id: diva2:1847101
Available from: 2024-03-26 Created: 2024-03-26 Last updated: 2024-03-26

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Villani, Mattias

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