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Bayesian modelling of effective and functional brain connectivity using hierarchical vector autoregressions
Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University , Linköping , Sweden;Center for Medical Image Science and Visualization (CMIV), Linköping University , Linköping , Sweden.ORCID iD: 0000-0003-2193-6003
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics. Department of Statistics, Umeå School of Business, Economics and Statistics (USBE), Umeå University , Umeå , Sweden.ORCID iD: 0000-0003-1524-0851
Center for Medical Image Science and Visualization (CMIV), Linköping University , Linköping , Sweden;Division of Medical Informatics, Department of Biomedical Engineering, Linköping University , Linköping , Sweden.
Department of Statistics, Stockholm University , Stockholm , Sweden.
2024 (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
Oxford University Press, 2024.
Keywords [en]
Bayesian inference, effective connectivity, functional connectivity, Hamiltonian Monte Carlo, hierarchical modelling, resting-state fMRI
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-222725DOI: 10.1093/jrsssc/qlae014ISI: 001186104900001OAI: oai:DiVA.org:umu-222725DiVA, id: diva2:1847110
Funder
Riksbankens Jubileumsfond, P16-028:1Swedish Research Council, 2020-02846Available from: 2024-03-26 Created: 2024-03-26 Last updated: 2024-03-26

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Lundquist, Anders

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