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A Hierarchical Bayesian Mixture Model Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).ORCID iD: 0000-0003-2135-9963
Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0003-1524-0851
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0003-3187-1987
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).ORCID iD: 0000-0002-3367-1746
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2020 (English)In: Brain Connectivity, ISSN 2158-0014, E-ISSN 2158-0022, Vol. 10, no 5, p. 202-211Article in journal (Refereed) Published
Abstract [en]

This article proposes a Bayesian hierarchical mixture model to analyze functional brain connectivity where mixture components represent "positively connected" and "non-connected" brain regions. Such an approach provides a data-informed separation of reliable and spurious connections in contrast to arbitrary thresholding of a connectivity matrix. The hierarchical structure of the model allows simultaneous inferences for the entire population as well as for each individual subject. A new connectivity measure, the posterior probability of a given pair of brain regions of a specific subject to be connected given the observed correlation of regions' activity, can be computed from the model fit. The posterior probability reflects the connectivity of a pair of regions relative to the overall connectivity pattern of an individual, which is overlooked in traditional correlation analyses. This article demonstrates that using the posterior probability might diminish the effect of spurious connections on inferences, which is present when a correlation is used as a connectivity measure. In addition, simulation analyses reveal that the sparsification of the connectivity matrix using the posterior probabilities might outperform the absolute thresholding based on correlations. Therefore, we suggest that posterior probability might be a beneficial measure of connectivity compared with the correlation. The applicability of the introduced method is exemplified by a study of functional resting-state brain connectivity in older adults.

Place, publisher, year, edition, pages
Mary Ann Liebert, 2020. Vol. 10, no 5, p. 202-211
Keywords [en]
brain aging, fMRI, functional connectivity, hierarchical modeling, lognormal distribution, resting state
National Category
Neurosciences Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-173434DOI: 10.1089/brain.2020.0740ISI: 000542106300002PubMedID: 32308015Scopus ID: 2-s2.0-85087095754OAI: oai:DiVA.org:umu-173434DiVA, id: diva2:1453563
Funder
Swedish Research Council, 340-2012-5931Knut and Alice Wallenberg FoundationRagnar Söderbergs stiftelse, KVA/2011/88/65Riksbankens Jubileumsfond, P16-0628:1Available from: 2020-07-10 Created: 2020-07-10 Last updated: 2023-03-23Bibliographically approved

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Gorbach, TetianaLundquist, Andersde Luna, XavierNyberg, LarsSalami, Alireza

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Gorbach, TetianaLundquist, Andersde Luna, XavierNyberg, LarsSalami, Alireza
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StatisticsUmeå Centre for Functional Brain Imaging (UFBI)Department of Integrative Medical Biology (IMB)Diagnostic RadiologyWallenberg Centre for Molecular Medicine at Umeå University (WCMM)
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Brain Connectivity
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