Bayesian structure learning for dynamic brain connectivity

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorAndersen, Michael Riisen_US
dc.contributor.authorWinther, Oleen_US
dc.contributor.authorHansen, Lars Kaien_US
dc.contributor.authorPoldrack, Russellen_US
dc.contributor.authorKoyejo, Oluwasanmien_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDanmarks Tekniske Universiteten_US
dc.contributor.departmentStanford Universityen_US
dc.contributor.departmentUniversity of Illinois at Urbana-Champaignen_US
dc.date.accessioned2020-01-02T14:11:09Z
dc.date.available2020-01-02T14:11:09Z
dc.date.issued2018-01-01en_US
dc.description.abstractHuman brain activity as measured by fMRI exhibits strong correlations between brain regions which are believed to vary over time. Importantly, dynamic connectivity has been linked to individual differences in physiology, psychology and behavior, and has shown promise as a biomarker for disease. The state of the art in computational neuroimaging is to estimate the brain networks as relatively short sliding window covariance matrices, which leads to high variance estimates, thereby resulting in high overall error. This manuscript proposes a novel Bayesian model for dynamic brain connectivity. Motivated by the underlying neuroscience, the model estimates covariances which vary smoothly over time, with an instantaneous decomposition into a collection of spatially sparse components – resulting in parsimonious and highly interpretable estimates of dynamic brain connectivity. Simulated results are presented to illustrate the performance of the model even when it is mis-specified. For real brain imaging data with unknown ground truth, in addition to qualitative evaluation, we devise a simple classification task which suggests that the estimated brain networks better capture the underlying structure.en
dc.description.versionPeer revieweden
dc.format.extent11
dc.format.extent1436-1446
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAndersen , M R , Winther , O , Hansen , L K , Poldrack , R & Koyejo , O 2018 , Bayesian structure learning for dynamic brain connectivity . in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spain . Proceedings of Machine Learning Research , vol. 84 , JMLR , pp. 1436-1446 , International Conference on Artificial Intelligence and Statistics , Playa Blanca , Spain , 09/04/2018 . < http://proceedings.mlr.press/v84/andersen18a.html >en
dc.identifier.issn2640-3498
dc.identifier.otherPURE UUID: de11f02c-e29c-4090-9d02-e02bf3a550c3en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/de11f02c-e29c-4090-9d02-e02bf3a550c3en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85067805909&partnerID=8YFLogxKen_US
dc.identifier.otherPURE LINK: http://proceedings.mlr.press/v84/andersen18a.htmlen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/38801606/andersen18a_2.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/42257
dc.identifier.urnURN:NBN:fi:aalto-202001021368
dc.language.isoenen
dc.publisherPMLR
dc.relation.ispartofInternational Conference on Artificial Intelligence and Statisticsen
dc.relation.ispartofseriesProceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spainen
dc.relation.ispartofseriesProceedings of Machine Learning Researchen
dc.relation.ispartofseriesVolume 84en
dc.rightsopenAccessen
dc.titleBayesian structure learning for dynamic brain connectivityen
dc.typeConference article in proceedingsfi
dc.type.versionpublishedVersion
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