Show simple item record

dc.contributor.authorChakravarty, Sourish
dc.contributor.authorThrelkeld, Zachary D.
dc.contributor.authorBodien, Yelena G.
dc.contributor.authorEdlow, Brian L.
dc.contributor.authorBrown, Emery Neal
dc.date.accessioned2020-07-07T17:15:41Z
dc.date.available2020-07-07T17:15:41Z
dc.date.issued2020-03
dc.date.submitted2019-11
dc.identifier.isbn9781728143002
dc.identifier.issn2576-2303
dc.identifier.urihttps://hdl.handle.net/1721.1/126065
dc.description.abstractDynamic functional connectivity (DFC) analysis involves measuring correlated neural activity over time across multiple brain regions. Significant regional correlations among neural signals, such as those obtained from resting-state functional magnetic resonance imaging (fMRI), may represent neural circuits associated with rest. The conventional approach of estimating the correlation dynamics as a sequence of static correlations from sliding time-windows has statistical limitations. To address this issue, we propose a multivariate stochastic volatility model for estimating DFC inspired by recent work in econometrics research. This model assumes a state-space framework where the correlation dynamics of a multivariate normal observation sequence is governed by a positive-definite matrix-variate latent process. Using this statistical model within a sequential Bayesian estimation framework, we use blood oxygenation level dependent activity from multiple brain regions to estimate posterior distributions on the correlation trajectory. We demonstrate the utility of this DFC estimation framework by analyzing its performance on simulated data, and by estimating correlation dynamics in resting state fMRI data from a patient with a disorder of consciousness (DoC). Our work advances the state-of-the-art in DFC analysis and its principled use in DoC biomarker exploration.en_US
dc.description.sponsorshipNational Institutes of Health (Award P01-GM118629)en_US
dc.description.sponsorshipNational Institutes of Health (Award DP2-HD101400)en_US
dc.description.sponsorshipNational Institutes of Health (Award R21-NS109627)en_US
dc.description.sponsorshipNational Institutes of Health (Award RF1-NS115268)en_US
dc.description.sponsorshipNational Institutes of Health (Award K23-NS094538)en_US
dc.description.sponsorshipNational Institute of Neurological Disorders and Stroke (Award DP2-HD101400)en_US
dc.description.sponsorshipNational Institute of Neurological Disorders and Stroke (Award R21-NS109627)en_US
dc.description.sponsorshipNational Institute of Neurological Disorders and Stroke (Award RF1-NS115268)en_US
dc.description.sponsorshipNational Institute of Neurological Disorders and Stroke (Award K23-NS094538)en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ieeeconf44664.2019.9048807en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceProf. Brown via Courtney Crummetten_US
dc.titleA state-space model for dynamic functional connectivityen_US
dc.typeArticleen_US
dc.identifier.citationChakravarty, Sourish et al. "A state-space model for dynamic functional connectivity." 53rd Asilomar Conference on Signals, Systems, and Computers, November 2019, Pacific Grove, CA, USA, Institute of Electrical and Electronics Engineers (IEEE), March 2020 © 2019 IEEEen_US
dc.contributor.departmentPicower Institute for Learning and Memoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering and Scienceen_US
dc.relation.journal(ACSSC 2019) 2019 Asilomar Conference on Signals, Systems, and Computersen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.date.submission2019-12-10T20:37:48Z
mit.licenseOPEN_ACCESS_POLICY


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record