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dc.contributor.authorLila, Eardi
dc.contributor.authorAston, John
dc.date.accessioned2022-04-27T23:31:16Z
dc.date.available2022-04-27T23:31:16Z
dc.identifier.issn1932-6157
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336534
dc.description.abstractWe present a statistical framework that jointly models brain shape and functional connectivity, which are two complex aspects of the brain that have been classically studied independently. We adopt a Riemannian modeling approach to account for the non-Euclidean geometry of the space of shapes and the space of connectivity that constrains trajectories of co-variation to be valid statistical estimates. In order to disentangle genetic sources of variability from those driven by unique environmental factors, we embed a functional random effects model in the Riemannian framework. We apply the proposed model to the Human Connectome Project dataset to explore spontaneous co-variation between brain shape and connectivity in young healthy individuals.
dc.publisherInstitute of Mathematical Statistics
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleFunctional random effects modeling of brain shape and connectivity
dc.typeArticle
dc.publisher.departmentDepartment of Pure Mathematics And Mathematical Statistics
dc.date.updated2022-04-27T11:37:23Z
prism.publicationNameAnnals of Applied Statistics
dc.identifier.doi10.17863/CAM.83954
dcterms.dateAccepted2021-11-09
rioxxterms.versionAM
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEPSRC (EP/T017961/1)
cam.orpheus.counter22*
cam.depositDate2022-04-27
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2025-04-27


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