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dc.contributor.authorLangs, Georg
dc.contributor.authorLashkari, Danial
dc.contributor.authorSweet, Andrew Douglas
dc.contributor.authorTie, Yanmei
dc.contributor.authorRigolo, Laura
dc.contributor.authorGolby, Alexandra J.
dc.contributor.authorGolland, Polina
dc.date.accessioned2012-09-28T13:00:12Z
dc.date.available2012-09-28T13:00:12Z
dc.date.issued2011-06
dc.identifier.issn978-3-642-22091-3
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/1721.1/73459
dc.descriptionProceedings of the 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011.en_US
dc.description.abstractIn this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a low-dimensional embedding space derived from a diffusion process on a graph that represents correlations of fMRI time courses. The atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (IIS/CRCNS 0904625)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (CAREER grant 0642971)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NCRR NAC P41- RR13218)en_US
dc.description.sponsorshipNational Institute of Biomedical Imaging and Bioengineering (U.S.) (U54-EB005149)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (U41RR019703)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (P01CA067165)en_US
dc.description.sponsorshipSeventh Framework Programme (European Commission) (n◦257528 (KHRESMOI))en_US
dc.language.isoen_US
dc.publisherSpringer-Verlagen_US
dc.relation.isversionofhttp://dx.doi.org/ 10.1007/978-3-642-22092-0_12en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleLearning an atlas of a cognitive process in its functional geometryen_US
dc.typeArticleen_US
dc.identifier.citationLangs, Georg et al. “Learning an Atlas of a Cognitive Process in Its Functional Geometry.” Information Processing in Medical Imaging. Ed. Gábor Székely & Horst K. Hahn. LNCS, Vol. 6801. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. 135-146.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorLangs, Georg
dc.contributor.mitauthorLashkari, Danial
dc.contributor.mitauthorSweet, Andrew Douglas
dc.contributor.mitauthorGolland, Polina
dc.relation.journalInformation Processing in Medical Imagingen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsLangs, Georg; Lashkari, Danial; Sweet, Andrew; Tie, Yanmei; Rigolo, Laura; Golby, Alexandra J.; Golland, Polinaen
dc.identifier.orcidhttps://orcid.org/0000-0003-2516-731X
mit.licenseOPEN_ACCESS_POLICYen_US


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