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dc.contributor.authorLashkari, Danial
dc.contributor.authorVul, Edward
dc.contributor.authorKanwisher, Nancy
dc.contributor.authorGolland, Polina
dc.date.accessioned2012-04-05T16:52:03Z
dc.date.available2012-04-05T16:52:03Z
dc.date.issued2010-01
dc.date.submitted2009-12
dc.identifier.issn1053-8119
dc.identifier.issn1095-9572
dc.identifier.urihttp://hdl.handle.net/1721.1/69952
dc.description.abstractWe present a method for discovering patterns of selectivity in fMRI data for experiments with multiple stimuli/tasks. We introduce a representation of the data as profiles of selectivity using linear regression estimates, and employ mixture model density estimation to identify functional systems with distinct types of selectivity. The method characterizes these systems by their selectivity patterns and spatial maps, both estimated simultaneously via the EM algorithm. We demonstrate a corresponding method for group analysis that avoids the need for spatial correspondence among subjects. Consistency of the selectivity profiles across subjects provides a way to assess the validity of the discovered systems. We validate this model in the context of category selectivity in visual cortex, demonstrating good agreement with the findings based on prior hypothesis-driven methods.en_US
dc.description.sponsorshipMcGovern Institute Neurotechnology (MINT) Programen_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant NIBIB NAMIC U54-EB005149)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant NCRR NAC P41-RR13218)en_US
dc.description.sponsorshipNational Eye Institute (grant 13455)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (grant CAREER 0642971)en_US
dc.description.sponsorshipCollaborative Research in Computational Neuroscience (IIS/CRCNS 0904625)en_US
dc.description.sponsorshipDeshpande Center for Technological Innovation (MIT HST Catalyst grant)en_US
dc.description.sponsorshipAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshipen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.neuroimage.2009.12.106en_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.sourcePubMed Centralen_US
dc.titleDiscovering Structure in the Space of fMRI Selectivity Profilesen_US
dc.typeArticleen_US
dc.identifier.citationLashkari, Danial et al. “Discovering Structure in the Space of fMRI Selectivity Profiles.” NeuroImage 50.3 (2010): 1085–1098. Web. 5 Apr. 2012.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.approverKanwisher, Nancy
dc.contributor.mitauthorLashkari, Danial
dc.contributor.mitauthorVul, Edward
dc.contributor.mitauthorKanwisher, Nancy
dc.contributor.mitauthorGolland, Polina
dc.relation.journalNeuroImageen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsLashkari, Danial; Vul, Ed; Kanwisher, Nancy; Golland, Polinaen
dc.identifier.orcidhttps://orcid.org/0000-0003-3853-7885
dc.identifier.orcidhttps://orcid.org/0000-0003-2516-731X
mit.licenseOPEN_ACCESS_POLICYen_US


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