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dc.contributor.authorVenkataraman, Archana
dc.contributor.authorKubicki, Marek
dc.contributor.authorWestin, Carl-Fredrik
dc.contributor.authorGolland, Polina
dc.date.accessioned2012-08-28T19:24:52Z
dc.date.available2012-08-28T19:24:52Z
dc.date.issued2010-06
dc.date.submitted2010-06
dc.identifier.issn978-1-4244-7029-7
dc.identifier.urihttp://hdl.handle.net/1721.1/72387
dc.description.abstractWe propose an alternative to univariate statistics for identifying population differences in functional connectivity. Our feature selection method is based on a procedure that searches across subsets of the data to isolate a set of robust, predictive functional connections. The metric, known as the Gini Importance, also summarizes multivariate patterns of interaction, which cannot be captured by univariate techniques. We compare the Gini Importance with univariate statistical tests to evaluate functional connectivity changes induced by schizophrenia. Our empirical results indicate that univariate features vary dramatically across subsets of the data and have little classification power. In contrast, relevant features based on Gini Importance are considerably more stable and allow us to accurately predict the diagnosis of a test subject.en_US
dc.description.sponsorshipNational Science Foundation (U.S.). Career Award (0642971)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (R01MH074794)en_US
dc.description.sponsorshipNational Defense Science and Engineering Graduate Fellowship (Department of Defense Fellowship)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (National Alliance for Medical Image Analysis) (NIH NIBIB NAMIC U54-EB005149),en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Neuroimaging Analysis Center) (NIH NCRR NAC P41-RR13218)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). Career Award (0642971)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/CVPRW.2010.5543446en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceIEEEen_US
dc.titleRobust feature selection in resting-state fMRI connectivity based on population studiesen_US
dc.typeArticleen_US
dc.identifier.citationVenkataraman, Archana et al. “Robust Feature Selection in Resting-state fMRI Connectivity Based on Population Studies.” IEEE, 2010. 63–70. © Copyright 2010 IEEEen_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.approverGolland, Polina
dc.contributor.mitauthorVenkataraman, Archana
dc.contributor.mitauthorWestin, Carl-Fredrik
dc.contributor.mitauthorGolland, Polina
dc.relation.journal2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsVenkataraman, Archana; Kubicki, Marek; Westin, Carl-Fredrik; Golland, Polinaen
dc.identifier.orcidhttps://orcid.org/0000-0003-2516-731X
dc.identifier.orcidhttps://orcid.org/0000-0002-2683-5888
mit.licensePUBLISHER_POLICYen_US


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