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dc.contributor.authorKlein, Arno
dc.contributor.authorBao, Forrest S.
dc.contributor.authorGiard, Joachim
dc.contributor.authorStavsky, Eliezer
dc.contributor.authorLee, Noah
dc.contributor.authorRossa, Brian
dc.contributor.authorReuter, Martin
dc.contributor.authorChaibub Neto, Elias
dc.contributor.authorKeshavan, Anisha
dc.contributor.authorHame, Yrjo
dc.contributor.authorGhosh, Satrajit S
dc.date.accessioned2017-06-16T15:07:03Z
dc.date.available2017-06-16T15:07:03Z
dc.date.issued2017-02
dc.date.submitted2016-08
dc.identifier.issn1553-7358
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/1721.1/109947
dc.description.abstractMindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available.en_US
dc.description.sponsorshipNational Institute of Mental Health (U.S.) (R01 MH084029)en_US
dc.description.sponsorshipNational Institute of Mental Health (U.S.) (U01 MH074813)en_US
dc.description.sponsorshipNational Institute of Mental Health (U.S.) (3U01MH092250-03S1)en_US
dc.description.sponsorshipUnited States. National Institutes of Health (1R01EB020740)en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.1005350en_US
dc.rightsCreative Commons Attribution 4.0 International Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleMindboggling morphometry of human brainsen_US
dc.typeArticleen_US
dc.identifier.citationKlein, Arno; Ghosh, Satrajit S.; Bao, Forrest S.; Giard, Joachim; H?me, Yrj?; Stavsky, Eliezer; Lee, Noah et al. “Mindboggling Morphometry of Human Brains.” Edited by Dina Schneidman. PLOS Computational Biology 13, no. 2 (February 2017): e1005350 © 2017 Klein et alen_US
dc.contributor.departmentMcGovern Institute for Brain Research at MITen_US
dc.contributor.mitauthorGhosh, Satrajit S
dc.relation.journalPLOS Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsKlein, Arno; Ghosh, Satrajit S.; Bao, Forrest S.; Giard, Joachim; H?me, Yrj?; Stavsky, Eliezer; Lee, Noah; Rossa, Brian; Reuter, Martin; Chaibub Neto, Elias; Keshavan, Anishaen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-5312-6729
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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