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dc.contributor.authorKlein, Arnoen_US
dc.contributor.authorGhosh, Satrajit S.en_US
dc.contributor.authorBao, Forrest S.en_US
dc.contributor.authorGiard, Joachimen_US
dc.contributor.authorHäme, Yrjöen_US
dc.contributor.authorStavsky, Eliezeren_US
dc.contributor.authorLee, Noahen_US
dc.contributor.authorRossa, Brianen_US
dc.contributor.authorReuter, Martinen_US
dc.contributor.authorChaibub Neto, Eliasen_US
dc.contributor.authorKeshavan, Anishaen_US
dc.date.accessioned2017-04-06T03:19:04Z
dc.date.issued2017en_US
dc.identifier.citationKlein, A., S. S. Ghosh, F. S. Bao, J. Giard, Y. Häme, E. Stavsky, N. Lee, et al. 2017. “Mindboggling morphometry of human brains.” PLoS Computational Biology 13 (2): e1005350. doi:10.1371/journal.pcbi.1005350. http://dx.doi.org/10.1371/journal.pcbi.1005350.en
dc.identifier.issnen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:32072029
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
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.relation.isversionofdoi:10.1371/journal.pcbi.1005350en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322885/pdf/en
dash.licenseLAAen_US
dc.subjectImaging Techniquesen
dc.subjectNeuroimagingen
dc.subjectBiology and Life Sciencesen
dc.subjectNeuroscienceen
dc.subjectPhysical Sciencesen
dc.subjectMathematicsen
dc.subjectApplied Mathematicsen
dc.subjectAlgorithmsen
dc.subjectSimulation and Modelingen
dc.subjectGeometryen
dc.subjectGeodesicsen
dc.subjectMedicine and Health Sciencesen
dc.subjectDiagnostic Medicineen
dc.subjectDiagnostic Radiologyen
dc.subjectMagnetic Resonance Imagingen
dc.subjectRadiology and Imagingen
dc.subjectOrganismsen
dc.subjectAnimalsen
dc.subjectInvertebratesen
dc.subjectArthropodaen
dc.subjectInsectsen
dc.subjectHymenopteraen
dc.subjectAntsen
dc.subjectAnatomyen
dc.subjectNervous Systemen
dc.subjectCentral Nervous Systemen
dc.subjectComputer and Information Sciencesen
dc.subjectComputer Softwareen
dc.subjectBrainen
dc.titleMindboggling morphometry of human brainsen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalPLoS Computational Biologyen
dash.depositing.authorGhosh, Satrajit S.en_US
dc.date.available2017-04-06T03:19:04Z
dc.identifier.doi10.1371/journal.pcbi.1005350*
dash.authorsorderedfalse
dash.contributor.affiliatedGhosh, Satrajit
dash.contributor.affiliatedReuter, Martin
dc.identifier.orcid0000-0002-5312-6729


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