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dc.contributor.authorPlis, Sergey M.
dc.contributor.authorHjelm, Devon R.
dc.contributor.authorSlakhutdinov, Ruslan
dc.contributor.authorAllen, Elena
dc.contributor.authorBockholt, Henry J.
dc.contributor.authorLong, Jeffrey D.
dc.contributor.authorJohnson, Hans
dc.contributor.authorPaulsen, Jane S.
dc.contributor.authorTurner, Jessica A.
dc.contributor.authorCalhoun, Vince D.
dc.date.accessioned2015-09-09T09:29:35Z
dc.date.available2015-09-09T09:29:35Z
dc.date.issued2014-08-20
dc.identifier.issn1662-453X
dc.identifier.issn1662-4548
dc.identifier.urihttps://hdl.handle.net/1956/10440
dc.description.abstractDeep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.en_US
dc.language.isoengeng
dc.publisherFrontierseng
dc.rightsAttribution CC BYeng
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/eng
dc.subjectMRIeng
dc.subjectfMRIeng
dc.subjectintrinsic networkseng
dc.subjectclassificationeng
dc.subjectunsupervised learningeng
dc.titleDeep learning for neuroimaging: A validation studyeng
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2015-07-28T11:34:12Z
dc.description.versionpublishedVersion
dc.rights.holderCopyright 2014 The Authorseng
dc.source.articlenumber229
dc.identifier.doihttps://doi.org/10.3389/fnins.2014.00229
dc.identifier.cristin1155811
dc.source.journalFrontiers in Neuroscience
dc.source.408
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Psykologi: 260::Biologisk psykologi: 261
dc.subject.nsiVDP::Social sciences: 200::Psychology: 260::Biological psychology: 261


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