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dc.contributor.authorHoeft, Fumiko
dc.contributor.authorMcCandliss, Bruce D.
dc.contributor.authorBlack, Jessica M.
dc.contributor.authorGantman, Alexander
dc.contributor.authorZakerani, Nahal
dc.contributor.authorHulme, Charles
dc.contributor.authorLyytinen, Heikki
dc.contributor.authorWhitfield-Gabrieli, Susan
dc.contributor.authorGlover, Gary H.
dc.contributor.authorReiss, Allan L.
dc.contributor.authorGabrieli, John D. E.
dc.date.accessioned2012-03-30T21:55:14Z
dc.date.available2012-03-30T21:55:14Z
dc.date.issued2010-12
dc.date.submitted2010-06
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.urihttp://hdl.handle.net/1721.1/69912
dc.description.abstractIndividuals with developmental dyslexia vary in their ability to improve reading skills, but the brain basis for improvement remains largely unknown. We performed a prospective, longitudinal study over 2.5 y in children with dyslexia (n = 25) or without dyslexia (n = 20) to discover whether initial behavioral or brain measures, including functional MRI (fMRI) and diffusion tensor imaging (DTI), can predict future long-term reading gains in dyslexia. No behavioral measure, including widely used and standardized reading and language tests, reliably predicted future reading gains in dyslexia. Greater right prefrontal activation during a reading task that demanded phonological awareness and right superior longitudinal fasciculus (including arcuate fasciculus) white-matter organization significantly predicted future reading gains in dyslexia. Multivariate pattern analysis (MVPA) of these two brain measures, using linear support vector machine (SVM) and cross-validation, predicted significantly above chance (72% accuracy) which particular child would or would not improve reading skills (behavioral measures were at chance). MVPA of whole-brain activation pattern during phonological processing predicted which children with dyslexia would improve reading skills 2.5 y later with >90% accuracy. These findings identify right prefrontal brain mechanisms that may be critical for reading improvement in dyslexia and that may differ from typical reading development. Brain measures that predict future behavioral outcomes (neuroprognosis) may be more accurate, in some cases, than available behavioral measures.en_US
dc.description.sponsorshipEunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (Grant HD054720)en_US
dc.description.sponsorshipLucile Packard Children’s Hospital at Stanford (Child Health Research Program)en_US
dc.description.sponsorshipNational Center for Research Resources (U.S.) (Grant P41RR009874)en_US
dc.description.sponsorshipWilliam & Flora Hewlett Foundationen_US
dc.description.sponsorshipRichard King Mellon Foundationen_US
dc.language.isoen_US
dc.publisherProceedings of the National Academy of Sciences (PNAS)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1073/pnas.1008950108en_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.sourcePNASen_US
dc.titleNeural Systems Predicting Long-Term Outcome in Dyslexiaen_US
dc.typeArticleen_US
dc.identifier.citationHoeft, F. et al. “Neural Systems Predicting Long-term Outcome in Dyslexia.” Proceedings of the National Academy of Sciences 108.1 (2010): 361–366. Web. 30 Mar. 2012.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.approverGabrieli, John D. E.
dc.contributor.mitauthorGabrieli, John D. E.
dc.relation.journalProceedings of the National Academy of Sciencesen_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.orderedauthorsHoeft, F.; McCandliss, B. D.; Black, J. M.; Gantman, A.; Zakerani, N.; Hulme, C.; Lyytinen, H.; Whitfield-Gabrieli, S.; Glover, G. H.; Reiss, A. L.; Gabrieli, J. D. E.en
dc.identifier.orcidhttps://orcid.org/0000-0003-1158-5692
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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