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dc.contributor.authorChen, Chi-Hua
dc.contributor.authorFiecas, Mark
dc.contributor.authorGutierrez, E. D.
dc.contributor.authorPanizzon, Matthew S.
dc.contributor.authorEyler, Lisa T.
dc.contributor.authorVuoksimaa, Eero
dc.contributor.authorThompson, Wesley K.
dc.contributor.authorFennema-Notestine, Christine
dc.contributor.authorHagler, Donald J., Jr.
dc.contributor.authorJernigan, Terry L.
dc.contributor.authorNeale, Michael C.
dc.contributor.authorFranz, Carol E.
dc.contributor.authorLyons, Michael J.
dc.contributor.authorFischl, Bruce
dc.contributor.authorTsuang, Ming T.
dc.contributor.authorDale, Anders M.
dc.contributor.authorKremen, William S.
dc.date.accessioned2014-08-29T14:05:21Z
dc.date.available2014-08-29T14:05:21Z
dc.date.issued2013-10
dc.date.submitted2013-04
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.urihttp://hdl.handle.net/1721.1/89108
dc.description.abstractAnimal data show that cortical development is initially patterned by genetic gradients largely along three orthogonal axes. We previously reported differences in genetic influences on cortical surface area along an anterior-posterior axis using neuroimaging data of adult human twins. Here, we demonstrate differences in genetic influences on cortical thickness along a dorsal-ventral axis in the same cohort. The phenomenon of orthogonal gradations in cortical organization evident in different structural and functional properties may originate from genetic gradients. Another emerging theme of cortical patterning is that patterns of genetic influences recapitulate the spatial topography of the cortex within hemispheres. The genetic patterning of both cortical thickness and surface area corresponds to cortical functional specializations. Intriguingly, in contrast to broad similarities in genetic patterning, two sets of analyses distinguish cortical thickness and surface area genetically. First, genetic contributions to cortical thickness and surface area are largely distinct; there is very little genetic correlation (i.e., shared genetic influences) between them. Second, organizing principles among genetically defined regions differ between thickness and surface area. Examining the structure of the genetic similarity matrix among clusters revealed that, whereas surface area clusters showed great genetic proximity with clusters from the same lobe, thickness clusters appear to have close genetic relatedness with clusters that have similar maturational timing. The discrepancies are in line with evidence that the two traits follow different mechanisms in neurodevelopment. Our findings highlight the complexity of genetic influences on cortical morphology and provide a glimpse into emerging principles of genetic organization of the cortex.en_US
dc.description.sponsorshipNational Institute on Aging (Grant AG022381)en_US
dc.description.sponsorshipNational Institute on Aging (Grant AG018386)en_US
dc.description.sponsorshipNational Institute on Aging (Grant AG018384)en_US
dc.description.sponsorshipNational Institute on Aging (Grant AG022982)en_US
dc.description.sponsorshipNational Institute on Aging (Grant AG031224)en_US
dc.description.sponsorshipNational Institute on Drug Abuse (Grant DA029475)en_US
dc.description.sponsorshipNational Institute of Neurological Disorders and Stroke (U.S.) (Grant NS056883)en_US
dc.language.isoen_US
dc.publisherNational Academy of Sciences (U.S.)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1073/pnas.1308091110en_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.titleGenetic topography of brain morphologyen_US
dc.typeArticleen_US
dc.identifier.citationChen, C.-H., M. Fiecas, E. D. Gutierrez, M. S. Panizzon, L. T. Eyler, E. Vuoksimaa, W. K. Thompson, et al. “Genetic Topography of Brain Morphology.” Proceedings of the National Academy of Sciences 110, no. 42 (September 30, 2013): 17089–17094.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.mitauthorFischl, Bruceen_US
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.orderedauthorsChen, C.-H.; Fiecas, M.; Gutierrez, E. D.; Panizzon, M. S.; Eyler, L. T.; Vuoksimaa, E.; Thompson, W. K.; Fennema-Notestine, C.; Hagler, D. J.; Jernigan, T. L.; Neale, M. C.; Franz, C. E.; Lyons, M. J.; Fischl, B.; Tsuang, M. T.; Dale, A. M.; Kremen, W. S.en_US
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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