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dc.date.accessioned2021-01-08T20:20:18Z
dc.date.available2021-01-08T20:20:18Z
dc.date.created2020-12-19T12:57:09Z
dc.date.issued2020
dc.identifier.citationAnatürk, Melis Kaufmann, Tobias Cole, James H. Suri, Sana Griffanti, Ludovica Zsoldos, Enikő Filippini, Nicola Singh-Manoux, Archana Kivimäki, Mika Westlye, Lars Tjelta Ebmeier, Klaus de Lange, Ann-Marie Glasø . Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging. Human Brain Mapping. 2020
dc.identifier.urihttp://hdl.handle.net/10852/82006
dc.description.abstractThe concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging‐related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no “gold standard” for measuring these constructs. Using machine‐learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34–82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titlePrediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging
dc.typeJournal article
dc.creator.authorAnatürk, Melis
dc.creator.authorKaufmann, Tobias
dc.creator.authorCole, James H.
dc.creator.authorSuri, Sana
dc.creator.authorGriffanti, Ludovica
dc.creator.authorZsoldos, Enikő
dc.creator.authorFilippini, Nicola
dc.creator.authorSingh-Manoux, Archana
dc.creator.authorKivimäki, Mika
dc.creator.authorWestlye, Lars Tjelta
dc.creator.authorEbmeier, Klaus
dc.creator.authorde Lange, Ann-Marie Glasø
cristin.unitcode185,53,10,70
cristin.unitnameNORMENT part UiO
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.cristin1861880
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Human Brain Mapping&rft.volume=&rft.spage=&rft.date=2020
dc.identifier.jtitleHuman Brain Mapping
dc.identifier.doihttps://doi.org/10.1002/hbm.25316
dc.identifier.urnURN:NBN:no-84961
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1065-9471
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/82006/1/Prediction%2Bof%2Bbrain%2Bage%2Band%2Bcognitive%2Bage%252C%2BQuantifying%2Bbrain%2Band%2Bcognitive%2Bmaintenance%2Bin%2Baging.pdf
dc.type.versionPublishedVersion
cristin.articleidhbm.25316
dc.relation.projectNFR/286838


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