Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69763
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dc.contributor.authorMachado, Alejandraen_US
dc.contributor.authorBarroso, Joséen_US
dc.contributor.authorMolina, Yaizaen_US
dc.contributor.authorNieto, Antonietaen_US
dc.contributor.authorDíaz-Flores, Lucioen_US
dc.contributor.authorWestman, Ericen_US
dc.contributor.authorFerreira, Danielen_US
dc.date.accessioned2020-02-05T12:49:54Z-
dc.date.available2020-02-05T12:49:54Z-
dc.date.issued2018en_US
dc.identifier.issn0197-4580en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/69763-
dc.description.abstractCognitive aging is highly complex. We applied a data-driven statistical method to investigate aging from a hierarchical, multidimensional, and multivariate approach. Orthogonal partial least squares to latent structures and hierarchical models were applied for the first time in a study of cognitive aging. The association between age and a total of 316 demographic, clinical, cognitive, and neuroimaging measures was simultaneously analyzed in 460 cognitively normal individuals (35–85 years). Age showed a strong association with brain structure, especially with cortical thickness in frontal and parietal association regions. Age also showed a fairly strong association with cognition. Although a strong association of age with executive functions and processing speed was captured as expected, the association of age with visual memory was stronger. Clinical measures were less strongly associated with age. Hierarchical and correlation analyses further showed these associations in a neuroimaging-cognitive-clinical order of importance. We conclude that orthogonal partial least square and hierarchical models are a promising approach to better understand the complexity in cognitive aging.en_US
dc.languageengen_US
dc.relation.ispartofNeurobiology of Agingen_US
dc.sourceNeurobiology of Aging [ISSN 0197-4580], v. 71, p. 179-188en_US
dc.subject320107 Geriatríaen_US
dc.subject.otherAgingen_US
dc.subject.otherCognitionen_US
dc.subject.otherHierarchicalen_US
dc.subject.otherMagnetic Resonance Imagingen_US
dc.subject.otherMultivariate Analysisen_US
dc.subject.otherOPLSen_US
dc.titleProposal for a hierarchical, multidimensional, and multivariate approach to investigate cognitive agingen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.neurobiolaging.2018.07.017
dc.identifier.scopus85052141292-
dc.contributor.authorscopusid55921689200-
dc.contributor.authorscopusid7103318279-
dc.contributor.authorscopusid55921001400-
dc.contributor.authorscopusid7102673989-
dc.contributor.authorscopusid7004901441-
dc.contributor.authorscopusid35070775000-
dc.contributor.authorscopusid55356608800-
dc.description.lastpage188-
dc.description.firstpage179-
dc.relation.volume71-
dc.investigacionCiencias de la Saluden_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.date.coverdateNoviembre 2018
dc.identifier.ulpgces
dc.description.sjr2,352
dc.description.jcr4,398
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
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