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dc.contributor.authorMachado, Alejandra
dc.contributor.authorBarroso Ribal, José Domingo
dc.contributor.authorMolina Rodsríguez, Yaiza
dc.contributor.authorNieto Barco, Antonieta 
dc.contributor.authorDíaz Flores, Lucio
dc.contributor.authorWestman, Eric
dc.contributor.authorFerreira Padilla, Daniel
dc.contributor.otherPsicología Clínica, Psicobiología y Metodología
dc.contributor.otherGrupo de investigación ULL: Neuropsicología Facultad de Psicología y Logopedia Instituto Universitario de Neurociencia
dc.date.accessioned2024-01-16T21:06:30Z
dc.date.available2024-01-16T21:06:30Z
dc.date.issued2018
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/35396
dc.descriptionhttps://doi.org/10.1016/j.neurobiolaging.2018.07.017
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 (35e85 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
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesNeurobiology of Aging, 71 (2018)
dc.rightsNo autorizo la publicación del documento
dc.titleProposal for a hierarchical, multidimensional, and multivariate approach to investigate cognitive aging.
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1016/j.neurobiolaging.2018.07.017
dc.subject.keywordAgingen
dc.subject.keywordMultivariate analysisen
dc.subject.keywordOPLSen
dc.subject.keywordHierarchicalen
dc.subject.keywordCognitionen
dc.subject.keywordMagnetic resonance imagingen


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