RT info:eu-repo/semantics/article
T1 Proposal for a hierarchical, multidimensional, and multivariate approach to investigate cognitive aging
A1 Díaz-Flores Varela, Lucio
K1 Aging
K1 Multivariate analysis
K1 OPLS
K1 Hierarchical
K1 Cognition
K1 Magnetic resonance imaging
AB Cognitive 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.
YR 2018
FD 2018
LK http://riull.ull.es/xmlui/handle/915/36278
UL http://riull.ull.es/xmlui/handle/915/36278
LA en
NO Machado A, Barroso J, Molina Y, Nieto A, Díaz-Flores L, Westman E, Ferreira DProposal for a hierarchical, multidimensional, and multivariate approach to investigate cognitive aging. Neurobiol Aging. 2018 Nov;71:179-188. doi: 10.1016/j.neurobiolaging.2018.07.017. Epub 2018 Aug 1. PMID: 30149289.
DS Repositorio institucional de la Universidad de La Laguna
RD 19-may-2024