RT info:eu-repo/semantics/article
T1 Proposal for a hierarchical, multidimensional, and multivariate approach to investigate cognitive aging.
A1 Machado, Alejandra
A1 Barroso Ribal, José Domingo
A1 Molina Rodsríguez, Yaiza
A1 Nieto Barco, Antonieta
A1 Díaz Flores, Lucio
A1 Westman, Eric
A1 Ferreira Padilla, Daniel
A2 Psicología ClínicaPsicobiología y Metodología
A2 Grupo de investigación ULL: Neuropsicología
Facultad de Psicología y Logopedia
Instituto Universitario de Neurociencia
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 froma hierarchical, multidimensional, and multivariate approach. Orthogonal partial least squares to latentstructures and hierarchical models were applied for the first time in a study of cognitive aging. Theassociation between age and a total of 316 demographic, clinical, cognitive, and neuroimaging measureswas simultaneously analyzed in 460 cognitively normal individuals (35e85 years). Age showed a strongassociation with brain structure, especially with cortical thickness in frontal and parietal associationregions. Age also showed a fairly strong association with cognition. Although a strong association of agewith executive functions and processing speed was captured as expected, the association of age withvisual memory was stronger. Clinical measures were less strongly associated with age. Hierarchical andcorrelation analyses further showed these associations in a neuroimaging-cognitive-clinical order ofimportance. We conclude that orthogonal partial least square and hierarchical models are a promisingapproach to better understand the complexity in cognitive aging.
YR 2018
FD 2018
LK http://riull.ull.es/xmlui/handle/915/35396
UL http://riull.ull.es/xmlui/handle/915/35396
LA en
NO https://doi.org/10.1016/j.neurobiolaging.2018.07.017
DS Repositorio institucional de la Universidad de La Laguna
RD 05-dic-2024