Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69763
Title: Proposal for a hierarchical, multidimensional, and multivariate approach to investigate cognitive aging
Authors: Machado, Alejandra
Barroso, José
Molina, Yaiza
Nieto, Antonieta
Díaz-Flores, Lucio
Westman, Eric
Ferreira, Daniel
UNESCO Clasification: 320107 Geriatría
Keywords: Aging
Cognition
Hierarchical
Magnetic Resonance Imaging
Multivariate Analysis, et al
Issue Date: 2018
Journal: Neurobiology of Aging 
Abstract: 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.
URI: http://hdl.handle.net/10553/69763
ISSN: 0197-4580
DOI: 10.1016/j.neurobiolaging.2018.07.017
Source: Neurobiology of Aging [ISSN 0197-4580], v. 71, p. 179-188
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