Proposal for a hierarchical, multidimensional, and multivariate approach to investigate cognitive aging

Neurobiology of Aging
Alejandra MachadoDaniel Ferreira

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.

Citations

Nov 10, 2020·Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition·Maroua BelghaliVasyl Laver
Apr 19, 2021·Cerebral Cortex·Lissett Gonzalez-BurgosDaniel Ferreira
Jun 16, 2021·Neurobiology of Aging·Daniel FerreiraKejal Kantarci
Sep 8, 2021·Frontiers in Aging Neuroscience·Eloy Garcia-CabelloDaniel Ferreira

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