Cognitive aging on latent constructs for visual processing capacity: a novel structural equation modeling framework with causal assumptions based on a theory of visual attention

The Cognitive Connectome in Healthy Aging

Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups.

Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures.

Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups.

Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.

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

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.

Clinical TVA-based studies: a general review

In combination with whole report and partial report tasks, the theory of visual attention (TVA) can be used to estimate individual differences in five basic attentional parameters: the visual processing speed, the storage capacity of visual short-term memory, the perceptual threshold, the efficiency of top-down selectivity, and the spatial bias of attentional weighting. TVA-based assessment has been used in about 30 studies to investigate attentional deficits in a range of neurological and psychiatric conditions: (a) neglect and simultanagnosia, (b) reading disturbances, (c) aging and neurodegenerative diseases, and most recently (d) neurodevelopmental disorders. The article introduces TVA based assessment, discusses its methodology and psychometric properties, and reviews the progress made in each of the four research fields. The empirical results demonstrate the general usefulness of TVA-based assessment for many types of clinical neuropsychological research. The method's most important qualities are cognitive specificity and theoretical grounding, but it is also characterized by good reliability and sensitivity to minor deficits. The review concludes by pointing to promising new areas for clinical TVA-based research.