Intra-Individual Variability Across Fluid Cognition Can Reveal Qualitatively Different Cognitive Styles of the Aging Brain

Abstract

Dispersion is a measure of intra-individual variability reflecting how much performance across distinct cognitive functions varies within an individual. In cognitive aging studies, results are inconsistent: some studies report an increase in dispersion with increasing age and decline in performance, while others report an increasingly homogenous cognitive profile in older adults. We propose that inconsistencies may reflect qualitative differences in the cognitive functioning of the aging brain: age-groups may differ in how efficiently they engage resources, depending on both executive processing and resources available. This in turn would result in either greater or less dispersion. 21 young (mean 25.14 years, SD ± 2.85), 21 middle-old (65.05 ± 4.19), and 20 old-old (80.65 ± 4.38) healthy adults completed a series of neuropsychological tasks engaging executive processing, including switching, planning, updating, working memory and short-term memory. Individual dispersion profiles were obtained using a regression method which computes individual standard deviation across tasks from standardized test scores. Results revealed associations between performance, dispersion and cognitive reserve (measured as education level). Although differences across groups did not approach significance, there was a general pattern consistent with existing literature showing greater dispersion in the old-old group, and this was negatively associated with performance. In contrast, the middle-old group showed young-equivalent dispersion index, while performance was similar to the young group on some tasks and to the old-old group on others, possibly reflecting differences in cognitive demand. Educational level positively correlated with performance in the middle-old group only. Overall, a distinct pattern emerged for the middle-old adults: they showed young-equivalent performance on a number of measures and similar dispersion index, while uniquely benefitting from cognitive reserve. This may possibly reflect engagement in compensatory mechanisms. This study contributes to clarifying inconsistencies in previous studies and calls for more thoughtful selection of sample cohorts in aging research. The study of dispersion may provide a behavioral index of age-related changes in how cognition functions and recruits resources. Future work could examine whether this also reflects age-related changes in neural recruitment and aim at identifying factors contributing to cognitive reserve, in order to prolong good performance and improve cognition in aging.

Cognitive Dispersion Is Not Associated with Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease: Results from the European Prevention of Alzheimer’s Dementia (EPAD) v500.0 Cohort

“…Yet, dispersion scores emerged as associated with increasing age and fewer years of formal education, in keeping with previous studies [14,20,41].…”

“…Our work takes some steps to address this. From a selection of established risk factors for AD, we found that cognitive dispersion increased with age -findings that resonate with comparisons between age-groups [14,15,39,41,42] -and decreased in individuals with higher educational attainment, possibly reflecting more efficient compensatory strategies in response to neuronal senescence [20]. This latter suggestion could be explored further through exploring structural and functional neuroimaging changes and their association with cognitive dispersion to better understand the influence of cognitive or brain reserve on variability in older age.…”

“…However, in older adults, higher baseline dispersion scores have been shown to predict progression to mild cognitive impairment (MCI) and dementia [11,[16][17][18][19], suggesting such performance dispersion may mark the emergence of a pathological process. In healthy ageing, increased cognitive dispersion may reflect the emergence of impaired attention and executive functions alongside otherwise preserved cognitive domains, in turn reflecting a breakdown in compensatory mechanisms [20] as a result of age-related changes in neurotransmitter efficiency [21], grey and white matter integrity or the disruption of the default node network [22]; mechanisms that become increasingly compromised when a disease process accelerates. It has also been shown, as with most mean or normative indices of cognitive performance, that educational attainment may influence cognitive dispersion in mid-older adults (~65 years old), but not in late-older adults (~80 years old), suggesting that the former age group may uniquely benefit from early life brain stimulation acquired through education [20].…”

Age-Related Changes in Attentional Refocusing during Simulated Driving

“…Overall, older drivers compensate for slower RTs and general cognitive decline in their driving behaviour [91]. However, recent findings have shown that protective factors against cognitive decline in older age, such as level of education and engagement, no longer facilitate compensation later in older age, when the cognitive reserve available to compensate is no longer sufficient for the demand for compensation, caused by declines in underlying cognitive resources [102,103]. The development of an assessment and training intervention that aims to assess and improve the ability to switch between tasks during driving, such as attending to traffic and reading road signs, could therefore be beneficial to the ageing population.…”

Collective action or individual choice: Spontaneity and individuality contribute to decision-making inDrosophila

“…Inter-individual variability may arise either from consistent differences between individuals (i.e., 'individuality' (Beauchamp, 2001;Eccard et al, 2020;Gosling, 2001; Gosling and John, 1999;Hasenjager et al, 2020;Honegger and de Bivort, 2018;Massol and Crochet, 2008;Nettle, 2006;Réale et al, 2007;Sih et al, 2004;Verbeek et al, 1994;Wolf et al, 2007)), and/or by individuals generally behaving inconsistently over time (i.e., 'spontaneity'; (Brembs, 2011;De Felice and Holland, 2018;Lichtenstein et al, 2017;Maye et al, 2007;Morgan and Neuringer, 1990;Jensen, 2013, 2010)). Of course, individuals may also differ consistently in their variability (Hertel et al, 2020).…”