Adult Lifespan Cognitive Variability in the Cross-Sectional Cam-CAN Cohort

Abstract

This study examines variability across the age span in cognitive performance in a cross-sectional, population-based, adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study (n = 2680). A key question we highlight is whether using measures that are designed to detect age-related cognitive pathology may not be sensitive to, or reflective of, individual variability among younger adults. We present three issues that contribute to the debate for and against age-related increases in variability. Firstly, the need to formally define measures of central tendency and measures of variability. Secondly, in addition to the commonly addressed location-confounding (adjusting for covariates) there may exist changes in measures of variability due to confounder sub-groups. Finally, that increases in spread may be a result of floor or ceiling effects; where the measure is not sensitive enough at all ages. From the Cam-CAN study, a large population-based dataset, we demonstrate the existence of variability-confounding for the immediate episodic memory task; and show that increasing variance with age in our general cognitive measures is driven by a ceiling effect in younger age groups.

Differential effects of brain maintenance and cognitive reserve on age-related cognitive decline

“…Age-related cognitive decline has been reported repeatedly in many life-span epidemiological studies (Hartshorne & Germine, 2015; Hughes, Agrigoroaei, Jeon, Bruzzese, & Lachman, 2018) with the majority of studies examining only a truncated age range rather than the whole life-span (Cornelis et al, 2019; Singh-Manoux et al, 2012; Zaninotto, Batty, Allerhand, & Deary, 2018). The pattern of age-related changes varies across cognitive abilities (Salthouse, 1998, 2010; Elliot M. Tucker-Drob, 2019) in both cross-sectional (Green, Shafto, Matthews, Cam, & White, 2015; E. M. Tucker-Drob, 2009; Whitley et al, 2016) and longitudinal (De Vis et al, 2018; Hughes et al, 2018; Singh-Manoux et al, 2012) studies. Processing speed usually exhibits the steepest decline (Salthouse, 2019) whereas vocabulary is well maintained until late adulthood (Singh-Manoux et al, 2011).…”

Revisiting measurement invariance in intelligence testing in aging research: Evidence for almost complete metric invariance across age groups

“…This replicates previous work demonstrating increased within-group variability in older adulthood (Ardila, 2007; Christensen et al, 1994; Hultsch, MacDonald, & Dixon, 2002; Mella, Fagot, & de Ribaupierre, 2016; Rabbitt, 1993; Sylvain-Roy & Belleville, 2015; Ylikoski et al, 1999). Some posit that this increased variability is due to a lifetime of accumulating risk and protective factors like increased brain pathology (Rabbitt, 2011), but others find an increased variability is not inevitable across one’s cognitive development (Salthouse, 2011) or may be due to ceiling effects in young adulthood (Green, Shafto, Matthews, Cam-CAN, & White, 2015). Future work should examine if this pattern holds longitudinally, and if so, elucidate potential mechanisms of increased variability (e.g., ceiling effects in youth).…”

A Subtype Perspective on Cognitive Trajectories in Healthy Aging

“…Blazer, Yaffe, and Karlawish (2015) described that the age-dependent changes in cognitive function are due to longitudinal differences in experiences, health status, lifestyles, education, attitudinal and emotional factors, socioeconomic status, and genetics [26]. Further, the contributions of each of these factors are heterogeneous across the population, making it challenging to quantify their exact contributions [27]. Research indicates that the heritability of cognitive health shifts over the lifespan [28].…”