Intraindividual Variability across Neuropsychological Tests: Dispersion and Disengaged Lifestyle Increase Risk for Alzheimer’s Disease

Abstract

Objective : Increased intraindividual variability (IIV) in function has been linked to various age-related outcomes including cognitive decline and dementia. Most studies have operationalized IIV as fluctuations across trials (e.g., response latencies) for a single task, with comparatively few studies examining variability across multiple tasks for a given individual. In the present study, we derive a multivariable operationalization of dispersion across a broad profile of neuropsychological measures and use this index along with degree of engaged lifestyle to predict risk of cognitive impairment. Participants and Methods : Participants ( n = 60) were community-dwelling older adults aged 65+ years (M = 74.1, SD = 6.5) participating in a cross-sectional investigation of risk factors for amnestic mild cognitive impairment (a-MCI) and probable Alzheimer’s Disease (AD). Participants were classified into three subgroups based on test performance and clinical judgement. Healthy controls ( n = 30) scored better than −1 SD relative to existing norms on all classification measures, in the absence of memory complaints or functional impairments. The a-MCI group ( n = 23) had self- or informant-reported memory complaints and scored 1 SD or more below the mean for at least one memory task while scoring better than 1 SD below the mean for all other cognitive domains, in the absence of functional impairments. The AD group ( n = 7) scored at least 2 SD below the mean for two cognitive domains (including memory) with impairments in functioning. Measures spanned a range of cognitive domains (episodic memory, executive function, language), with the derived dispersion estimates reflecting variability across an individual’s neuropsychological profile relative to the group average. Further, an Activities Lifestyle Questionnaire, indexing social, cognitive, and physical behaviors, was administered to assess the protective benefits of engaged lifestyle. Results : Multinomial logistic regression models examined the risk of being classified as a-MCI or AD as a function of increased dispersion, (dis)engaged lifestyle, and their interaction. Greater dispersion was associated with an increased likelihood of being classified with AD, with protective engaged-lifestyle benefits apparent for a-MCI individuals only. Conclusion : As a measure of IIV, dispersion across neuropsychological profiles holds promise for the detection of cognitive impairment.

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

“…Individual dispersion profiles are obtained by using a regression technique, which computes iSD scores from standardized test scores ( Christensen et al, 1999 ; Hultsch et al, 2002 ; Cole et al, 2011 ; Halliday et al, 2018 ). Test scores of interest (TMT-A, TMT-B/TMT-A, Digit Span Forward, Digit Span Backward, Verbal Fluency Phonemic, Verbal Fluency Semantic, Word Span average across trials 4, 5, and 7) were initially regressed on linear age trends across all participants, then the resulting residuals from these models were standardized as z-scores ( M = 0, SD = 1), with individual iSDs subsequently computed across these z -scores.…”

“…Reduced dispersion (cognitive de-differentiation) has been reported as a function of age across measures of speed of processing, working-memory, verbal fluency and lexical decision both in cross-sectional and longitudinal studies ( Li et al, 2004 ; Rabbitt et al, 2004 ). However, more recent studies have reported the opposite pattern, namely an increase in dispersion with increasing age ( Hultsch et al, 2000 ; Sosnoff and Newell, 2006 ; Hilborn et al, 2009 ; Halliday et al, 2018 ). These inconsistent results may derive from (i) differences in neuropsychological batteries, (ii) analysis adopted to compute dispersion index, and/or (iii) demographic differences in age groups across studies.…”

“…While the terms intra-individual variability, differentiation and dispersion have been used interchangeably in the literature, the term “de-differentiation” specifically refers to reduced intra-individual variability within person across tasks ( Balinsky, 1941 ; Juan-Espinosa et al, 2002 ). For clarity, here we will adopt the term ‘dispersion’ (or dispersion index) to refer to the variability in individuals’ performance across tasks, as defined in previous studies ( Sosnoff and Newell, 2006 ; Hilborn et al, 2009 ; Halliday et al, 2018 ).…”

“…In contrast to individual cognitive performance, the dispersion index is thought to provide an indicator of fairly stable endogenous factors, such as central nervous system (CNS) integrity ( MacDonald et al, 2006 , 2009 ), and to be less influenced by situation-dependent factors including fluctuations in stress or sleep ( Hultsch et al, 2000 ). Several studies have reported that a measure of dispersion can be a meaningful indicator of individual differences in behavioral cognitive integrity and neurological mechanisms over age, education and socio-economic status ( Hultsch et al, 2000 ; Rapp et al, 2005 ; Hilborn et al, 2009 ; Wojtowicz et al, 2012 ; Halliday et al, 2018 ). Behavioral studies have shown that greater dispersion across neuropsychological measures is associated with greater cognitive decline and poorer performance in healthy older adults ( Rapp et al, 2005 ; Hilborn et al, 2009 ; Wojtowicz et al, 2012 ; Halliday et al, 2018 ).…”

“…Several studies have reported that a measure of dispersion can be a meaningful indicator of individual differences in behavioral cognitive integrity and neurological mechanisms over age, education and socio-economic status ( Hultsch et al, 2000 ; Rapp et al, 2005 ; Hilborn et al, 2009 ; Wojtowicz et al, 2012 ; Halliday et al, 2018 ). Behavioral studies have shown that greater dispersion across neuropsychological measures is associated with greater cognitive decline and poorer performance in healthy older adults ( Rapp et al, 2005 ; Hilborn et al, 2009 ; Wojtowicz et al, 2012 ; Halliday et al, 2018 ). However, little attention has been given to the cognitive behavioral profile of older adults who do not show such dispersion, and fewer studies have specifically focused on variability in dispersion between old-age groups (e.g., see Hilborn et al, 2009 ).…”