The utility of global versus domain-specific neuropsychological test score dispersion as markers of cognitive impairment

Objective Cognitive dispersion (CD) or neuropsychological test score variability has been associated with more rapid cognitive decline, and the development of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The predictive utility of global (cross-domain) versus domain-specific dispersion remains understudied. We examined whether baseline global versus domain-specific CD better predicted conversion to MCI or dementia over time.Methods 1,595 participants (Mage = 71.41, SD = 7.73) from the National Alzheimer’s Coordinating Center (NACC) dataset were followed for ≥4 visits. Baseline CD was calculated using standardized neuropsychological test scores, with global dispersion defined as the intraindividual standard deviation (ISD) across 10 NACC scores. Domain-specific dispersion was calculated by constructing composites with ISD across tests within three domains (Memory, Language, and Executive functioning/attention/processing speed [EFAS]). Multinomial logistic regression model fit statistics were compared across four dispersion models (global, EFAS, language, memory) in predicting progression to MCI and dementia in (1) the full non-dementia sample and (2) those cognitively normal at baseline only, controlling for demographics, APOE4 status, and MMSE. Model fit was evaluated using LRT and AIC. Follow-up hierarchical regressions assessed the incremental value of the most successful dispersion metric beyond mean EFAS and memory composite scores.Results In the full sample, 25% were considered to have MCI at follow-up, and 20% developed dementia, whereas among those considered cognitively normal at baseline (n = 1166), 17.6% progressed to MCI, and 11% progressed to dementia in their follow-up. In the overall sample, global dispersion was the only significant predictor of dementia diagnosis (AIC = 2504.27, p < .001) with moderate classification accuracy: 62.6%. Adding global dispersion to a model with covariates + mean EFAS and memory composite performances did not improve prediction (Δχ2 = 1.803, p = .179). Among cognitively normal at baseline, only EFAS dispersion predicted dementia conversion, and classification accuracy remained moderate, though it was increased (71.7%). Adding EFAS dispersion to a model with covariates and mean composite performance also did not improve prediction (Δχ2 = .614, p = .433). None of the dispersion metrics predicted conversion to MCI.Conclusions Dispersion (global in a non-dementia sample or domain-specific [EFAS] among cognitively asymptomatic individuals) may show limited predictive value for dementia conversion, but it does not exceed traditional mean-based cognitive performance, highlighting its complementary, rather than superior, role in diagnostic prediction.