Computational phenotyping of cognitive decline with retest learning

Abstract

INTRODUCTION: Cognitive change is a complex phenomenon encompassing both retest-related performance gains and potential cognitive decline. Disentangling these dynamics is necessary for effective tracking of subtle cognitive change and risk factors for ADRD. METHODS: We applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (n = 316). EAS participants completed multiple bursts of ultra-brief, high-frequency cognitive assessments on smartphones. Analyzing response time data, we extracted several key cognitive markers: short-term intraindividual variability in performance, within-burst retest learning and asymptotic (peak) performance, across-burst change in asymptote and forgetting of retest gains. RESULTS: Asymptotic performance was related to both MCI and age, and there was evidence of asymptotic slowing over time. Long-term forgetting, learning rate, and within-person variability uniquely signified MCI, irrespective of age. DISCUSSION: Computational cognitive markers hold promise as sensitive and specific indicators of preclinical cognitive change, aiding risk identification and targeted interventions