Abstract

Even though there is a clear link between Alzheimer’s Disease (AD) related neuropathology and cognitive decline, numerous studies have observed that healthy cognition can exist in the presence of extensive AD pathology, a phenomenon sometimes called Cognitive Resilience (CR). To better understand and study CR, we develop the Alzheimer’s Disease Cognitive Resilience Score (AD-CR Score), which we define as the difference between the observed and expected cognition given the observed level of AD pathology. Unlike other definitions of CR, our AD-CR Score is a fully non-parametric, stand-alone, individual-level quantification of CR that is derived independently of other factors or proxy variables. Using data from two ongoing, longitudinal cohort studies of aging, the Religious Orders Study (ROS) and the Rush Memory and Aging Project (MAP), we validate our AD-CR Score by showing strong associations with known factors related to CR such as baseline and longitudinal cognition, non AD-related pathology, education, personality, APOE, parkinsonism, depression, and life activities. Even though the proposed AD-CR Score cannot be directly calculated during an individual’s lifetime because it uses postmortem pathology, we also develop a machine learning framework that achieves promising results in terms of predicting whether an individual will have an extremely high or low AD-CR Score using only measures available during the lifetime. Given this, our AD-CR Score can be used for further investigations into mechanisms of CR, and potentially for subject stratification prior to clinical trials of personalized therapies.

Highlights

  • Alzheimer’s Disease (AD) is a debilitating, irreversible, and progressive brain disorder that destroys memory and cognitive skills

  • We provide a quantitative definition of cognitive resilience (CR) with the AD-CR Score; given the AD related pathology, the AD-CR Score is the difference between the observed and expected cognition

  • Proxy variables are a poor surrogate for CR as the variables used are not standardized among individuals and are correlated with other variables known to be associated with AD

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Summary

Objectives

Our objective is to develop a quantitative definition of CR in AD that is stand-alone, nonparametric, and produces individual quantifications independent of other measures

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