Quantifying longitudinal cognitive resilience to Alzheimer's disease and other neuropathologies

Abstract

Introduction: Cognitive resilience (CR) has been defined as the continuum of better (or worse) than expected cognition, given the degree of neuropathology. To quantify this concept, existing approaches focus on either cognitive level at a single time point or slopes of cognitive decline.

Methods: In a prospective study of 1215 participants, we created a continuous measure of CR defined as the mean of differences between estimated person-specific and marginal cognitive levels over time, after accounting for neuropathologies.

Results: Neuroticism and depressive symptoms were associated with all CR measures (P-values < .012); as expected, cognitive activity and education were only associated with the cognitive-level approaches (P-values < .0002). However, compared with the existing CR measures focusing on a single measure or slopes of cognition, our new measure yielded stronger relations with risk factors.

Discussion: Defining CR based on the longitudinal differences between person-specific and marginal cognitive levels is a novel and complementary way to quantify CR.

Keywords: Alzheimer's dementia; cognitive resilience; longitudinal study; mixed-effects model; neuropathology; neuropsychological tests.

Figure 1.

Graphs illustrating the three strategies used to quantify cognitive resilience (CR) at the individual level. The graphs each represent an individual with 5 repeated observations of global cognition collected over 12 years preceding death. The CR_{Last_level} (panel A) is computed as the standardized difference between the observed and the estimated levels of global cognition (i.e., standardized residuals) proximate to death from a linear regression model accounting for demographics and neuropathologic indices. The ${\text{CR}}_{\text{slope}}$ (panel B) is defined as the person-specific deviation from the rate of cognitive decline expected at the population level (i.e., random slope), estimated in a latent process mixed-effects (LPM) model assuming linearity and adjusted for demographic and neuropathologic indices. Finally, $\text{C}{\text{R}}_{\overline{\text{Level}}}$(panel C) corresponds to the mean of the differences between the person-specific and the marginal estimations of global cognition at each measurement time estimated in a LPM model allowing non-linearity and adjusted for demographic and neuropathologic indices. For ${\text{CR}}_{\text{slope}}$ and $\text{C}{\text{R}}_{\overline{\text{Level}}}$ (panels C–D), marginal trajectories represent the expected shape of cognition among individuals sharing similar profile of covariates (demographics and pathologic indices) as the theoretical individual.

Figure 2.

Illustrative examples of two participants with varying z-scores of cognitive resilience (CR) and different estimated trajectories of cognition before death: Religious Orders Study and Rush Memory and Aging Project.

Figure 3.

Distributions of ${\text{CR}}_{\text{Last\_level}}$, ${\text{CR}}_{\text{slope}}$, and $\text{C}{\text{R}}_{\overline{\text{Level}}}$, and scatterplots of the relationship between ${\text{CR}}_{\text{Last\_level}}$ and $\text{C}{\text{R}}_{\overline{\text{Level}}}$ (panel A), and ${\text{CR}}_{\text{slope}}$ and $\text{C}{\text{R}}_{\overline{\text{Level}}}$ (panel B); Religious Orders Study and Rush Memory and Aging Project (n=1,215).