Research Project
10485503
Project Summary/Abstract Sensitive and accurate repeated measurement of change in cognitive performance is necessary for the detection of subtle cognitive decline in the preclinical phase of Alzheimer?s disease and related dementias (ADRD). It is also required to evaluate outcomes of early interventions aimed at mitigating advanced cognitive decline. However, this is a difficult task as these changes are subtle not only in terms of magnitude, but also in terms of the latent processes through which they manifest. In longitudinal studies researchers and clinicians are often interested in detecting long-timescale (i.e., normative aging vs. disease progression) change patterns; however retest related learning processes on short and long-timescales often confound these effects. To address these challenges, we propose to develop a modern statistical toolset designed for use with data from high-frequency repeated assessments over time. For this, we will combine longitudinal measurement ?burst? designs and a novel Bayesian statistical toolkit to capture learning together with cognitive change and decline. These tools will provide interpretable parameters (e.g., change in peak performance, probability of decline, caution in decision making, etc.) that can then be deployed as digital biomarkers of subtle cognitive decline. The Bayesian framework will also provide for a principled framework to communicate individual- specific dementia risks towards clinicians. Our specific aims are to: 1. Disentangle cognitive change and decline, for example due to aging and/or disease progression, from learning during repeated assessments over time, by implementing the multi- timescale Bayesian double exponential learning model. This work can identify digital biomarkers of cognitive decline associated with ADRD that are not confounded by learning effects. 2. Study individual differences in learning across multiple timescales and their links to normative aging, and ADRD risks. This work can identify digital biomarkers of cognitive decline associated with ADRD that articulated in terms of features of learning. 3. Delineate normative cognitive aging, ADRD risk, and features of learning by mapping them onto latent cognitive sub-processes during task performance learning by developing the Drift Diffusion Double Exponential Model framework. This work can identify digital biomarkers of cognitive decline associated with ADRD that relate to sub-processes of cognitive performance while also accounting for learning, as well as characterize sub-processes in which learning occurs.
