Tuesday, April 1, 2025
4/1/2025
Multi-timescale process models to disentangle subtle cognitive decline and learning effects - Project Summary Sensitive and accurate 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. Although researchers and clinicians are often interested in detecting long-timescale patterns of change (i.e., normative aging vs. disease progression), learning processes on short and long-timescales confound such effects. To address these challenges, we will develop a modern statistical toolset designed for use with data from high- frequency repeated assessments. For this, we will combine longitudinal measurement “burst” designs with a novel Bayesian computational toolkit to simultaneously capture multi-timescale learning processes together with cognitive change and decline. These tools will provide interpretable features (e.g., change in peak performance, probability of decline, caution in decision making, etc.) that can then be deployed as digital markers of subtle cognitive decline. The Bayesian approach will also provide for a principled framework to communicate individual-specific dementia risks towards clinicians. Our specific aims are to: 1. Separate multi-timescale learning processes from cognitive changes related to aging and neurodegeneration and extract key digital cognitive markers to identify digital computational phenotypes of ADRD risk. 2. Disentangle cognitive processes in task performance by developing novel statistical tools that quantify latent processes to enrich our set of novel digital cognitive markers. 3. Identify which combination of digital cognitive markers and test interval between measurement bursts carries the most power for predicting ADRD risk. For Aim 1 we will analyze two measurement burst design data sets. To further refine the framework in Aims 2 and 3, we develop novel statistical tools and collect data with fine-tuned, novel feature binding tasks known to be sensitive for preclinical AD. We will evaluate the predictive power of the identified novel digital cognitive markers by linking them to AD biomarkers levels and ADRD risk scores.
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