Abstract
Identifying individuals at risk of cognitive decline is only half the problem. The other half — and arguably the harder half — is determining what to do about it. Cognitive training programs exist, but they are overwhelmingly designed as one-size-fits-all protocols: every participant receives the same type of training, at the same intensity and frequency, regardless of their individual cognitive profile, risk factors, or response trajectory. This is analogous to prescribing the same medication at the same dose to every patient, and it is almost certainly suboptimal.
This project aims to change that by developing a mathematical “digital twin” framework for personalized cognitive intervention. The concept is borrowed from engineering, where digital twins — computational replicas of physical systems — are used to simulate and optimize performance before making changes to the real system. Applied to cognitive health, a digital twin of a patient’s cognitive trajectory would allow researchers and clinicians to simulate the expected impact of different intervention strategies in silico: What happens if we increase training frequency? Switch modalities? Adjust intensity as the patient responds? The answers to these questions, explored computationally rather than through trial-and-error on real patients, could dramatically improve intervention design.
The framework couples a dynamical systems model of cognitive trajectory with reinforcement-learning-based optimization of intervention parameters — including training intensity, frequency, and modality. The reinforcement learning agent learns to select intervention configurations that maximize projected cognitive outcomes for a given individual, adapting as new data on the patient’s response becomes available. The project also includes developing novel approaches for estimating cognitive function that integrate behavioral and neuropsychological measures, providing the outcome signal that the optimization system requires.
This work is funded by the CEAT Engineering Research and Seed Funding Program ($25,000; Co-PI) and represents the first stage of what I envision as a multi-phase program. The current stage produces the mathematical framework and proof-of-concept results needed for a larger NIH/NIA proposal, which would integrate real-world sensor data and robot-assisted training platforms to close the loop between simulation and physical intervention.
The manuscript is in preparation for PLOS Computational Biology.