Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.

Sa Zhou, Yang Liu, Adam Turnbull, Cristiano Tapparello, Ehsan Adeli, F Vankee Lin

Department of Psychiatry and Behavioral Sciences, Stanford University, United States. Electronic address: [email protected] of Psychiatry and Behavioral Sciences, Stanford University, United States. Electronic address: [email protected]. · pubmed

Paper

Abstract

Emerging digitally delivered non-pharmacological interventions (dNPIs) offer scalable, low-risk solutions for enhancing cognitive function in older adults, yet their effectiveness remains inconsistent due to a lack of personalization and precise mechanisms of action. Generic, population-based designs often fail to predict individual gains, underscoring the need for more tailored approaches. To address this, we propose a closed-loop human-machine interface (HMI) framework for personalizing dNPIs by optimizing the engagement of neurocognitive resources for cognitive enhancement. Our framework tackles three major challenges: (1) comprehensive and effective neurobehavioral representations for cognitive decoding, (2) tailoring interventions for domain-specific cognitive processes, and (3) ensuring aging-friendly design on usability, validity, and reliability for long-term adherence. We provide reviews and perspectives to guide the development of closed-loop HMIs by outlining the operational details of three key components-sensor, controller, and external actuator-that monitor, analyze, and modulate neurobehavioral activities through real-time adaptive interventions. Centering on neurobehavioral characteristics of older adults, we propose to advance closed-loop HMIs toward (1) deploying multimodal sensor network that captures activities from both central and peripheral nervous systems, (2) artificial intelligence (AI)-powered cognitive decoding and modulation that integrates multi-modal easy-to-acquire neurobehavioral signals and predicts the cross-modal harder-to-acquire signals, and (3) targeting neurobehavioral processes via internal and/or external regulation. We envision that the proposed closed-loop HMI framework could provide personalized dNPI with enhanced effectiveness and scalability for cognitive enhancement in older adults, promoting brain resilience and healthy longevity in the aging population.

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Research summary

The paper proposes a closed-loop human-machine interface framework for personalizing digital non-pharmacological interventions to enhance cognitive function in older adults. This research is relevant as it addresses cognitive enhancement strategies that could contribute to healthier aging and potentially improve longevity by promoting brain resilience.

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