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ECG personalization engine for optimizing cognitive training

Research Background

Cognitive training is considered one of the major interventions that have the potential to slow cognitive decline in older adults, including those with MCI. Research from our lab shows that cognitive training modifies neural circuits that support multiple cognitive and functional domains, and the training effect on cognitive functions is relatively selective and transitory in older adults at risk for dementia. A possible solution is to consider the autonomic nervous system (ANS) response since it is strongly related to a broad spectrum of cognitive functions and brain circuits (e.g., central autonomic networks) in old age. Notably, ANS supports overall adaptation to environmental demands (regardless of neurodegeneration), and its regulatory capacity is related to better physiological, emotional, and cognitive regulation. By using emerging technologies and AI-driven analytical methods, we can personalize the cognitive training of a person by analyzing ECG features that are correlated to learning and brain function. With these characteristics, the cognitive training activities are modified in real-time. The goal of this study is to create a "personalization engine" that captures novel ECG patterns representing learning and brain function from our previous work and use such patterns to guide real-time adaptation of computerized cognitive training tasks. The “personalization engine” has the potential to strengthen the effect of existing cognitive training programs on slowing cognitive decline in general.

Study Aims

  • Develop a human-machine interface communication program for creating the “personalization engine”.

  • Test the feasibility of incorporating the “personalization engine” to a traditional cognitive training program in older adults with MCI.


Study recruitment occurs at Stanford University.

Study Participation

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