Researchers at Stanford Psychiatry and University of Rochester awarded grant to monitor effective engagement in cognitive training

We are pleased to announce that Stanford Psychiatry’s Feng Vankee Lin, clinical professor of psychiatry and behavioral sciences, and Ehsan Adeli, clinical assistant professor of psychiatry and behavioral sciences, have received a grant from the National Institute on Aging titled, “A facial expression-based personalization engine (FPE) for monitoring and modulating real-time effective engagement in cognitive training in older adults at risk for AD/ADRD.” Dr. Lin and Dr. Adeli are joined by co-principal investigator Cristiano Tapparello at the University of Rochester.

Ensuring adherence to at-home or self-administered computerized cognitive training in older adults at risk for Alzheimer’s disease (AD) or AD related dementia (ADRD) is understudied. Based on previous work, the study team hypothesizes that mental fatigue revealed in facial expressions will reflect a degree of effective engagement, which can be modified by modulating task novelty. The study team will refine and test a novel facial expression-based personalization engine (FPE) that will monitor and modulate effective engagement in real-time. In the current application, they will test FPE in a cognitive training program called speed of processing training. However, FPE may be embedded to any computerized cognitive training in future studies to help address adherence-related issues.

“Ensuring adherence to cognitive training in unsupervised circumstances in older adults at risk for AD/ADRD may help promote the usage and benefit of cognitive training in slowing cognitive decline and AD/ADRD progression,” say Drs Lin, Adeli, and Tapparello.

Dr. Lin’s career is focused on understanding the neural mechanisms involved in brain aging and brain plasticity, with a special focus on early detection and prevention of AD. She currently leads an interdisciplinary clinical neuroscience lab conducting a wide spectrum of research on brain aging. Recent publications related to this work include, “A Multi-Dimensional Model of Fatigue in Old Age: Implications for Brain Aging,” published in the American Journal of Geriatric Psychiatry.

Dr. Adeli’s research is focused on interpretable machine learning algorithms for precision healthcare and computational neuroscience applications. His broader research aims to understand the underlying neural phenotypes and digital biomarkers of neurodegenerative and neuropsychiatric diseases. Recent publication related to this work include “Vision-based Estimation of Fatigue and Engagement in Cognitive Training Sessions” preprint and this work “CCA identifies a neurophysiological marker of adaptation capacity that is reliably linked to internal locus of control of cognition in amnestic MCI,” published in the journal Geroscience.

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For more information about this study, please visit NIH’s RePORTER