Current studies

The goal of this project is to uncover the link between life-course sleep behaviors, 24-hour circadian rhythms, and neurodegenerative diseases. The risk of neurodegenerative disease may begin to develop as early as in utero. Therefore, the need for a life course approach to study cognitive aging has gained increasing attention. A life course approach studies risk factors from all stages of the life course that influence health and aging and it emphasizes the need to understand the aging process across the whole of life. This is particularly relevant to the study of Alzheimer's disease and related dementias, given the long disease process, the distinct life-course trajectories of cognitive function between individuals, and the various factors that might affect cognitive trajectories. We leverage longitudinal sleep and health data collected in multiple large-scale cohort studies to investigate neurodegenerative disease risk across the life span.

This project focuses on AI in Sleep Medicine. We have an unprecedented opportunity to leverage multidimensional, multimodal polysomnography (PSG) sleep data from three NIH-supported multicenter longitudinal cohorts: a diverse clinical sleep cohort, consisting of over 70K subjects aged 50 years and older with 15 years of follow-up, and two community-based cohorts, with over 3500 community-dwelling older adults followed for up to 13 years. Using state-of-the-art AI models, we will discover PSG biomarkers that identify current and future diagnoses of Alzheimer's disease and Parkinson's disease in clinical settings. We plan to validate the performance and generalizability of the PSG biomarkers in community settings.

This project focuses on wearable technology in sleep. We aim to improve in-home sleep assessment through studies focused on the application of wearable and nearable sleep technology in real-world settings.

This project focuses on unraveling the causal relationships between sleep, circadian rhythms, and neurodegenerative diseases. We seek to do so through the study of gene-environment interactions and by leveraging biobank data and novel statistical methods such as Mendelian randomization.