Agentic AI-enabled discovery across large-scale sleep physiology
Organizations: Department of Biomedical Data Science, Stanford University, Stanford, CA, USA · Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark · Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Glostrup, Denmark · Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA · Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA · Together AI, USA · Department of Neurology, Washington University School of Medicine in St. Louis, MO, USA · Consortium for Biomedical Research and AI in Neurodegeneration (C-BRAIN), USA · Department of Computer Science, Stanford University, Stanford, CA, USA
Abstract
Sleep occupies roughly one-third of human life, yet many aspects of its physiology remain poorly understood. Large polysomnography (PSG) datasets offer new opportunities to study sleep and its links to disease, but extracting insight from these recordings requires substantial expert effort and remains difficult for general-purpose AI systems. We developed AI Sleep Co-Scientist, an expert-guided environment in which human scientists direct specialist agents for hypothesis development, signal preprocessing, and statistical analysis, reviewing intermediate outputs. Each reported result is linked to the executable code that produced it. Across four cohorts of approximately 124,000 PSG recordings and more than 50 TB of raw signals, we conducted five case studies spanning how sleep physiology relates to future disease, how it distinguishes clinical phenotypes, and how sleep is organized and regulated. Diminished network-level physiological coupling during sleep was associated with incident Parkinson's disease (HR 1.48) and Alzheimer's disease (HR 1.38). A physiologically structured late-fusion sleep-age model outperformed an unconstrained early-fusion approach, and its age residual was associated with incident disease across multiple organ systems. Arousal dynamics characterized comorbid insomnia and sleep apnoea as an intermediate phenotype skewed towards obstructive sleep apnoea, distinguished by prolonged post-arousal wakefulness. Rapid eye movement (REM) bout duration tracked preceding non-REM sleep more closely than intervening wakefulness. Transient-oscillation analysis identified a fast-sigma deficit and excess centrofrontal theta activity in narcolepsy type 1. Together, these findings connect sleep to disease risk, clinical classification, and its own regulation, and show how agentic AI can support large-scale, multimodal discovery.