AI 中文总结
研究利用人工智能睡眠联合科学家环境，在约124,000份PSG记录上进行五个案例研究，揭示睡眠与疾病风险、临床分类及自身调节的联系，展示智能体人工智能助力大规模、多模态睡眠生理学发现。
AI 中文摘要
睡眠约占人类生命的三分之一，但其生理学的许多方面仍知之甚少。大型多导睡眠图（PSG）数据集为研究睡眠及其与疾病的联系提供了新机会，但从这些记录中提取见解需要大量专家工作，对通用人工智能系统来说也很困难。我们开发了人工智能睡眠联合科学家，这是一个由专家指导的环境，人类科学家指导专业智能体进行假设开发、信号预处理和统计分析，并审查中间输出。每个报告的结果都与产生它的可执行代码相关联。在四个队列的约124,000份PSG记录和超过50TB的原始信号上，我们进行了五个案例研究，涵盖睡眠生理学与未来疾病的关系、如何区分临床表型以及睡眠如何组织和调节。睡眠期间网络水平生理耦合减弱与帕金森病（HR 1.48）和阿尔茨海默病（HR 1.38）的发生有关。一种生理结构的晚期融合睡眠年龄模型优于无约束的早期融合方法，其年龄残差与多个器官系统的疾病发生有关。觉醒动态将共病失眠和睡眠呼吸暂停表征为偏向阻塞性睡眠呼吸暂停的中间表型，其特征是觉醒后清醒时间延长。快速眼动（REM）发作持续时间与之前的非快速眼动睡眠比与中间的清醒状态更密切相关。瞬态振荡分析在1型发作性睡病中发现了快速西格玛缺陷和额中央theta活动过多。这些发现共同将睡眠与疾病风险、临床分类及其自身调节联系起来，并展示了智能体人工智能如何支持大规模、多模态发现。
英文摘要
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.
