Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States.
Elife. 2024 Sep 27;13:RP98739. doi: 10.7554/eLife.98739.
Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16 hr fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.
理解睡眠的功能需要研究整个夜间睡眠及其转变过程中大脑活动的动态。然而，目前的金标准多导睡眠图（PSG）在跟踪大脑活动方面的空间分辨率有限。此外，之前的 fMRI 研究时间太短，无法捕捉到完整的睡眠阶段及其循环。为了研究整个大脑在整个夜间睡眠中的动态和转变，我们使用了一种无监督学习方法，即隐马尔可夫模型（HMM），对 12 名非睡眠剥夺的参与者进行了两晚、16 小时的 fMRI 记录，这些参与者达到了所有基于 PSG 的睡眠阶段。该方法除了 PSG 定义的睡眠阶段外，还确定了 21 种重复出现的大脑状态及其转换概率。在一夜之间训练的 HMM 可以准确预测另一夜的情况，显示出前所未有的可重复性。我们还在快速眼动（REM）和非快速眼动 2 阶段内发现了具有功能相关性的细分。这项研究为睡眠期间的大脑动态和转变提供了新的见解，有助于我们理解影响睡眠转变的睡眠障碍。
