The space-time organization of sleep oscillations as potential biomarker for hypersomnolence

项目摘要 睡眠过度(HYP)，或白天过度嗜睡，是在 睡眠医学，并可呈现为与其他医学障碍相联系或独立。辨别性 在HYP的多种可能原因中，是一个复杂的过程，其根本原因往往是 未知。此外，目前还没有可靠的电生理参数或生物标志物 Hyp，这是对诊断和治疗过程的严重限制。了解 在睡眠脑动力学中HYP的生物物理表现对两者的可靠识别是必不可少的 电生理生物标志物与构建生理学机制的认识 HYP的表现。 睡眠脑电动力学的大多数研究集中于按其主导频率统一分组的节律， 有时解决他们的空间呈现，但总体上忽略了睡眠节奏的清晰度 时空有组织的活动。在最近对典型成年人群的研究中，PI引入了数据- 揭示慢振荡(SOS)和纺锤波的时空模式的驱动技术，两者都是睡眠 节奏对睡眠的动态平衡至关重要，SOS明确地与一夜睡眠的恢复性联系在一起。 这条研究路线还表明，在时空模式中区分睡眠节奏是一种强大的 揭示同一“韵律”事件之间生物物理差异的方法 这表明它们对睡眠功能的潜在差异贡献。在这里，我们建议应用这些 数据驱动的方法来详细描述睡眠脑动力学中HYP的时空呈现， 为了确定高血压病的生物标志物，加深对高血压病临床表现的认识 大脑活动对健康和认知很重要。 这项研究将重新分析一个具有良好特征的数据集，包括对HYP患者的睡眠研究 和对照组，这两组也是基于有无严重抑郁障碍而表达出来的。 具体地说，我们将描述头皮上SOS和纺锤波的时空模式及其主要 生物物理特性，比较HYP组和对照组(目标1)。然后我们将使用 机器学习分类以确定每个个体的估计皮质-皮质下电流 最能区分SOS时空类型，比较HYP和CONTROLS的结果(目标2)。最后， 我们将在统计学上评估这些生物物理量度指标之间的联系。 以及对HYP症状、抑郁和焦虑的临床/行为评估。这项研究将导致 对构成HYP的潜在大脑机制的新见解，以及改进的诊断和未来 对患有HYP障碍的人群的治疗。

Project Summary Hypersomnolence (HYP), or excessive daytime sleepiness, is the most common symptom encountered in sleep medicine, and can present as linked to other medical disorders or independently. Discriminating among the multiple possible causes of HYP is a complex process, and the underlying cause is often unknown. Furthermore, there are currently no reliable electrophysiological parameters or biomarkers for HYP, which is a severe limitation to the diagnostic and therapeutic process. Understanding the biophysical presentation of HYP in sleep brain dynamics is essential to both the identification of reliable electrophysiological biomarkers and to building a mechanistic understanding of the physiological manifestations of HYP. Most studies of sleep EEG dynamics focus on rhythms uniformly grouped by their dominant frequency, sometimes addressing their spatial presentation, but overall ignoring the articulation of sleep rhythms in space-time organized events. In recent work on typical adult populations the PI has introduced data- driven techniques that reveal the space-time patterns of slow oscillations (SOs) and spindles, both sleep rhythms cardinal to sleep homeostasis, with SOs explicitly tied to the restorative-ness of a night of sleep. This research line has also shown that differentiation of sleep rhythms in space-time patterns is a powerful approach to revealing biophysical differentiation among events classified as the same “rhythm” suggesting their potential differential contribution to sleep functions. Here, we propose to apply these data-driven approaches to describe in detail the space-time presentation of HYP in sleep brain dynamics, in order to determine HYP biomarkers and to advance our understanding of the manifestations of HYP in brain activity important to health and cognition. This study will re-analyze a well-characterized dataset including the sleep studies of persons with HYP and controls, with both groups also articulated based on presence/absence of major depressive disorder. Specifically, we will describe the space-time patterns of SOs and spindles on the scalp and their main biophysical properties, comparing them among the HYP and control group (aim 1). We will then use machine learning classification to determine for each individual the estimated cortical-subcortical currents that most differentiate SOs space-time types, compare the results in HYP and controls (aim 2). Finally, we will statistically evaluate the link between these biophysical quantifiers of space-time sleep patterns and clinical/behavioral assessments of HYP symptoms, depression, and anxiety. This research will lead to new insights into potential brain mechanisms that underlie HYP, as well as refined diagnostic and future therapies for the multitude suffering with HYP disorder.