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

睡眠振荡的时空组织作为嗜睡的潜在生物标志物

• 批准号: 10731224
• 负责人: Paola Malerba
• 金额: $ 12.72万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-17 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731224
• 关键词: Acceleration; Address; Adult; Anxiety; Articulation; Behavior assessment; Behavioral; Biological Markers; Biophysics; Brain; Central Nervous System; Characteristics; Classification; Clinical; Cognition; Cognitive; Complex; Control Groups; Data; Data Set; Detection; Diagnostic; Disease; Disorders of Excessive Somnolence; Electrodes; Electroencephalography; Electrophysiology (science); Elements; Event; Excessive Daytime Sleepiness; Frequencies; Future; Health; Homeostasis; Hypersomnias; Hypersomnolence; Individual; Intervention; Investigation; Linear Models; Link; Machine Learning; Major Depressive Disorder; Measures; Medical; Medicine; Mental Depression; Methodology; Neuropsychology; Obstructive Sleep Apnea; Outcome Assessment; Parietal; Participant; Pattern; Persons; Phenotype; Physiological; Population; Prevalence; Process; Property; Quality of life; Research; Role; Scalp structure; Sleep; Sleep Fragmentations; Statistical Study; Symptoms; Techniques; Testing; Therapeutic; Time; Work; advanced analytics; biomarker identification; biophysical properties; common symptom; comorbidity; density; diagnostic criteria; economic impact; follow-up; improved; insight; machine learning classification; memory consolidation; memory process; neural; non rapid eye movement; population based; potential biomarker; sleep pattern; socioeconomics

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.

项目摘要 嗜睡症（HYP），或白天过度嗜睡，是最常见的症状， 睡眠药物，并可能与其他医学疾病相关或独立存在。判别 在HYP的多种可能原因中，有一个复杂的过程，其根本原因通常是 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 HYP在睡眠脑动力学中的生物物理表现对于识别可靠的 电生理学生物标志物，并建立一个机制的理解， HYP的表现。 大多数睡眠EEG动力学的研究都集中在按其主频率均匀分组的节律上， 有时解决他们的空间表现，但总体上忽略了睡眠节律的表达， 时空组织活动。在最近对典型成年人群体的研究中，PI引入了数据- 驱动技术，揭示了慢振荡（SO）和纺锤波的时空模式，两者都是睡眠 睡眠节律是睡眠稳态的基础，SO与一夜睡眠的连续性有着明确的联系。 这项研究还表明，睡眠节律在空间-时间模式中的分化是一个强大的 揭示被归类为相同“节律”的事件之间的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HYP在睡眠脑动力学中的时空呈现， 为了确定HYP的生物标志物，并促进我们对HYP在 大脑活动对健康和认知很重要。 这项研究将重新分析一个充分表征的数据集，包括HYP患者的睡眠研究 和对照组，两组也基于是否存在重度抑郁症进行阐述。 具体来说，我们将描述头皮上SO和纺锤体的时空模式及其主要的 生物物理特性，比较HYP和对照组之间的差异（目的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和对照的结果（目的2）。最后， 我们将从统计学上评估这些时空睡眠模式的生物物理量化指标之间的联系， 以及HYP症状、抑郁和焦虑的临床/行为评估。这项研究将导致 新的见解潜在的大脑机制，基础HYP，以及完善的诊断和未来 治疗患有HYP疾病的人群。