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.

项目概要 嗜睡症 (HYP)，或白天过度嗜睡，是最常见的症状 睡眠医学，可以与其他疾病相关或独立存在。 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 此外，目前还没有可靠的电生理参数或生物标志物。 HYP，严重限制了诊断和治疗过程。 HYP 在睡眠大脑动力学中的生物物理表现对于识别可靠的 电生理生物标志物并建立对生理学的机械理解 HYP 的表现。 大多数睡眠脑电图动态研究都集中在按主导频率统一分组的节律上， 有时关注它们的空间呈现，但总体上忽略了睡眠节律的表达 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（SO）和纺锤体睡眠时空模式的驱动技术 节律对睡眠稳态至关重要，SO 与夜间睡眠的恢复能力明确相关。 该研究还表明，时空模式中睡眠节律的区分是一种强大的方法。 揭示被归类为相同“节奏”的事件之间生物物理差异的方法 在这里，我们建议应用它们对睡眠功能的潜在差异贡献。 数据驱动的方法来详细描述睡眠大脑动力学中 HYP 的时空表现， 为了确定 HYP 生物标志物并加深我们对 HYP 表现的理解 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，两组也根据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特征。 生物物理特性，在 HYP 和对照组之间进行比较（目标 1）。 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 SO 的时空类型，比较 HYP 和对照的结果（目标 2）。 我们将仔细评估这些时空睡眠模式的生物物理量词之间的联系 这项研究将导致 HYP 症状、抑郁和焦虑的临床/行为评估。 对 HYP 潜在大脑机制的新见解，以及精细的诊断和未来 针对 HYP 障碍患者的治疗方法。