Establishing a Brain Health Index from the Sleep Electroencephalogram

从睡眠脑电图建立大脑健康指数

• 批准号: 10180268
• 负责人: SYDNEY S CASH
• 金额: $ 150.66万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10180268
• 关键词: 20 year old; Address; Age; Aging; Alzheimer' s Disease; Amyloid; Amyloid beta-Protein; Archives; Arousal; Biological; Biological Markers; Biological Process; Brain; Brain Diseases; Brain Pathology; Cerebrum; Chronic; Chronology; Claustral structure; Clinical; Clinical Trials; Cognitive; Cognitive aging; Cognitive deficits; Complex; Data; Dementia; Devices; Diabetes Mellitus; Disease; Drainage procedure; Early Diagnosis; Electroencephalogram; Epidemiology; Exhibits; Framingham Heart Study; General Hospitals; Genome; Goals; HIV; HIV Infections; Health; Heart; Hippocampus (Brain); Home; Hypertension; Impaired cognition; Individual; Intervention; Israel; Knowledge; Koreans; Life Expectancy; Link; Lung; Machine Learning; Magnetic Resonance Imaging; Massachusetts; Measurable; Measures; Medial; Medical center; Memory; Modeling; Motivation; Nerve Degeneration; Neurodegenerative Disorders; Neurologic; Neuropsychological Tests; Neuropsychology; Outcome; Pathology; Patient Care; Patients; Pattern; Physiological; Play; Population; Positron-Emission Tomography; Prefrontal Cortex; Process; Property; REM Sleep; Reporting; Research; Risk; Role; Science; Series; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Stages; Sleep disturbances; Smoker; Statistical Models; Structure; System; Test Result; Testing; Thick; Thinness; Time; Variant; Work; age related; aging brain; basal forebrain; base; brain health; cognitive function; cognitive performance; cognitive testing; cohort; deep neural network; epidemiology study; executive function; functional loss; glymphatic system; healthy aging; high risk; indexing; individual patient; large datasets; machine learning algorithm; memory consolidation; mild cognitive impairment; model development; mortality; multi-task learning; neuroimaging; neuropathology; non rapid eye movement; novel; novel therapeutics; predictive modeling; sleep physiology; sleep spindle; specific biomarkers; therapy development

Project Abstract: Establishing a Brain Health Index from the Sleep Electroencephalogram Although cognitive decline is a “normal” part of aging, some individuals clearly age better than others. However, the concept of differential aging has been minimally studied for the brain. Electroencephalogram (EEG) oscillations signals carry rich information regarding brain health and brain aging. Alzheimer's disease (AD) is associated with fragmented sleep and altered sleep oscillations. Clearance of cerebral beta amyloid through the brain's glymphatic drainage system occurs mainly in non-rapid eye movement (NREM) sleep, and depends on EEG slow oscillations. Cortical generators of sleep EEG oscillations overlap with regions of cortical thinning and loss of functional connectivity in AD. Disturbances of NREM disrupt memory consolidation. Finally, deficient REM sleep contributes to dementia. These observations suggest that brain health may be measurable from information contained in the sleep EEG. In preliminary work we have developed EEG-brain age – a machine learning model that predicts a patient's age based on patterns of overnight sleeping EEG oscillations. This allows prediction of age with a precision of +/- 7 years. Our preliminary data suggest diabetes and hypertension, chronic HIV infection, an MCI or AD are reflected in the EEG as excessive brain age, and that excessive brain age is independently associated with reduced life expectancy. Our central hypothesis is that sleep physiology data can provide sensitive and specific biomarkers of brain health. This hypothesis is based on our prior work showing that BAI is elevated in several clinical populations. BAI can be accurately calculated using frontal EEG signals, making it suitable for implementation on at-home EEG devices. The rationale for the proposed research is that validating sleep EEG-derived biomarkers as measures of brain health at the level of individual patients would lay the ground for use in clinical trials and patient care. We plan to accomplish the central objective by pursuing two complementary aims. In Aim 1, we will take a hypothesis-driven approach, and test for associations of specific sleep features with specific cognitive deficits and specific structural pathology. In Aim 2, we will take ad data-driven approach, and develop optimized biomarkers of brain health using a novel form of machine learning known as multitask learning, which combine multiple features of sleep – including conventional features, as well as data-driven features directly learned from the data – to predict or “explain” variation in cognitive performance and in structural brain MRI measures that are indicative of brain health or disease. The project will take advantage of a large and diverse set of sleep data (>33,000 patients), as well as thousands of brain MRI and cognitive testing results. At the conclusion of this study, we expect to have a better understanding of the role sleep oscillations play in brain health, and clinically useful brain health biomarkers. These outcomes will aid development of interventions to promote brain health.

项目摘要：通过睡眠脑电图建立大脑健康指数 尽管认知能力下降是衰老的“正常”一部分，但有些人显然比其他人年龄更好。 但是，差异衰老的概念已被最小化大脑的研究。 脑电图（EEG）振荡信号具有有关大脑健康和大脑衰老的丰富信息。 阿尔茨海默氏病（AD）与睡眠碎片和睡眠振荡改变有关。清除 通过大脑的糖化排水系统的脑β淀粉样蛋白淀粉样蛋白淀粉样蛋白主要发生在非比型眼中 运动（NREM）睡眠，取决于脑电图缓慢的振荡。睡眠脑电图的皮质发电机 振荡与AD中皮质稀疏区域和功能连通性丧失重叠。干扰 NREM破坏内存整合。最后，REM睡眠不足会导致痴呆症。这些观察 表明可以通过睡眠脑电图中的信息来测量大脑健康。 在初步工作中，我们开发了EEG-Brain年龄 - 一种机器学习模型，可以预测患者的 年龄基于过夜的脑电图振荡的模式。这允许年龄的预测精确 +/- 7年。我们的初步数据表明糖尿病和高血压，慢性HIV感染，MCI或AD是 反映在脑电图中是过度的大脑年龄，并且过多的脑年龄与 降低了预期寿命。 我们的中心假设是睡眠生理学数据可以提供敏感和特定的大脑生物标志物 健康。该假设是基于我们先前的工作，表明在几个临床人群中BAI升高。 可以使用额叶脑电图准确地计算BAI，使其适合于在家实施 脑电图。拟议的研究的理由是，验证睡眠脑电图衍生的生物标志物是 个别患者水平的大脑健康措施将为临床试验奠定基础 病人护理。我们计划通过追求两个完整的目标来实现中心目标。在AIM 1中，我们 将采用假设驱动的方法，并测试特定睡眠特征的关联 认知定义和特定的结构病理。在AIM 2中，我们将采用数据驱动的方法，并开发 使用一种新型的机器学习形式（称为多任务学习），优化了大脑健康的生物标志物， 结合了睡眠的多个功能 - 包括常规功能以及数据驱动的功能 直接从数据中学习 - 预测或“解释”认知性能和结构大脑的变化 指示大脑健康或疾病的MRI措施。该项目将利用大型 多种睡眠数据（> 33,000名患者）以及数千个大脑MRI和认知测试结果。 在这项研究结束时，我们希望更好地了解睡眠振荡在 大脑健康和临床上有用的大脑健康生物标志物。这些结果将有助于发展 促进大脑健康的干预措施。