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：从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”部分，但有些人显然比其他人更容易衰老。 然而，对于大脑的差异老化概念的研究却很少。 脑电图 (EEG) 振荡信号携带有关大脑健康和大脑衰老的丰富信息。 阿尔茨海默病 (AD) 与睡眠碎片化和睡眠波动改变有关。清关 脑β淀粉样蛋白通过大脑的类淋巴引流系统主要发生在非快速眼 运动（NREM）睡眠，并且依赖于脑电图缓慢振荡。睡眠脑电图的皮质发生器 振荡与 AD 中皮质变薄和功能连接丧失的区域重叠。的干扰 NREM 会破坏记忆巩固。最后，快速眼动睡眠不足会导致痴呆。这些观察 表明大脑健康可以通过睡眠脑电图包含的信息来测量。 在前期工作中，我们开发了脑电图脑年龄——一种预测患者年龄的机器学习模型 基于夜间睡眠脑电图振荡模式的年龄。这使得年龄预测的精度为 +/- 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 脑电图反映为大脑年龄过大，而大脑年龄过大与 预期寿命减少。 我们的中心假设是睡眠生理学数据可以提供敏感且特定的大脑生物标志物 健康。这一假设基于我们之前的研究，该研究表明 BAI 在几个临床人群中升高。 使用额叶脑电信号可以准确计算 BAI，适合在家中实施 脑电图设备。拟议研究的基本原理是验证睡眠脑电图衍生的生物标志物 在个体患者水平上测量大脑健康状况将为临床试验和使用奠定基础 病人护理。我们计划通过追求两个互补的目标来实现中心目标。在目标 1 中，我们 将采用假设驱动的方法，并测试特定睡眠特征与特定睡眠特征之间的关联 认知缺陷和特定的结构病理学。在目标2中，我们将采取广告数据驱动的方法，并开发 使用一种称为多任务学习的新型机器学习来优化大脑健康的生物标志物， 结合了睡眠的多种特征——包括传统特征，以及数据驱动的特征 直接从数据中学习——预测或“解释”认知表现和大脑结构的变化 指示大脑健康或疾病的 MRI 测量。该项目将利用大而 不同的睡眠数据集（>33,000 名患者），以及数千个脑部 MRI 和认知测试结果。 在这项研究结束时，我们希望能够更好地了解睡眠振荡在睡眠中所起的作用。 大脑健康，以及临床上有用的大脑健康生物标志物。这些成果将有助于发展 促进大脑健康的干预措施。