Sleep and Electroencephalography Biomarkers of Alzheimer's Disease

项目摘要 阿尔茨海默病(AD)是一种日益流行的疾病，潜在的治疗方法不太可能有效 除非在AD的最早阶段部署，在认知症状之前。目前没有 廉价、非侵入性的生物标志物用于有效的AD筛查，是早期识别和治疗所必需的 在广泛的范围内。临床前阿尔茨海默病的睡眠异常，甚至在认知症状之前就是异常的，睡眠中断 可能反过来加速AD的病理机制。脑电(EEG)直接测量大脑 功能，而睡眠脑电的刻板印象提供了一个特别丰富的机会来识别生物标志物 阿尔茨海默病所致的脑功能障碍。这项拟议研究的中心假设是睡眠-唤醒大脑 机制在阿尔茨海默病的早期就出现异常，可以通过细微但不同的睡眠和脑电检测到 改变。目的是开发阿尔茨海默病的睡眠和脑电生物标志物，使非侵入性和 通过以下具体目标，大规模进行廉价筛查。 目的1)确定AD病理的24小时睡眠-觉醒模式。 动态睡眠-EEG数据将在家中24小时内从一个大型的、多样化的、 以社区为基础的队列，假设24小时内睡眠-醒来转换的增加是 临床前至轻度AD的特点。目的2)评估慢波完整性指标作为AD的生物标志物 病理学。慢波睡眠的脑电异常尤其与淀粉样蛋白-β水平升高有关 还有斑块。新的分析技术将提取双半球慢波相干性、慢波速度、 以及从睡眠和清醒时收集的脑电数据中获得的慢波日内比率。假设是淀粉样蛋白 AD早期出现的斑块将通过所有三种措施降低慢波完整性。目的3)测定脑电 使用机器学习的AD签名。机器学习技术将从一个完整的 进行过夜多导睡眠图检查，以确定AD病理的“特征”。我们的目标是确定一个 可以通过在家中收集的空间有限的脑电数据检测到的“特征”。 这些目标的预期结果是确定可以检测到的阿尔茨海默病的睡眠脑电生物标记物 在国内非侵入性且价格低廉。我们工作的影响将是筛查大量人口的能力 易于进行AD病理检查，以便识别和治疗受影响的个体。此外，睡眠脑电 在AD的临床试验中，生物标记物可以用来跟踪疾病进展和治疗反应。最后， 由于睡眠障碍对AD的病理有直接影响，通过很早就识别睡眠-脑电的变化 病理过程中，我们也许能够进行干预，改善睡眠，并潜在地改变大脑的运动轨迹 广告。 好了！

Project Summary Alzheimer's Disease (AD) is a growing epidemic, and potential treatments are unlikely to be effective unless deployed during the earliest stages of AD, prior to cognitive symptoms. Currently there are no inexpensive, non-invasive biomarkers for effective AD screening necessary for early recognition and treatment on a broad scale. Sleep is abnormal in preclinical AD, even prior to cognitive symptoms, and disrupted sleep may in turn accelerate AD pathological mechanisms. Electroencephalography (EEG) directly measures brain function, and the stereotyped nature of sleep EEG offers a particularly rich opportunity to identify biomarkers of brain dysfunction due to AD. The central hypothesis of the proposed study is that sleep-wake brain mechanisms are abnormal very early in AD, and can be detected via subtle but distinct sleep and EEG changes. The objective is to develop sleep and EEG biomarkers of AD, to enable non-invasive and inexpensive screening on a large scale, through the following specific aims. Aim 1) Identify sleep-wake patterns across the 24-hour day characteristic of AD pathology. Ambulatory sleep-EEG data will be recorded over the 24-hour period in the home setting from a large, diverse, community-based cohort, with the hypothesis that increased sleep-wake transitions over the 24-hour day are characteristic of preclinical-to-mild AD. Aim 2) Assess slow wave integrity measures as biomarkers of AD pathology. EEG abnormalities of slow wave sleep are particularly associated with elevated amyloid-β levels and plaques. Novel analytic techniques will extract bihemispheric slow wave coherence, slow wave velocity, and slow wave intradaily ratio from EEG data collected during sleep and wake. The hypothesis is that amyloid plaques present in early AD will reduce slow wave integrity by all three measures. Aim 3) Determine the EEG signature of AD using machine learning. Machine learning techniques will be applied to EEG from a full attended overnight polysomnogram, to identify a “signature” of AD pathology. The goal is to identify a “signature” that can be detected with spatially limited EEG data that could be collected at home. The expected outcome of these aims is to identify sleep-EEG biomarkers of AD that can be detected noninvasively and inexpensively at home. The impact of our work will be the ability to screen large populations easily for AD pathology, so that affected individuals can be identified and treated. Moreover, sleep-EEG biomarkers could be used to track disease progression and treatment response in clinical trials for AD. Lastly, since sleep disturbance has a direct effect on AD pathology, by identifying sleep-EEG changes very early in the pathological process, we may be able to intervene, improve sleep, and potentially change the trajectory of AD. !