Characterizing sleep-wake activity patterns to detect early Alzheimer's disease in normal older individuals

项目概要/摘要 最近的研究已经建立了睡眠中断和阿尔茨海默病之间的联系， 在记忆力受损或痴呆之前的几年。许多参与调节日常生活模式的大脑区域 睡眠-觉醒行为也是阿尔茨海默病进展中最早受到影响的。因此，我们认为， 了解阿尔茨海默病早期阶段睡眠-觉醒模式的变化可能会导致 更好的疾病检测和治疗干预战略。Activography手表，使用类似于 到我们手机和智能手表中的加速度计，可以用来收集外界的睡眠-觉醒活动数据。 大规模的实验室。通过分析从数千名认知者中收集的睡眠-觉醒活动， 健康的老年人，该项目将确定日常活动模式的差异是否能够预测 随后记忆力下降和阿尔茨海默病诊断。此外，为了了解早期 老年痴呆症相关的大脑变化影响睡眠，我们将收集大脑成像和液体标记物， 阿尔茨海默病沿着当地老年人队列的睡眠-觉醒节律数据。高分辨率 结构脑成像，结合睡眠-觉醒活动表型，将允许识别睡眠- 与特定病理性大脑变化相关的清醒功能障碍特征。这项研究计划利用了巨大的 数据与丰富的神经影像学并行的多模式方法，这将促进我们对神经系统的理解。 睡眠与阿尔茨海默病之间的关系具有重要的临床意义。

PROJECT SUMMARY/ABSTRACT Recent work has established a connection between disrupted sleep and Alzheimer’s disease that begins many years before memory impairment or dementia. Many of the brain regions involved in regulating daily patterns of sleep-wake behavior are also the earliest to be affected in the progression of Alzheimer’s disease. Therefore, understanding how sleep-wake patterns change during the earliest stages of Alzheimer’s disease may lead to better disease detection and treatment intervention strategies. Actigraphy watches, which use technology similar to the accelerometers in our phones and smartwatches, can be used to collect sleep-wake activity data outside the laboratory on a massive scale. By analyzing sleep-wake activity collected from thousands of cognitively healthy older adults, this project will determine whether differences in daily activity patterns are able to forecast subsequent memory decline and Alzheimer’s disease diagnosis. Additionally, in order to understand how early Alzheimer’s disease-related changes in the brain affect sleep, we will collect brain imaging and fluid markers of Alzheimer’s disease along with sleep-wake rhythm data from a local cohort of older adults. High-resolution structural brain imaging, combined with sleep-wake activity phenotypes, will allow for the identification of sleep- wake dysfunction signatures linked to specific pathological brain changes. This research proposal leverages big data in parallel with rich neuroimaging in a multimodal approach which will advance our understanding of the relationship between sleep and Alzheimer’s disease with important clinical implications.