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

项目摘要/摘要 最近的研究确定了睡眠障碍和阿尔茨海默氏症之间的联系，阿尔茨海默氏症开始了许多 在记忆力受损或痴呆症之前的几年。参与调节日常生活模式的许多大脑区域 在阿尔茨海默病的发展过程中，睡眠-觉醒行为也是最早受到影响的。因此， 了解阿尔茨海默病早期睡眠-觉醒模式的变化可能会导致 更好的疾病检测和治疗干预策略。动作学手表，它使用类似的技术 到我们手机和智能手表中的加速计，可以用来收集外面的睡眠-觉醒活动数据 大规模的实验室。通过分析从数以千计的认知中收集的睡眠-觉醒活动 这个项目将确定日常活动模式的差异是否能够预测 随后的记忆力减退和阿尔茨海默病的诊断。此外，为了了解多早 阿尔茨海默病相关脑部变化影响睡眠，我们将收集脑部成像和体液标志物 阿尔茨海默氏症以及来自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- 觉醒功能障碍信号与特定的大脑病理变化有关。这项研究计划充分利用了 数据与丰富的神经成像并行在多模式方法中，这将促进我们对 睡眠与阿尔茨海默病的关系及其重要临床意义。

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.