Developing an imperceptible electroencephalography at-home sleep monitoring system for cognitive and clinical research

项目总结。睡眠障碍在美国很普遍，并已被宣布为 疾病控制和预防中心(CDC)的卫生流行病。睡眠不佳影响深远 对认知、健康的影响，并增加了许多疾病的风险，包括阿尔茨海默病(AD)。 睡眠中测量的脑电(EEG)神经特征可能对AD神经病理敏感 这预示着认知正常的人在被诊断为阿尔茨海默病的前几年认知能力下降。目前，AD 病理只能在活体内使用PET成像或通过脊柱抽吸进行测量，这是有创的，而且成本很高。 如果某些睡眠脑电信号被认为是AD病理的敏感和特异的，它们可能在早期起作用， 这种疾病的非侵入性、诊断性生物标志物。传统上，睡眠EEG信号是使用大块的、 在不熟悉的实验室环境中使用僵硬、不舒服的设备，会对睡眠信号产生负面影响。由于这些原因 由于睡眠的局限性，睡眠脑电数据的采集通常仅限于一晚。因为夜间睡眠的变异性 预测认知表现和健康结果不佳的人，对多个晚上的睡眠进行抽样是至关重要的。 现有的移动系统仍然笨重，缺乏可靠评估睡眠阶段所需的电极。多重PI 在衰老、机械/生物医学的认知神经科学方面拥有互补专业知识的跨学科团队 工程学和睡眠神经科学将开发和验证一种低成本、几乎不可感知、柔软、可穿戴的 专为长期使用而设计的无线移动设备，用于测量个人在睡觉时的睡眠脑电 他们自己的家度过了多个夜晚。这款设备是第一个完全便携、紧凑、灵活的皮肤- 像混合电子公司(SKINTRONICS)的脑电系统，包括超薄的喷雾打印的额头、眼睛和 下巴移动电极、锂聚合物电池和用于数据存储的微型安全数字(SD)卡。这个 封装设备的弹性体可防止干扰并确保高信噪比，而无需 电极胶。脑电记录的所有必要组件大致包含在不粘的粘合剂中 创可贴®的大小，参与者可以轻松地使用和拆卸。将对该系统进行评估 在实验室和家里对年轻人和老年人进行研究，以确保在患有潜在不同水平的 可穿戴技术的脑电信噪比、睡眠质量和舒适性。个体水平之间的关系 睡眠脑电指标和对睡眠质量高度敏感的情景记忆任务的表现 为了确认我们的系统揭示了睡眠脑电和记忆的联系，我们将对AD进行评估 与文献中的一致。将获得10个晚上的数据，以测试SKINTRONICS的长期 为了区分睡眠脑电中习惯性和夜间变异性的预测价值 影响内存性能的信号。此应用程序生成的结果将另外适用于 对有阿尔茨海默病风险的参与者进行纵向研究，以确定特定的睡眠脑电模式是否后天获得 通过SKINTRONICS，可以帮助识别最有可能表现出认知能力下降的个体。

PROJECT SUMMARY. Disordered sleep is pervasive in the United States and has been declared a public health epidemic by the Centers for Disease Control and Prevention (CDC). Poor sleep has far-reaching impacts on cognition, health, and contributes to an increased risk of many diseases, including Alzheimer’s disease (AD). Electroencephalography (EEG) neural signatures measured during sleep may be sensitive to AD neuropathology that is predictive of cognitive decline in cognitively normal people years before AD diagnosis. Currently, AD pathology can only be measured in vivo using PET imaging or via spinal taps, which are invasive and expensive. If certain sleep EEG signatures are deemed to be sensitive and specific to AD pathology, they may serve as early, non-invasive, diagnostic biomarkers of the disease. Sleep EEG signals are traditionally measured using bulky, rigid, uncomfortable equipment in an unfamiliar lab setting that can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. As night-to-night sleep variability is predictive of poor cognitive performance and health outcomes, it is essential to sample multiple nights of sleep. Existing mobile systems are still bulky and lack electrodes needed to reliably assess sleep stages. The multi-PI interdisciplinary team with complementary expertise in cognitive neuroscience of aging, mechanical/biomedical engineering, and sleep neuroscience will develop and validate a low-cost, virtually imperceptible, soft, wearable, wireless mobile device designed for long-term use and use it to measure individuals’ sleep EEG as they sleep in their own homes over multiple nights. This device is the first example of a fully portable, compact, flexible skin- like hybrid electronics (SKINTRONICS) EEG system that includes ultrathin aerosol-jet printed forehead, eye and chin movement electrodes, a Li-polymer battery, and a micro secure digital (SD) card for data storage. The elastomer that encapsulates the device protects from interference and ensures high SNR without the need for electrode gel. All necessary components for EEG recording are contained within a non-sticky adhesive roughly the size of a Band-Aid® that participants can easily apply and remove themselves. This system will be assessed in the lab and at home in young and older adults to ensure feasibility in adults with potentially different levels of EEG SNR, sleep quality, and comfort with wearable technology. The relationship between individual levels of sleep EEG metrics and performance on an episodic memory task designed to be highly sensitive to sleep quality and AD will be assessed for the purposes of confirming that our system reveals sleep EEG-memory associations consistent with those in the literature. Ten nights of data will be acquired to test SKINTRONICS long-term capabilities and in order to separate the predictive value of habitual from night-to-night variability in sleep EEG signals on memory performance. Results produced by this application will additionally be applicable for longitudinal studies in participants at risk for AD to determine whether particular sleep EEG patterns, acquired with SKINTRONICS, can help identify individuals most likely to exhibit cognitive decline.