. 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.
Poor sleep has far-reaching short- and long-term impacts on cognition and health, even in the absence of a sleep disorder, and certain sleep disruptions may also be predictive of Alzheimer?s disease (AD). Certain neural signatures measured during sleep are deemed to be sensitive and specific to AD pathology; they may serve as early, non-invasive, diagnostic biomarkers of the disease and aid in early detection and treatment. By developing a low-cost, virtually imperceptible, soft, wearable, wireless mobile device that can reliably measure an individual?s sleep-related brain activity from the comfort of home, we will overcome existing limitations of lab-based and mobile sleep electroencephalography (EEG) systems in human sleep research for both cognitive and clinical applications.
