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

表征睡眠-觉醒活动模式以检测正常老年人的早期阿尔茨海默病

基本信息

批准号：
10668213
负责人：
Joseph Robert Winer
金额：
$ 7.2万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-24 至 2024-08-23
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10668213
关键词：
Accelerometer Affect Aging Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease pathology Alzheimer’s disease biomarker Amyloid beta-Protein Beds Behavior Big Data Brain Brain imaging Brain region Clinical Cognitive Data Data Analyses Data Collection Data Set Dementia Detection Diagnosis Disease Disease Progression Early Diagnosis Elderly Functional disorder Goals Hippocampus Hour Impairment Individual Intervention Knowledge Laboratories Life Link Liquid substance Longevity Magnetic Resonance Imaging Measures Medial Memory Memory Loss Memory impairment Neuroanatomy Neurodegenerative Disorders Neurosciences Non-Invasive Detection Pathologic Pathology Pattern Performance Phenotype Physical activity Positron-Emission Tomography Principal Component Analysis Process Public Health Research Proposals Resolution Rest Severities Sleep Sleep Fragmentations Sleep Wake Cycle Sleep disturbances Structure Symptoms Techniques Technology Temporal Lobe Testing Training Work actigraphy aging population behavior change career cohort dentate gyrus high dimensionality improved locus ceruleus structure multimodality neural correlate neuroimaging neuropathology noradrenergic pre-clinical sleep pattern sleep regulation smart watch tau Proteins ultra high resolution

项目摘要

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.

项目总结/文摘