Sleep metrics from machine learning for Alzheimer's disease diagnostics

项目总结 本提案是对美国国家卫生研究院PA-17-089号文件的响应，该文件涉及对已有的 老年数据集。虽然目前还没有治愈阿尔茨海默氏症的方法，但现有的文献表明，早期 在临床前阶段，即在临床症状出现之前进行诊断将是治疗的关键。的确有 迫切需要非侵入性的认知衰退预测指标，使之能够及早识别个体 有患阿尔茨海默病的风险。越来越多的科学证据表明，睡眠障碍(包括 微结构对非快眼动睡眠的干扰和睡眠质量的下降)可能是最早的 阿尔茨海默病的明显症状。随时随地的睡眠和活动监控可以满足这种需求 对于无症状或轻微症状的受试者的认知衰退的非侵入性指标 阿尔茨海默病的临床前阶段。在这里，我们将建立在揭示一组睡眠的初步结果的基础上 多导睡眠图(PSG)中可预测认知表现的特征。我们正在提议 对动脉粥样硬化多种族研究(MESA)的睡眠和认知数据进行二次分析 使用最先进的深度学习工具实现基于睡眠的认知障碍预测 早期发现阿尔茨海默病。虽然PSG是测量睡眠的黄金标准，但它并不好- 适合日常使用。相比之下，基于腕部的测量(例如，动作图、心率、心电图、 和脉搏血氧饱和度)从可穿戴设备获得的睡眠监测功能可以实现“在路上”睡眠监测。这两种技术的结合 这些使用最新人工智能工具的移动措施是治疗早期阿尔茨海默氏症的可行途径 诊断。我们将使用注意力引导的长期短期记忆自动编码器来识别外显和潜在的 原始时间序列数据集的特征，这将使我们能够更有效地挖掘丰富的台地数据 资源。我们的深度学习框架还将考虑到社会人口变量、 健康状况和药物。为确保科学严谨性，对台地训练的深度进行二次验证 学习模型将在来自哈佛老龄化大脑研究的PSG和动作图数据上进行，这是一项 旨在加深我们对正常衰老和临床前衰老的区别的纵向研究 阿尔茨海默氏症。为了解决人们对深度学习模型“黑箱”性质的任何担忧，我们将 将学习的特征集与先前使用经典计算的睡眠微体系结构特征进行比较 统计技术。先前的数据表明，受试者的载脂蛋白ε4(ApoE4)等位基因携带者状态 影响他们的睡眠模式对他们认知能力的影响程度。我们将通过以下方式进行核实 将载脂蛋白E4状态作为深度学习模型的附加输入。文献表明，超过60%的 轻度认知障碍和阿尔茨海默病患者至少有一种临床睡眠障碍。这个 使用非侵入性睡眠测量的On-The-Go预测范例将在该项目中得到验证 对早期阿尔茨海默氏症的诊断产生重大影响，并促进正在进行的临床试验。

PROJECT SUMMARY This proposal is responsive to NIH solicitation PA-17-089 for projects involving secondary analysis of pre-existing geriatric datasets. While presently there is no cure for Alzheimer’s disease, existing literature indicates that early diagnosis in the preclinical stage, i.e., before the onset of clinical symptoms, will be key to treatments. There is a pressing need for noninvasive predictors of cognitive decline that can enable early identification of individuals at Alzheimer’s disease risk. A mounting body of scientific evidence suggests that sleep disturbances (including microarchitectural disruptions to non-rapid-eye-motion sleep and decline in sleep quality) might be the earliest observable symptoms of Alzheimer’s disease. On-the-go sleep and activity monitoring could address the need for noninvasive indicators of cognitive decline in subjects who are in the (asymptomatic or mildly symptomatic) preclinical stage of Alzheimer’s disease. Here, we will build on preliminary results that reveal a set of sleep features derived from polysomnography (PSG) that are predictive of cognitive performance. We are proposing to perform secondary analysis of sleep and cognition data from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort using state-of-the-art deep learning tools to enable sleep-based prediction of cognitive impairment for early detection of Alzheimer’s disease. While PSG is the gold standard for sleep measurement, it is not well- suited for routine, day-to-day use. In comparison, wrist-based measurements (e.g. actigraphy, heart rate, ECG, and pulse oximetry) obtained from wearable devices allow “on-the-go” sleep monitoring. The combination of these on-the-go measures with the latest artificial intelligence tools is a feasible route to early Alzheimer’s diagnostics. We will use attention-guided long short-term memory autoencoders to identify overt and latent characteristics of the raw time-series datasets, which will allow us to more effectively mine the rich MESA data resource. Our deep learning framework will also take into account sociodemographic variables, indicators of health status, and medications. To ensure scientific rigor, secondary validation of the MESA-trained deep learning models will be performed on PSG and actigraphy data from the Harvard Aging Brain Study, which is a longitudinal study designed to further our understanding of what differentiates normal aging from preclinical Alzheimer’s disease. To address any concern about the “black-box” nature of deep learning models, we will compare the learned feature set with sleep microarchitectural features previously computed using classical statistical techniques. Previous data suggests that a subject’s apolipoprotein ε4 (ApoE4) allele carrier status influences the degree to which their sleep patterns impact their cognitive abilities. We will verify this by incorporating ApoE4 status as an additional input to the deep learning model. Literature shows that over 60% of patients with mild cognitive impairment and Alzheimer’s disease have at least one clinical sleep disorder. The on-the-go prediction paradigm using noninvasive sleep measurements to be validated in this project will have a significant impact on early Alzheimer’s diagnostics and facilitate ongoing clinical trials.