Sleep metrics from machine learning for Alzheimer's disease diagnostics

用于阿尔茨海默病诊断的机器学习睡眠指标

• 批准号: 10715006
• 负责人: Joyita Dutta
• 金额: $ 20.14万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10715006
• 关键词: Sleep; metrics; machine; learning; Alzheimer

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.

项目摘要 本提案是对NIH征集PA-17-089的回应，涉及预先存在的二次分析项目。 老年数据集。虽然目前还没有治愈阿尔茨海默病的方法，但现有文献表明， 临床前阶段的诊断，即，在临床症状出现之前，将是治疗的关键。有 迫切需要能够早期识别个体的认知能力下降的非侵入性预测指标 有患老年痴呆症的风险。越来越多的科学证据表明，睡眠障碍（包括 非快速眼动睡眠的微结构破坏和睡眠质量下降）可能是最早的 阿尔茨海默病的症状移动睡眠和活动监测可以满足这一需求 对于（无症状或轻度症状）受试者中认知下降的非侵入性指标 阿尔茨海默病的临床前期在这里，我们将建立在初步的结果，揭示了一组睡眠 来自多导睡眠图（PSG）的预测认知表现的特征。我们提议 对多种族动脉粥样硬化研究（梅萨）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来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是睡眠测量的黄金标准，但它并不好- 适合日常使用。相比之下，基于腕部的测量（例如，体动记录、心率、ECG， 和脉搏血氧测定法）允许“在移动中”的睡眠监测。的组合 这些使用最新人工智能工具的动态措施是治疗早期阿尔茨海默病的可行途径， 诊断我们将使用注意力引导的长短期记忆自动编码器来识别显性和隐性记忆。 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梅萨数据 resource.我们的深度学习框架还将考虑社会人口变量， 健康状况和药物为了确保科学的严谨性，MESA培训的深度二次验证 学习模型将在来自哈佛衰老大脑研究的PSG和活动记录数据上进行，这是一项 一项纵向研究，旨在进一步了解正常衰老与临床前衰老的区别 老年痴呆症为了解决任何关于深度学习模型的“黑匣子”性质的问题，我们将 将所学习的特征集与先前使用经典睡眠微体系结构计算的睡眠微体系结构特征进行比较， 统计技术。以前的数据表明，受试者的载脂蛋白ε4（ApoE 4）等位基因携带状态 影响他们的睡眠模式对认知能力的影响程度。我们将通过以下方式进行验证： 将ApoE 4状态作为深度学习模型的额外输入。文献显示，超过60%的 患有轻度认知障碍和阿尔茨海默病的患者至少有一种临床睡眠障碍。的 在这个项目中，使用非侵入性睡眠测量进行预测的范例将有一个 对早期阿尔茨海默氏症诊断产生重大影响，并促进正在进行的临床试验。

项目成果

Noise2Void: unsupervised denoising of PET images.

• DOI: 10.1088/1361-6560/ac30a0
• 发表时间: 2021-11-01
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Song TA;Yang F;Dutta J
• 通讯作者: Dutta J

AI-Driven sleep staging from actigraphy and heart rate.

AI驱动的睡眠分阶段是由动作学和心律进行的。

• DOI: 10.1371/journal.pone.0285703
• 发表时间: 2023
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Song, Tzu-An;Chowdhury, Samadrita Roy;Malekzadeh, Masoud;Harrison, Stephanie;Hoge, Terri Blackwell;Redline, Susan;Stone, Katie L.;Saxena, Richa;Purcell, Shaun M.;Dutta, Joyita
• 通讯作者: Dutta, Joyita