Connected Language and Speech Along the Spectrum of Alzheimer’s Disease and Related Dementias: Digital Assessment and Monitoring.

阿尔茨海默病和相关痴呆症范围内的互联语言和言语：数字评估和监测。

• 批准号: 10662754
• 负责人: Kimberly D Mueller
• 金额: $ 78.71万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-03-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662754
• 关键词: Acoustics; Adult; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amyloid; Amyloid beta-Protein; Anterior; Area; Biological Markers; Cerebrospinal Fluid; Clinical; Cognition; Cognitive; Collection; Communication; Consumption; Cultural Sensitivity; Data; Dementia; Digital biomarker; Disease; Early Diagnosis; Early Intervention; Elderly; Exhibits; Goals; Home; Image; Impaired cognition; Impairment; Improve Access; Individual; Inferior; Intervention; Language; Lead; Linguistics; Liquid substance; Longitudinal cohort study; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Memory Loss; Methods; Modeling; Monitor; Natural Language Processing; Nerve Degeneration; Neurons; Neuropsychological Tests; Outcome; Parietal Lobe; Participant; Pathology; Pattern; Persons; Phase; Plasma; Populations at Risk; Positron-Emission Tomography; Procedures; Quality of life; Registries; Research; Risk; Sampling; Self Administration; Semantic memory; Sensitivity and Specificity; Speech; Standardization; System; Temporal Lobe; Testing; Time; Underserved Population; Validation; Wisconsin; Work; clinical diagnosis; clinical trial recruitment; cognitive change; cognitive function; cohort; cost; digital; digital assessment; digital monitoring; digital technology; frontal lobe; handheld mobile device; improved; innovation; language outcome; large scale data; longitudinal, prospective study; mild cognitive impairment; mobile application; neuropathology; novel; participant enrollment; performance based measurement; pre-clinical; prodromal Alzheimer' s disease; prospective; recruit; signal processing; stability testing; supervised learning; tau Proteins; theories; tool; underserved community; usability

PROJECT SUMMARY/ABSTRACT Advances in early detection of Alzheimer’s disease (AD) pathology via imaging and fluid-based biomarkers in individuals with little to no evidence of cognitive decline allow for the targeted enrollment of participants who are most likely to show benefit from early interventions. However, many of these biomarkers are costly and invasive. As a result, more accessible, performance-based measures of cognitive function are needed to improve early detection, aid in recruitment for clinical trials, serve as clinical endpoints for interventions, and improve access to individuals from underserved communities. In this R01 proposal, we respond to this need: by leveraging existing longitudinal data of over 3,000 speech samples from two large cohorts, we propose validation of a digital speech marker across all ADRD stages. Connected speech and language (CSL) analysis of digitally recorded speech—the detailed measurement of everyday spoken language—is a noninvasive digital marker that measures communication, an activity essential for quality of life. Advances in natural language processing and Machine Learning have made automated linguistic analysis a rapid and effective means for analyzing CSL, while the ubiquity of mobile devices makes remote and frequent digital speech collection widely accessible to more people at risk for AD. Our central objective is to further develop, increase accessibility, validate, and improve the sensitivity of digital speech markers to AD through new analytic approaches and remote collection methods. By pursuing the following specific aims on a large-scale dataset, we investigate early changes in CSL to predict how and when CSL relates to the risk of developing ADRD: Aim 1: Validate connected speech and language measures from older adults across multiple stages of ADRD, including prodromal ADRD, mild cognitive impairment, and dementia. Aim 2: Test the hypothesis that connected speech and language measures are associated with AD biomarkers, including Aβ and tau from PET imaging, levels of tau, Aβ, and neurodegeneration (NFL) in CSF and blood plasma, and global neurodegeneration from MRI. Aim 3: Evaluate the usability and accessibility of frequent, remote at-home speech collection and validate linguistic metrics collected remotely against those obtained in person. The results of these three aims will lead to a larger goal of understanding the CSL features that are sensitive to cognitive decline and AD neuropathology in at- risk adults, and the linguistic and acoustic markers necessary to develop a widely accessible tool measuring early cognitive change along the ADRD continuum.

项目总结/摘要 阿尔茨海默病（AD）病理学早期检测的研究进展 在几乎没有或没有认知能力下降证据的个体中的生物标志物允许有针对性地招募 最有可能从早期干预中获益的参与者。然而，其中许多 生物标记物是昂贵的和侵入性的。因此，更容易获得的，基于绩效的 需要认知功能来改善早期发现，帮助招募临床试验， 干预措施的临床终点，并改善对服务不足社区个人的访问。 在R 01提案中，我们对这一需求做出了回应：利用现有的3，000多个纵向数据， 语音样本从两个大的队列，我们提出了一个数字语音标记验证所有 ADRD分期。数字录音语音的连接语音和语言分析 日常口语的详细测量-是一种非侵入性的数字标记， 沟通是生活质量的重要组成部分。自然语言处理和 机器学习使自动语言分析成为一种快速有效的分析手段， 而移动的设备的普遍存在使得远程和频繁的数字语音采集得到广泛应用 让更多有AD风险的人能够接触到。我们的中心目标是进一步发展， 可访问性，验证，并通过新的分析提高数字语音标记对AD的敏感性 方法和远程收集方法。通过大规模追求以下具体目标 数据集，我们调查CSL的早期变化，以预测CSL如何以及何时与以下风险相关： 制定ADRD：目标1：从老年人中收集有关语言和语言的措施 ADRD的多个阶段，包括前驱ADRD、轻度认知障碍和痴呆。目标二： 检验连接言语和语言测量与AD相关的假设 生物标志物，包括来自PET成像的Aβ和tau，tau，Aβ和神经变性（NFL）的水平， CSF和血浆，以及MRI显示的整体神经变性。目标3：评估可用性和 可访问频繁的远程家庭语音收集，并验证收集的语言指标 远程对抗那些亲自获得的。这三个目标的结果将导致更大的目标， 了解对at中认知下降和AD神经病理学敏感的CSL特征， 风险成年人，以及开发广泛使用的工具所必需的语言和声学标记 沿着ADRD连续体测量早期认知变化沿着。