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.

项目概要/摘要 通过成像和基于液体的方法早期检测阿尔茨海默病 (AD) 病理学的进展 几乎没有或没有认知能力下降证据的个体的生物标志物允许有针对性的招募 最有可能从早期干预中受益的参与者。 生物标记物成本高昂且具有侵入性，因此需要更容易获得、基于性能的测量方法。 需要认知功能来改善早期检测、帮助临床试验招募、充当 干预措施的临床终点，并改善服务不足社区的个人的获取机会。 在这个 R01 提案中，我们响应了这一需求：通过利用现有的 3,000 多个纵向数据 来自两个大群体的语音样本，我们建议在所有样本中验证数字语音标记 ADRD 阶段。对数字录制语音进行互联语音和语言 (CSL) 分析。 日常口语的详细测量——是一种非侵入性数字标记，可测量 沟通，这是自然语言处理和生活质量的重要活动。 机器学习使自动语言分析成为一种快速有效的分析手段 CSL，而移动设备的普及使得远程、频繁的数字语音采集得到广泛应用 我们的中心目标是进一步开发、增加更多有 AD 风险的人。 通过新的分析方法提高数字语音标记对 AD 的可访问性、验证和敏感性 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 如何以及何时与风险相关 制定 ADRD：目标 1：验证不同地区老年人的互联言语和语言测量结果 ADRD 的多个阶段，包括前驱 ADRD、轻度认知障碍和痴呆。 目标 2： 检验关联语音和语言测量与 AD 相关的假设 生物标志物，包括 PET 成像中的 Aβ 和 tau 蛋白、tau 蛋白、Aβ 水平和神经退行性变 (NFL) 脑脊液和血浆，以及 MRI 的整体神经变性 目标 3：评估可用性和稳定性。 可进行频繁、远程的家庭语音收集并验证收集到的语言指标 这三个目标的结果将导致一个更大的目标。 了解对认知衰退和 AD 神经病理学敏感的 CSL 特征 风险成人，以及开发可广泛使用的工具所需的语言和声学标记 沿着 ADRD 连续体测量早期认知变化。