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

项目摘要/摘要 影像和体液学早期检测阿尔茨海默病(AD)病理研究进展 在没有或很少有认知能力下降证据的个人中，生物标志物允许有针对性的登记 最有可能从早期干预中受益的参与者。然而，其中许多 生物标记物既昂贵又具有侵入性。因此，更容易获得的、基于绩效的衡量标准 需要认知功能来改善早期发现，帮助招募临床试验，作为 作为干预措施的临床终点，并改善接触服务不足社区的个人的机会。 在此R01提案中，我们通过利用超过3,000个现有的纵向数据来满足这一需求 来自两个大队列的语音样本，我们建议验证数字语音标记在所有 ADRD阶段。数字录音语音的关联语音和语言(CSL)分析 日常口语的详细测量-是一种非侵入性的数字标记，可以测量 沟通，这是一项对生活质量至关重要的活动。自然语言处理与研究进展 机器学习使自动语言分析成为一种快速而有效的分析手段 CSL，而移动设备的无处不在使得远程和频繁的数字语音采集广泛存在 更多有AD风险的人可以访问。我们的中心目标是进一步发展、增加 通过新的分析，可访问、验证和提高数字语音标记对AD的敏感度 途径和远程采集方法。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 发展ADRD：目标1：验证来自各地老年人的相互联系的言语和语言测量 ADRD的多个阶段，包括前驱ADRD、轻度认知障碍和痴呆。目标2： 测试连接的语音和语言测量与AD相关的假设 生物标志物，包括来自正电子发射计算机断层扫描的Aβ和tau，tau，Aβ和神经退行性变的水平 脑脊液和血浆，以及核磁共振显示的全局性神经变性。目标3：评估可用性和 可访问频繁的远程在家语音收集，并验证收集的语言指标 与那些亲自获得的数据进行远程对比。这三个目标的结果将导致更大的目标 了解认知功能减退和阿尔茨海默病患者的CSL特征 风险成人，以及开发广泛可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。

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.