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

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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.