Development of Digital Voice Biomarkers and Associations with Cognition, CSF Biomarkers and Neural Representation in Early Alzheimer’s Disease

Ihab Hajjar, Jinho D. Choi, Elliot Moore II, Maureen Okafor, Anees Abrol, Vince D. Calhoun, Felicia C. Goldstein


Introduction: Advances in natural language processing, speech recognition and machine learning allow the exploration of linguistic and acoustic changes previously difficult to measure. We developed processes for deriving lexical-semantic and acoustic measures as Alzheimer’s disease (AD) digital voice biomarkers, and evaluated the clinical sensitivity of these derived measures against neurocognitive, neuroimaging, and CSF AD biomarker data.

Methods: We collected connected speech, neuropsychological test findings, neuroimaging (brain volume, connectivity, and CSF AD biomarker data (amyloid-β-1-42, total tau, phosphorylated tau) of 92 cognitively unimpaired  (40 Aβ+) and 114 cognitively impaired (63 Aβ+) participants. Acoustic and lexical-semantic features were derived from audio recordings using machine learning approaches.

Results: Lexical-semantic (AUC=0.80) and acoustic scores (AUC=0.77) demonstrated higher diagnostic performance for detecting MCI compared to Boston Naming Test (AUC=0.66). Only lexical-semantic scores detected amyloid-β status (p=0.0003). Acoustic scores associated with hippocampal volume (p=0.017) while lexical-semantic scores associated with CSF amyloid-β (p=0.007). Both measures were significantly associated with 2-year disease progression and mapped to functional connectivity in AD-susceptible brain regions.

Discussion: These preliminary findings suggest that biomarkers derived from standardized audio recordings may  identify persons with cognitive impairment due to preclinical or prodromal AD and may predict disease progression.

Venue / Year

Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) / 2023


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