Characterizing progressive speech changes in prodromal‐to‐mild Alzheimer's disease using natural language processing. (20th December 2022)

• Record Type: Journal Article
• Authors: Robin, Jessica; Xu, Mengdan; Balagopalan, Aparna; Novikova, Jekaterina; Kahn, Laura; Oday, Abdi; Hejrati, Mohsen; Hashemifar, Somaye; Negahdar, Mohammadreza; Simpson, Bill; Teng, Edmond
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.063244
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Novel automated tools for analyzing speech and language may provide new insights into Alzheimer's disease (AD). Although speech and language changes occur in AD and other neurodegenerative diseases, current clinical assessments to monitor these symptoms can be burdensome and may have limited sensitivity. Through analyses of open‐ended naturalistic speech collected from a standardized clinical interview, we developed a novel measure to characterize progressive speech changes in AD. Methods: We analyzed Clinical Dementia Rating (CDR) recordings from a subset of 101 participants (58F, 43M, mean age=69 years, SD=7) from the Tauriel trial of semorinemab in prodromal‐to‐mild AD. CDR recordings were collected at the baseline, 6‐month, 12‐month and 18‐month timepoints. Recordings were processed using the Winterlight speech analysis platform which generates >500 acoustic and linguistic features. After controlling for age, sex and level of education, we identified multiple features that had significant linear effects of time (indicating progressive longitudinal change). These speech features were combined into an unweighted composite speech score, which was compared with other clinical endpoints. Results: The novel speech composite score included six linguistic features (related to word duration, word frequency, syntactic depth, use of nouns, pronouns and particles) and three acoustic features (related to the power spectrum of the vocal recordings). When compared with clinical endpoints, the speech composite had a similar effect size for detecting longitudinal change (β=0.29) compared to the CDR‐Sum of Boxes (CDR‐SB; β=0.30) and Alzheimer's Disease Assessment Scale‐Cognitive Subscale (ADAS‐Cog; β=0.22). Notably, it had a significantly greater longitudinal effect size compared to the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; β=‐0.15, p<0.01) and the language subscales of the ADAS‐Cog measuring word finding difficulty (β=0.12, p<0.01) and spoken language ability (β=0.09, p<0.01). Conclusions: Progressive speech changes are detectable in early AD and measurable via automated language processing tools. Speech composite scores have the potential to be more sensitive measures of disease progression and/or treatment response for speech‐related symptoms in AD that do not contribute to additional patient burden. Further validation is needed to replicate these findings and confirm the clinical and neuropathological relevance of this novel measure.