Natural speech markers of Alzheimer's disease co-pathology in Lewy body dementias. (September 2022)

• Record Type: Journal Article
• Authors: Shellikeri, Sanjana; Cho, Sunghye; Cousins, Katheryn A.Q.; Liberman, Mark; Howard, Erica; Balganorth, Yvonne; Weintraub, Daniel; Spindler, Meredith; Deik, Andres; Lee, Edward B.; Trojanowski, John Q.; Irwin, David; Wolk, David; Grossman, Murray; Nevler, Naomi
• Is Part Of: Parkinsonism & related disorders. Volume 102(2022)
• Page Start: 94
• Page End: 100
• Publication Date: 2022-09
• Subjects: Speech analysis -- Automatic speech processing -- Natural language processing -- Digital biomarker -- Alzheimer's disease -- Lewy body dementia -- Parkinson disease dementia; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2022.07.023
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Introduction: An estimated 50% of patients with Lewy body dementias (LBD), including Parkinson's disease dementia (PDD) and Dementia with Lewy bodies (DLB), have co-occurring Alzheimer's disease (AD) that is associated with worse prognosis. This study tests an automated analysis of natural speech as an inexpensive, non-invasive screening tool for AD co-pathology in biologically-confirmed cohorts of LBD patients with AD co-pathology (SYN + AD) and without (SYN-AD). Methods: We analyzed lexical-semantic and acoustic features of picture descriptions using automated methods in 22 SYN + AD and 38 SYN-AD patients stratified using AD CSF biomarkers or autopsy diagnosis. Speech markers of AD co-pathology were identified using best subset regression, and their diagnostic discrimination was tested using receiver operating characteristic. ANCOVAs compared measures between groups covarying for demographic differences and cognitive disease severity. We tested relations with CSF tau levels, and compared speech measures between PDD and DLB clinical disorders in the same cohort. Results: Age of acquisition of nouns ( p = 0.034, | d | = 0.77) and lexical density ( p = 0.0064, | d | = 0.72) were reduced in SYN + AD, and together showed excellent discrimination for SYN + AD vs. SYN-AD (95% sensitivity, 66% specificity; AUC = 0.82). Lower lexical density was related to higher CSF t-Tau levels (R = −0.41, p = 0.0021). Clinically-diagnosed PDD vs. DLB did not differ on any speech features. Conclusion: AD co-pathology may result in a deviant natural speech profile in LBD characterized by specific lexical-semantic impairments, not detectable by clinical disorder diagnosis. Our study demonstrates the potential of automated digital speech analytics as a screening tool for underlying AD co-pathology in LBD. Highlights: AD co-pathology in Lewy body dementias impacts natural language production. Picture descriptions are less dense and comprised of earlier-learned nouns. Language measures classify patients with AD co-pathology with high sensitivity. Language measures are worse in patients with higher CSF tau levels. These lexical-semantic impairments are not detectable by syndrome diagnosis.