Digital speech biomarkers for assessing cognitive decline across neurodegenerative conditions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F25%3A00082493" target="_blank" >RIV/00159816:_____/25:00082493 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14740/25:00142524 RIV/00216305:26220/26:0199708

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S001048252501604X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S001048252501604X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2025.111251" target="_blank" >10.1016/j.compbiomed.2025.111251</a>

Result language

angličtina

Original language name

Digital speech biomarkers for assessing cognitive decline across neurodegenerative conditions

Original language description

This study investigates speech impairments in individuals with mild cognitive impairment due to Alzheimer’s disease (MCI-AD), mild cognitive impairment with Lewy bodies (MCI-LB), and Parkinson’s disease with mild cognitive impairment (PD-MCI), compared to healthy controls (HC), aiming to identify linguistic and acoustic digital biomarkers that differentiate these groups. Monologue recordings were collected from 68 HC, 42 MCI-AD, 50 MCI-LB, and 47 PD-MCI participants (ON state). Participants were instructed to speak spontaneously for one and a half minutes. Speech was automatically transcribed, manually corrected, and analyzed using natural language processing to extract eight linguistic (lexical/syntactic) and four acoustic (prosodic) biomarkers. Group differences were assessed using the Mann–Whitney U test, with Spearman’s correlation used to examine associations with clinical and MRI measures (FDR-corrected). Machine learning models (XGBoost) were applied to evaluate the classificatory and predictive potential of speech features. Distinct speech patterns were observed across groups: MCI-AD participants exhibited reduced use of function words, resulting in increased content density, PD-MCI participants used shorter sentences and fewer coordinating conjunctions with longer pauses, and MCI-LB participants exhibited greater lexical repetition than MCI-AD. Altered speech features correlated with structural brain changes but not with global cognition (MoCA) or depressive symptoms (GDS). Sentence structure and pausing features showed strong interrelationships. Machine learning models showed that adding speech biomarkers improved classification performance compared to using clinical scores alone. In regression analyses, the models predicted MoCA with a normalized error of 10%, performing similarly on automatic and manually corrected transcripts. These findings suggest that speech biomarkers and traditional clinical assessments may offer complementary information about cognitive status and brain health, supporting their use in scalable, non-invasive cognitive monitoring. Copyright © 2025. Published by Elsevier Ltd.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers in Biology and Medicine

ISSN

0010-4825

e-ISSN

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Volume of the periodical

198

Issue of the periodical within the volume

Nov 2025

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

111251

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105020955849