Language impairment in Alzheimer’s disease—Robust and explainable evidence for AD-related deterioration of spontaneous speech through multilingual machine learning
Open accessAbstract
Alzheimer's disease (AD) is a pervasive neurodegenerative disease that affects millions worldwide and is most prominently associated with broad cognitive decline, including language impairment. Picture description tasks are routinely used to monitor language impairment in AD. Due to the high amount of manual resources needed for an in-depth analysis of thereby-produced spontaneous speech, advanced natural language processing (NLP) combined with machine learning (ML) represents a promising opportunity. In this applied research field though, NLP and ML methodology do not necessarily ensure robust clinically actionable insights into cognitive language impairment in AD and additional precautions must be taken to ensure clinical-validity and generalizability of results. In this study, we add generalizability through multilingual feature statistics to computational approaches for the detection of language impairment in AD. We include 154 participants (78 healthy subjects, 76 patients with AD) from two different languages (106 English speaking and 47 French speaking). Each participant completed a picture description task, in addition to a battery of neuropsychological tests. Each response was recorded and manually transcribed. From this, task-specific, semantic, syntactic and paralinguistic features are extracted using NLP resources. Using inferential statistics, we determined language features, excluding task specific features, that are significant in both languages and therefore represent "generalizable" signs for cognitive language impairment in AD. In a second step, we evaluated all features as well as the generalizable ones for English, French and both languages in a binary discrimination ML scenario (AD vs. healthy) using a variety of classifiers. The generalizable language feature set outperforms the all language feature set in English, French and the multilingual scenarios. Semantic features are the most generalizable while paralinguistic features show no overlap between languages. The multilingual model shows an equal distribution of error in both English and French. By leveraging multilingual statistics combined with a theory-driven approach, we identify AD-related language impairment that generalizes beyond a single corpus or language to model language impairment as a clinically-relevant cognitive symptom. We find a primary impairment in semantics in addition to mild syntactic impairment, possibly confounded by additional impaired cognitive functions.
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In Alzheimer's disease, the contribution of semantic, syntactic and paralinguistic features for disease identification differs between speakers of English and French.
“As shown in a machine learning study, the contribution of semantic, syntactic and paralinguistic features for disease identifica tion differs between speakers of English and French. 110”
From Speech and language markers of neurodegeneration: a call for global equity (2023) · cites this paper as [110]
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Speech and language markers of neurodegeneration: a call for global equity
García, A. M., de Leon, J., Tee, B. L., Blasi, D. E., & Gorno-Tempini, M. L. (2023) · Brain · doi:10.1093/brain/awad253 · Cited by 102 · see citing works
Speech based detection of Alzheimer's disease: a survey of AI techniques, datasets and challenges
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- Authors
- Lindsay, H., Tröger, J., & König, A.
- Affiliations
- German Research Centre for Artificial Intelligence (DE)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Lindsay, Hali
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2021-05-19
- Volume
- 13
- Issue
- 11
- Pages
- 642033-642033
- ISSN
- 1663-4365 (ISSN-L)
- PMC
- PMC8170097
- OpenAlex
- W3161827824
- Cited by
- 64
- References
- 59
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 14 · 2023: 21 · 2022: 2 · 2021: 2
- FWCI
- 7.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Aging Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Bundesministerium für Bildung und Forschung (01IS20075)
- Horizon 2020 Framework Programme (777222)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, language impairment, spontaneous speech, picture description task, semantic features, syntactic features, paralinguistic features, natural language processing, machine learning classification, clinical validity
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