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Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks
Nyongesa CA, Hogarth M, Pa J.. Journal of Alzheimer's disease : JAD, 2025-05-07
Topic: Amyloid, tau and blood-test diagnosis
BackgroundLanguage deficits often occur early in the neurodegenerative process, yet traditional methods frequently fail to detect subtle changes. Natural language processing (NLP) offers a novel approach to identifying linguistic patterns associated with cognitive impairment.ObjectiveWe aimed to analyze linguistic features that differentiate cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) groups.MethodsData was extracted from picture description tasks performed by 336 participants in the DementiaBank datasets. 53 linguistic features aggregated into 4 categories: lexical, structural, syntactic, and discourse domains, were identified using NLP toolkits. With normal diagnostic cutoffs, cognitive function was evaluated with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA).ResultsWith age and education as covariates, ANOVA and post-hoc Tukey's HSD tests revealed that linguistic features such as pronoun usage, syntactic complexity, and lexical sophistication showed significant differences between CU, MCI, and AD groups (p < 0.05). Notably, past tense and personal references were higher in AD than both CU and MCI (p < 0.001), while pronoun usage differed between AD and CU (p < 0.0001). Correlations indicated that higher pronoun rates and lower syntactic complexity were associated with lower MMSE scores and although some features like conjunctions and determiners approached significance, they lacked consistent differentiation.ConclusionsWith the growing adoption of artificial intelligence (AI)-based scribing, these results emphasize the potential of targeted linguistic analysis as a digital biomarker to enable continuous screening for cognitive impairment.
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APA 7th
Nyongesa, C. A., Hogarth, M., & Pa, J. (2025). Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks. Journal of Alzheimer's disease : JAD. https://doi.org/10.1177/13872877251339756
Vancouver
Nyongesa CA, Hogarth M, Pa J. Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks. Journal of Alzheimer's disease : JAD. 2025. doi:10.1177/13872877251339756. PMID: 40336266.
BibTeX
@article{nyongesa2025-40336266,
author = {Nyongesa, C. A. and Hogarth, M. and Pa, J.},
title = {{Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks}},
journal = {Journal of Alzheimer's disease : JAD},
year = {2025},
doi = {10.1177/13872877251339756},
pmid = {40336266},
url = {https://doi.org/10.1177/13872877251339756}
}
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Key findings
- Natural language processing (NLP) offers a novel approach to identifying linguistic patterns associated with cognitive impairment.ObjectiveWe aimed to analyze linguistic features that differentiate cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) groups.MethodsData was extracted from picture description tasks performed by 336 participants in the DementiaBank datasets. 53 linguistic features aggregated into 4 categories: lexical, structural, syntactic, and discourse domains, were identified using NLP toolkits.
- With normal diagnostic cutoffs, cognitive function was evaluated with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA).ResultsWith age and education as covariates, ANOVA and post-hoc Tukey's HSD tests revealed that linguistic features such as pronoun usage, syntactic complexity, and lexical sophistication showed significant differences between CU, MCI, and AD groups (p < 0.05).
- Correlations indicated that higher pronoun rates and lower syntactic complexity were associated with lower MMSE scores and although some features like conjunctions and determiners approached significance, they lacked consistent differentiation.ConclusionsWith the growing adoption of artificial intelligence (AI)-based scribing, these results emphasize the potential of targeted linguistic analysis as a digital biomarker to enable continuous screening for cognitive impairment.
Study details
- Study designpost-hoc analysis
- Participants336
- Evidence strengthLow
How this study scored 53/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 31 of the 58 points we could judge it on, which is 53 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of Alzheimer's disease : JAD) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | Design recorded as “post-hoc analysis”, which our rubric does not rank (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 336 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40336266) |
| Free to read | 2 / 2 | Free to read |
| Points | 31 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 31 of the 58 points we could judge it on is 53 out of 100. |
| Total | 53 / 100 | — |
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