Part of speech production in patients with primary progressive aphasia: An analysis based on natural language processing
Open accessAbstract
Background: Primary progressive aphasia (PPA) is a neurodegenerative disorder characterized by a progressive decline of language functions. Its symptoms are grouped into three PPA variants: nonfluent PPA, logopenic PPA, and semantic PPA. Grammatical deficiencies differ depending on the PPA variant. Aims: This study aims to determine the differences between PPA variants with respect to part of speech (POS) production and to identify morphological markers that classify PPA variants using machine learning. By fulfilling these aims, the overarching goal is to provide objective measures that can facilitate clinical diagnosis, evaluation, and prognosis. Method and Procedure: Connected speech productions from PPA patients produced in a picture description task were transcribed, and the POS class of each word was estimated using natural language processing, namely, POS tagging. We then implemented a twofold analysis: (a) linear regression to determine how patients with nonfluent PPA, semantic PPA, and logopenic PPA variants differ in their POS productions and (b) a supervised classification analysis based on POS using machine learning models (i.e., random forests, decision trees, and support vector machines) to subtype PPA variants and generate feature importance (FI). Outcome and Results: Using an automated analysis of a short picture description task, this study showed that content versus function words can distinguish patients with nonfluent PPA, semantic PPA, and logopenic PPA variants. Verbs were less important as distinguishing features of patients with different PPA variants than earlier thought. Finally, the study showed that among the most important distinguishing features of PPA variants were elaborative speech elements, such as adjectives and adverbs.
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Across primary progressive aphasia variants, ASLA markers capture syndrome-specific patterns that predict underlying neuropathology years before death and correlate with variant-specific atrophy, even longitudinally.
“Across primary progressive aphasia variants, ASLA markers capture syndromespecific patterns 49-53 that predict underlying neuropathology years before death 7 and correlate with variant-specific atrophy, 53-56 even longitudinally. 57”
From Speech and language markers of neurodegeneration: a call for global equity (2023) · cites this paper as [49]
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Distribution of nouns, verbs, and adjectives in phonemic fluency tasks
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Precision Physician Allocation System
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Open Brain AI: automating language analysis
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- Authors
- Themistocleous, C., Webster, K., Afthinos, A., & Tsapkini, K.
- Affiliations
- Johns Hopkins University (US)
- Venue
- American Journal of Speech-Language Pathology
- Published
- 2021-02-11
- Volume
- 30
- Issue
- 1S
- Pages
- 466-480
- ISSN
- 1058-0360 (ISSN-L)
- 1558-9110
- PMC
- PMC8702871
- OpenAlex
- W3044885756
- Cited by
- 41
- References
- 62
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 12 · 2023: 7 · 2022: 6 · 2021: 5
- FWCI
- 3.28
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Speech-Language Pathology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (R01AG068881)
- National Institute on Deafness and Other Communication Disorders (R01DC014475)
- 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 › Medicine › Physiology › Voice and Speech Disorders
- Keywords
- primary progressive aphasia, morphological markers, picture description task, automated language assessment, feature importance, verb, adjectives and adverbs
- MeSH terms
- Humans, Language, Natural Language Processing, Semantics, Speech, Aphasia, Primary Progressive
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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