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This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

✓ Peer-reviewed alzhub usefulness score 45 out of 100. Cognitive tests and assessment

Topic: Risk, prevention and care in population studies

Early-stage Alzheimer's disease (AD) remains difficult to assess using conventional linguistic or cognitive assessments, which often overlook subtle and individualized disruptions in speech. In this work, we propose a novel biomarker discovery framework that leverages fine-grained, character-level information from speech transcripts to capture these early cognitive changes. By encoding transcripts symbolically at the character level and applying recurrence quantification analysis (RQA), we generate interpretable recurrence plots that reveal temporal dynamics in speech patterns such as pauses, repetitions, and hesitations. Siamese neural networks are then used to learn embeddings from these recurrence representations, enabling the discovery of discriminative linguistic biomarkers associated with cognitive decline. Applied to the DementiaBank corpus, our approach uncovers meaningful character-level signatures and enables visualization of subtle cognitive disruptions through recurrence plots. These findings suggest that character-level temporal patterns may offer a promising new direction for digital biomarker discovery in dementia research, complementing traditional word-level analyses and enhancing interpretability for clinical applications.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Mekulu, K., Aqlan, F., & Yang, H. (2025). Character-level linguistic biomarkers for precision assessment of cognitive decline: a symbolic recurrence approach. Frontiers in aging neuroscience. https://doi.org/10.3389/fnagi.2025.1681124

Vancouver

Mekulu K, Aqlan F, Yang H. Character-level linguistic biomarkers for precision assessment of cognitive decline: a symbolic recurrence approach. Frontiers in aging neuroscience. 2025. doi:10.3389/fnagi.2025.1681124. PMID: 41446371.

BibTeX

@article{mekulu2025-41446371,
  author  = {Mekulu, K. and Aqlan, F. and Yang, H.},
  title   = {{Character-level linguistic biomarkers for precision assessment of cognitive decline: a symbolic recurrence approach}},
  journal = {Frontiers in aging neuroscience},
  year    = {2025},
  doi     = {10.3389/fnagi.2025.1681124},
  pmid    = {41446371},
  url     = {https://doi.org/10.3389/fnagi.2025.1681124}
}

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

Key findings

Study details

How this study scored 45/100
How this study's score is builtJournal tier 7 of 10; Journal's citation record left out, no data; Study design 0 of 12; Strength of evidence 1 of 5; Peer-review validation 10 of 10; How recent 4 of 5; Sample size 0 of 4; Effect reported 0 of 3; Studied in people 0 of 3; Randomised / blinded 0 of 2; Times cited left out, no data; Citations per year left out, no data; Impact for its field and year left out, no data; Cited by studies here left out, no data; Still being cited left out, no data; Senior author's track record left out, no data; Institution's track record left out, no data; Multi-centre collaboration left out, no data; Indexed in PubMed 2 of 2; Free to read 2 of 2. 26 of the 58 points it could be judged on, which is 45 of 100. Total 45 of 100.26 of the 58 points we could judge = 45 of 100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people 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 26 of the 58 points we could judge it on, which is 45 out of 100.

ComponentPointsWhy
Journal tier 7 / 10 Tier 3 — peer-reviewed journal (Frontiers in aging neuroscience)
Journal's citation record — / 4 No journal record from OpenAlex — left out of this score
Study design 0 / 12 No study design recorded — scores zero, not guessed (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 0 / 4 Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported 0 / 3 No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people 0 / 3 Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
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 41446371)
Free to read 2 / 2 Free to read
Points 26 / 58 Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 26 of the 58 points we could judge it on is 45 out of 100.
Total 45 / 100 —

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