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Record W4312086463 · doi:10.1002/alz.068093

Association between tau uptake and verbal fluency converge in language centers

2022· article· en· W4312086463 on OpenAlexaff
Alyssa Stevenson, Nesrine Rahmouni, Joseph Therriault, Jenna Stevenson, Cécile Tissot, Firoza Z Lussier, Gleb Bezgin, Mira Chamoun, Tharick A. Pascoal, Serge Gauthier, Andréa Lessa Benedet, Paolo Vitali, Pedro Rosa‐Neto

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsDouglas Mental Health University InstituteMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsVerbal fluency testPsychologyFluencyNeuropsychologyCognitionPopulationAssociation (psychology)Cognitive psychologyClinical psychologyAudiologyDevelopmental psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Recent studies have shown that pathological tau accumulation, a hallmark of Alzheimer’s disease (AD), is closely related to cognitive deficits in older populations. Semantic deficits, measured using tests of category verbal fluency, are among the features of early clinical stages of AD presentations. While the association between semantic decline and AD biomarkers have been evaluated, the use of a novel tau positron emission tomography (PET) tracer [18F]MK6240, remains relatively unexplored. The present study aims to evaluate whether tau accumulation is associated with participants’ performance on both category and letter fluency neuropsychological tests and whether category fluency performance is disproportionately impaired in AD in comparison to letter fluency. Method The study was conducted in a population of 298 individuals (188 cognitively unimpaired, 110 cognitively impaired). The D‐KEFS Category Fluency test was used to assess semantic knowledge while Letter Fluency was used to measure lexical speed access. Participants also underwent the Mini‐Mental State Examination (MMSE) to assess overall global cognitive functioning. Accumulation of tau was assessed with [18F]MK6240 PET and standardized uptake value ratio (SUVRs) were calculated between 90 to 110 min post‐injection, using cerebellum grey matter as the reference region. A voxel‐based regression model evaluated the relationship between the cognitive scoring and the PET marker [18F]MK6240, correcting for age, sex, education, diagnosis and RFT was used to account for multiple comparisons. Result This study found negative associations between Category and Letter Fluency scores and tau accumulation. Notably, stronger associations were found in the Category Fluency grouping. The associated regions are mainly in the left hemisphere in language related regions and the temporal lobe. Letter Fluency scores demonstrated the strongest association in the frontal regions. Comparatively, the MMSE scores were strongly associated with tau accumulation in both right and left hemispheres. Conclusion Tau accumulation is associated with language dysfunction and is a major driver of domain‐specific cognitive impairment across the AD spectrum. Associations were more pronounced in the left hemisphere for both semantic knowledge and lexical speed. These results also suggest that tau accumulation is more strongly associated with semantic knowledge as indicated by uptake in language related areas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.295
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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