Verbal fluency associated with tau accumulation and not amyloid deposition in the Alzheimer’s disease spectrum
Bibliographic record
Abstract
Abstract Background Recent studies have shown that pathological amyloid deposition and tau accumulation, hallmarks of the Alzheimer's disease, are closely related to cognitive deficits in older individuals. The present study evaluates the associations between the Alzheimer's disease hallmarks and verbal fluency and lexical speed access impairment. Method The study was conducted in a population of 262 individuals (162 cognitively unimpaired individuals (CU), 100 cognitively impaired individuals (CI; 61 MCI and 39 AD). The verbal Letter Fluency test was used to assess the vocabulary size and lexical speed access. Individuals underwent an MRI, a [18F]AZD4694 amyloid‐PET scan and a [18F]MK6240 tau‐PET scan. [18F]AZD4694 and [18F]MK6240 standardized uptake value ratio (SUVRs) were calculated between 40 to 70 min and 90 to 110 min post‐injection, respectively, using cerebellum grey matter as the reference region. A voxel‐based regression model evaluated the relationship between the cognitive scoring and the PET markers [18F]AZD4694 and [18F]MK6240, correcting for age, sex, education, APOE, diagnosis and RFT was used to account for multiple comparisons Result Negative correlations were found between Letter Fluency scores and tau accumulation. The associated regions were medial frontal, superior temporal, medial occipital lobes and precuneus area. No associations were found between the Letter Fluency scoring and amyloid deposition. Conclusion Our results suggest that tau accumulation is associated with lower vocabulary size and lexical speed access, whereas amyloid deposition does not influence this neuropsychological field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".