Association between left hemisphere MK6240 uptake and language dysfunction in aging and Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is characterized by a progressive deterioration of multiple cognitive domains. The relative effect of amyloid plaques, neurofibrillary tangles and neurodegeneration on cognitive decline, more specifically in the language‐related areas, is still unclear. The objective of this study was to investigate the independent effects of Amyloid‐β, Tau and neurodegeneration on language using The Boston Naming Test. Method The present study was conducted in a population of 163 individuals (116 cognitively unimpaired, 33 mild cognitively impaired and 14 AD). Boston naming Test (BNT) was used to measure the confrontational word retrieval in individuals. Tau was assessed with [18F]MK6240. [18F]MK6240 standardized uptake value ratios (SUVRs) used the cerebellum grey matter as the reference region and were calculated between 90‐110 min post‐injection. A voxel‐based regression model evaluated the relationship between BNT and [18F]MK6240, adjusting for age, sex, years of education, diagnosis. Analyses were corrected for multiple comparisons using random field theory at p<0.001. Result In the present study, negative associations were observed between the levels of BNT scores and accumulation of tau indexed by [18F]MK6240‐PET. The brain regions affected were the medial frontal, parietal and temporal lobes, with a predominance in the left hemisphere. Conclusion These results support the claim that only tau seems to be related to language dysfunction in aging and AD. Associations were more pronounced in the left hemisphere, recapitulating known functional neuroanatomy of language production in humans. Our results support the emerging concept that tau pathology is a major driver of local neurodegeneration and domain‐specific cognitive impairment.
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 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 | 0.001 |
| 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 | 0.000 | 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".