Tau deposition assessed by [<sup>18</sup>F]MK6240 PET is associated with longitudinal decrease in grey matter density across the spectrum of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Reduction in grey matter (GM) is a well‐established neuroimaging finding in Alzheimer’s disease (AD). Here, we sought to determine whether baseline tau‐PET using the high‐affinity tracer [18F]MK6240 is predictive of longitudinal changes in GM across the AD spectrum. Method Baseline [18F]MK6240 PET data and longitudinal structural MRI was acquired for 79 participants in the TRIAD cohort (47 CN, 20 MCI, 12 AD). [18F]MK6240 standardized uptake value ratio (SUVR) were calculated 90‐110 minutes post‐injection using cerebellar GM as the reference region. T1‐weighted MR images were segmented into probabilistic GM and WM maps, which were non‐linearly registered to the ADNI template using DARTEL and smoothed with an 8mm FWHM Gaussian kernel. Voxel‐based morphometry (VBM) was run on GM and WM maps. Longitudinal changes in GM density were indexed by voxel‐wise percentage change in VBM‐derived GM. Voxel‐based regression analyses were conducted to examine associations between baseline [18F]MK6240 SUVR and change in GM density, with age, gender, years of education, diagnosis, and time interval between MRI acquisitions employed as covariates. Result Baseline [18F]MK6240 SUVRs in Braak I/II, III/IV, and V/VI were significantly correlated with longitudinal decrease in GM density in the lateral and medial temporal lobe, including hippocampal regions. All results survived correction for multiple comparisons using random field theory at p < 0.001. Conclusion Our results suggest that tau pathology at baseline is associated with the progression of GM atrophy over time in brain regions vulnerable to AD pathological changes, providing evidence that tau‐PET may identify individuals who are more susceptible to atrophy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".