Reshaping the Amyloid Buildup Curve in Alzheimer Disease? Partial-Volume Effect Correction of Longitudinal Amyloid PET Data
Bibliographic record
Abstract
It was hypothesized that the brain β-amyloid buildup curve plateaus at an early symptomatic stage of Alzheimer disease (AD). Atrophy-related partial-volume effects (PVEs) degrade signal in hot-spot imaging techniques such as amyloid PET. The current study, a longitudinal analysis of amyloid-sensitive PET data, investigated the effect on the shape of the β-amyloid curve in AD when PVE correction (PVEC) is applied. Methods: We analyzed baseline and 2-y follow-up data for 216 symptomatic individuals on the AD continuum (positive amyloid status) enrolled in the Alzheimer’s Disease Neuroimaging Initiative (17 with AD dementia and 199 with mild cognitive impairment), including 18F-florbetapir PET, MRI, and Mini Mental State Examination scores. For PVEC, the modified Müller–Gärtner method was performed. Results: Compared with non–PVE-corrected data, PVE-corrected data yielded significantly higher changes in regional and composite SUV ratio (SUVR) over time (P = 0.0002 for composite SUVRs). Longitudinal SUVR changes in relation to Mini Mental State Examination decreases showed a significantly higher slope for the regression line in the PVE-corrected than in the non–PVE-corrected PET data (F1 = 7.1, P = 0.008). Conclusion: These PVEC results indicate that the β-amyloid buildup curve does not plateau at an early symptomatic disease stage. A further evaluation of the impact of PVEC on the in vivo characterization of time-dependent AD pathology, including the reliable assessment and comparison of other amyloid tracers, is warranted.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".