Spatial Extent of Amyloid-β Levels and Associations with Alzheimer’s Disease Biomarkers
Bibliographic record
Abstract
Abstract Objective: To investigate the biological and clinical correlates of Aβ spatial extent deposition levels in cognitively unimpaired older adults.Methods: We included cognitively unimpaired older adults from three cohorts, totalling 529 participants (PREVENT-AD, n=129; ADNI, n=400 and HABS, n=288) who underwent Aβ positron emission tomography (PET). We used Gaussian-mixture models to identify region-specific thresholds of Aβ positivity in seven brain regions prone to early Aβ accumulation. Individuals were classified as having “widespread” Aβ deposition if they were positive in all seven regions, “regional” Aβ deposition if they were positive in one to six regions, or Aβ negative if negative in all regions. We compared demographics, genetics, tau-PET binding, and cognitive performance and decline between the three groups.Results: In all cohorts, most participants with regional Aβ-PET binding did not meet the cohort-specific criteria for Aβ-positivity (79% for PREVENT-AD, 57% for ADNI, and 100% for HABS). Regional Aβ groups had normal baseline cognition and relatively normal tau-PET binding, but a greater proportion of APOE ε4 carriers, decreased CSF Aβ1-42 levels, and greater amount of longitudinal Aβ-PET binding accumulation (only available in ADNI and HABS) when compared with the Negative Aβ groups. Widespread Aβ groups had lower baseline cognitive performance (PREVENT-AD only), faster cognitive decline (all cohorts) and greater amount of longitudinal tau binding than the other groups (only available in ADNI and HABS). Conclusions: Individuals with regional Aβ deposition might be the best candidate for preventive trials since they do not yet have widespread tau and cognitive decline. Widespread levels of Aβ seem to be needed for tau spreading.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".