Mapping amyloid beta predictors of entorhinal tau in preclinical Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Amyloid beta (Aβ) plaques and hyperphosphorylated tau in the entorhinal regions are key Alzheimer's disease (AD) markers, but the spatial Aβ pathways influencing tau pathology remain unclear. METHODS: We applied predictive modeling to identify Aβ standardized uptake value ratio (SUVR) spatial patterns that predict entorhinal tau levels, future hippocampal volume, and Preclinical Alzheimer's Cognitive Composite (PACC) scores at 5-year follow-up. The model was trained on Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 237), incorporating amyloid-PET (positron emission tomography), tau-PET, magnetic resonance imaging (MRI), and cognitive data, and validated on Harvard Aging Brain Study (HABS) (N = 276). RESULTS: The model accurately predicted entorhinal tau levels (r = 0.48, p < 0.0001), future hippocampal volume (r = 0.24, p = 0.002), and PACC scores (r = 0.35, p < 0.0001) based on regional Aβ. DISCUSSION: Aβ in the rostral middle frontal, medial orbitofrontal, and striatal regions predict entorhinal tau levels, future hippocampal volume, and PACC scores, indicating their potential as early biomarkers in AD prediction models. HIGHLIGHTS: Positron emission tomography (PET) imaging reveals amyloid beta (Aβ) patterns predicting entorhinal tau levels in preclinical Alzheimer's disease (AD). Aβ in medial orbitofrontal, rostral middle frontal, and nucleus accumbens best predicts tau. Aβ distribution in these regions predicts future hippocampal neurodegeneration and cognitive decline. Model validated with Alzheimer's Disease Neuroimaging Initiative (ADNI) and Harvard Aging Brain Study (HABS) data sets, showing robustness and reproducibility. Findings suggest early Aβ patterns can aid in diagnosing AD and guide anti-Aβ therapies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".