Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer’s Disease
Bibliographic record
Abstract
Background: The AT[N] research framework focuses on three major biomarkers in Alzheimer’s disease (AD): amyloid-β deposition (A), pathologic tau (T), and neurodegeneration [N]. Objective: We hypothesize that the diverse mechanisms such as A⟶T and A⟶[N] pathways from one brain region to others, may underlie the wide variation in clinical symptoms. We aim to uncover the causal-like effect of regional AT[N] biomarkers on cognitive decline as well as the interaction with non-modifiable risk factors such as age and APOE4. Methods: We apply multi-variate statistical inference to uncover all possible mechanistic spreading pathways through which the aggregation of an upstream biomarker (e.g., increased amyloid level) in a particular brain region indirectly impacts cognitive decline, via the cascade build-up of a downstream biomarker (e.g., reduced metabolism level) in another brain region. Furthermore, we investigate the survival time for each identified region-to-region pathological pathway toward the AD onset. Results: We have identified a collection of critical brain regions on which the amyloid burdens exert an indirect effect on the decline in memory and executive function (EF) domain, being mediated by the reduction of metabolism level at other brain regions. APOE4 status has been found not only involved in many A⟶N mechanistic pathways but also significantly contributes to the risk of developing AD. Conclusion: Our major findings include 1) the region-to-region A⟶N⟶MEM and A⟶N⟶MEM pathways exhibit distinct spatial patterns; 2) APOE4 is significantly associated with both direct and indirect effects on the cognitive decline while sex difference has not been identified in the mediation analysis.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".