Personalised regional modelling predicts tau progression in the human brain
Bibliographic record
Abstract
Aggregation of the hyperphosphorylated tau protein is a central driver of Alzheimer's disease, and its accumulation exhibits a rich spatiotemporal pattern that unfolds during the course of the disease, sequentially progressing through the brain across axonal connections. It is unclear how this spatiotemporal process is orchestrated, namely, to what extent the spread of pathologic tau is governed by transport between brain regions, local production, or both. To address this, we develop a mechanistic model from tau PET data to describe tau dynamics along the Alzheimer's disease timeline. Our analysis reveals longitudinal changes in production and transport dynamics in two independent cohorts, with subjects in the early stage of the disease exhibiting transport-dominated spread, consistent with an initial spread of pathological tau seeds, and subjects in the late stage disease characterized primarily by local tau production. Further, we demonstrate that the model can predict accurately subject-specific longitudinal tau accumulation at the regional level, potentially providing a new clinical tool to monitor and classify patient disease progression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".