Spread of pathological tau proteins through communicating neurons in human Alzheimer’s disease
Bibliographic record
Abstract
Abstract Tau is one of the two pathological hallmarks of Alzheimer’s disease, and bears a much closer relationship to local neurodegeneration and cognitive impairment than the other hallmark, β -amyloid. Cell and rodent models have shown evidence that tau spreads from cell to cell through anatomical neuronal connections, and that this process is facilitated by the presence of β -amyloid. We test this hypothesis in humans by using an epidemic spreading model (ESM) to simulate the spread of tau over human neuronal connections, and we compare the simulated pattern of progression to the observed pattern measured in the brains of 312 individuals on the Alzheimer’s disease spectrum, using PET. Fitting our model, we found that the majority of variance in the overall pattern of tau progression could be explained by diffusion of an agent through the human connectome, measured using either functional connectivity or diffusion tractography. These models far exceeded chance, and outperformed models testing the extracellular spread of tau over Euclidian space. Surprisingly, the ESM predicted the spatial patterns of tau irrespective of whether subjects demonstrated evidence for brain β -amyloid. In addition, in β -amyloid-positive subjects only, regions with greater amyloid burden showed greater tau than predicted by connectivity patterns, suggesting a role of amyloid in accelerating the spread of tau in certain isocortical regions. Altogether, our results provide strong evidence that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain β -amyloid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".