Tau spreading is driven by neuronal connectivity in primary tauopathies - evidence from tau-PET and histopathology
Bibliographic record
Abstract
ABSTRACT Tau pathology is the main driver of neuronal dysfunction in 4-repeat tauopathies (4RT), including cortico-basal degeneration and progressive supranuclear palsy (PSP). Tau is assumed to spread prion-like across connected neurons, but the mechanisms of tau propagation are largely elusive in 4RTs, characterized not only by neuronal but also by astroglial and oligodendroglial tau accumulation. Here, we assessed whether connectivity drives 4R-tau spreading patterns by combining resting-state fMRI connectomics with both 2 nd generation 18 F- PI-2620 tau-PET in 46 patients with clinically diagnosed 4RTs and post-mortem cell-type- specific regional tau assessments from two independent PSP samples (n=97/96). We found that inter-regional connectivity was associated with higher inter-regional correlation of both tau- PET and post-mortem tau levels in 4RTs. In regional cell-type specific post-mortem tau assessments, this association was stronger for neuronal than for astroglial or oligodendroglial tau, suggesting that connectivity is primarily associated with trans-neuronal tau spread. Using tau-PET we found that patient-level tau patterns can be predicted by the connectivity of subcortical tau epicenters. Together, the current study provides combined in vivo tau-PET and histopathological evidence for brain connectivity as a key mediator of trans-neuronal tau spreading in 4RTs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".