P2‐355: EPIDEMIC SPREADING OF TAU THROUGH HUMAN FUNCTIONAL BRAIN CONNECTIONS
Bibliographic record
Abstract
Animal studies have provided strong evidence for the propagation of neurofibrillary tau tangles through connected neurons. Here, we test the theory of tau spreading through communicating neurons in humans by placing a seed in the entorhinal cortex and allowing it to diffuse naturally through the human connectome, and comparing the predicted pattern of tau to observed regional tau load in living humans. AV1451-PET scans from 175 cognitively intact individuals (69 amyloid-positive), 57 amyloid-positive individuals with mild cognitive impairment and 58 amyloid-positive individuals with suspected Alzheimer's dementia were aggregated from two multicenter studies and processed using a standard pipeline. Mean native-space AV1451 signal was extracted within 66 cortical ROIs from the Desikan-Killiany atlas, and these values were converted to tau-positive probabilities using regional mixture-modeling. A previously described Epidemic Spreading diffusion model (ESM) was fit to each individual's AV1451-PET data, where free parameters related to subject-specific age-at-onset, clearance rate and production rate were solved through simulation. The ESM was fit over resting-state functional connections extracted from 74 healthy young individuals; and separately over Euclidian distances between ROIs. Each model was fit specifying the bilateral entorhinal cortex as the epicenter of spread. ROI-level AV45 data was downloaded for ADNI subjects and converted to positivity-probabilities. Threshold-free conversion of tau-PET data to tau-positivity probabilities resulted in a sparse and ordered distribution resembling previously described pathological spread and staging patterns (Figure 1). The ESM fitted over the functional connectome predicted 56% of the variance in whole-brain tau-PET pattern, whereas the Euclidian distance matrix explained only 22% (Figure 2A, 3). Furthermore, regions with higher amyloid tended to be underestimated by the model (p<0.001; Figure 2B-D).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".