Fully individualized models for cross-sectional and longitudinal network-based tau spread
Bibliographic record
Abstract
Abstract Heterogeneity in regional tau burden limits evaluation of disease progression using one-size-fits-all approaches. Prior work using tau positron emission tomography (PET) has highlighted the important role of connectivity to epicenters of tau pathology in explaining this heterogeneity. However, previous studies using population-based epicenters or connectomes fall short of a fully individualized approach to predicting regional tau burden. We use diffusion MRI-derived structural connectomes to assess the prediction of regional tau burden using distance along individual structural connectomes from individualized epicenters of tau pathology both cross-sectionally and longitudinally. Fully individualized models of connectivity and epicenters outperformed models using either population-based connectomes or epicenters in explanation of cross-sectional and longitudinal regional tau burden, improved prediction in validation datasets, and produced stronger single-subject level prediction. Together, these findings demonstrate the power of a fully individualized approach to explain regional tau heterogeneity and provide the strongest in vivo evidence to date for network-based spread of tau pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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".