Brain connectivity drives tau presence while regional vulnerability drives tau load in Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) tau pathology is believed to propagate cell-to-cell through synaptic connections, but local properties - both intrinsic and dynamic - likely influence cellular vulnerability to tau accumulation. This study uses computational models to disentangle the roles of brain connectivity and regional vulnerability in determining where (presence) and how much (load) tau accumulates across brain regions. METHOD: We analyzed [18F]RO948-PET data across 66 Desikan-Killiany regions from 646 Aβ-positive participants (219 unimpaired, 212 mild cognitive impairment, 215 AD dementia) from the Swedish BioFINDER-2 study. tau-PET standardized uptake value ratios (SUVR) represented tau load. Tau presence was calculated as tau-PET-positive probabilities from Gaussian mixture models (Figure 1b,1c). The Susceptible-Infected-Removed (SIR) model simulated regional tau synthesis, misfold, clearance, and spread over a connectome, with a priori regional information influencing model parameters. Exploratory models used eight connectome types and 49 spatial maps of brain properties (Figure 1a,2a,2b), and were fit over whole-population and subtype-specific tau patterns. Pearson correlation between simulated and observed tau patterns assessed model performance. RESULT: Tau presence aligned better with Braak staging compared to tau load. SC-based models better explained tau presence than tau load (Figure 1d,1e), but including regional AD factors improved simulations more for tau load (Figure 1f,1g,2c). Exploratory analyses validated known mechanisms, including SC-guided spreading influencing by MAPT (misfolding/synthesis) and Aβ (spreading), but highlighted under-explored mechanisms like CSF clearance, developmental morphometry and various receptor distributions (Figure 2d,2e). Modeling tau propagation through receptor similarity networks yielded the best results overall. Subtype analyses reproduced known patterns for tau load patterns but no subtypes of tau presence emerged (Figure 3a,3b), suggesting consistent propagation pathways across individuals but variability in accumulation. Analyzing tau load subtypes, the MTL-sparing subtype originated in the precuneus and propagated through functional connections, unlike other subtypes originating in the entorhinal cortex and spreading anatomically (Figure 3c,3d). CONCLUSION: Tau presence follows a Braak-like pattern driven by connectivity, while tau load varies across people based on regional vulnerability. These findings validate known mechanisms but highlight under-explored contributions of receptor distributions to tau accumulation.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".