Patient-specific functional brain architecture explains cortical patterns of tau PET in Alzheimer's disease
Bibliographic record
Abstract
The spatial distribution of tau pathology, the core driver of neurodegeneration in Alzheimer's disease (AD), varies markedly across individuals. While tau is thought to spread along brain networks, the role of inter-individual variability in shaping these patterns remains underexplored. Using resting-state fMRI and tau-PET from 805 participants across the AD continuum, we studied whether subject-specific functional connectivity (FC) profiles enhance the characterization of tau deposition patterns. A hybrid approach integrating individual and group-average FC outperformed both alone, particularly in symptomatic individuals and at finer spatial resolutions, the latter underscoring a critical but often overlooked role of spatial scale. Individualized FC also better captured individual tau topographies than canonical tau-PET maps derived from cohort-level data. These effects were specific to tau, and not seen for β-amyloid, and their predictive power increased with spatial granularity. Furthermore, baseline FC also predicted future tau accumulation at the individual level, supporting its prognostic value. Together, these findings provide strong evidence that individual functional brain architecture shapes tau propagation in humans, supporting the network spread hypothesis by showing that variability in connectivity translates into heterogeneity in tau distribution. This work advances biological understanding of tau propagation in AD, highlighting functional connectivity as a mechanistic substrate that supports prognostic assessment of tau trajectories.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".