Aβ remotely and locally facilitates Alzheimer’s disease tau spreading
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by the accumulation of amyloid-beta plaques initiated approximately 2 decades before the symptom onset followed by build-up and spreading of neurofibrillary tau aggregates. Although it has been suggested that the amyloid-beta amplifies tau spreading the observed spatial disparity called it into question. Yet, it is unclear how neocortical amyloid-beta remotely affects early pathological tau, triggering it to leave the early formation area, and how amyloid-beta facilitates tau aggregate spreading throughout cortical regions. I aimed to investigate how amyloid-beta can facilitate tau spreading through neuronal connections in the Alzheimer's disease pathological process by combining functional magnetic resonance imaging normative connectomes and longitudinal in vivo molecular imaging data. In total, the imaging data of 317 participants, including 173 amyloid-beta-negative non-demented and 144 amyloid-beta -positive non-demented participants, have entered the study from Alzheimer's Disease Neuroimaging Initiative. Furthermore, normative resting-state functional magnetic resonance imaging connectomes were used to model tau spreading through functional connections. It was observed that the amyloid-beta in regions with the highest deposition (amyloid-beta epicenter) is remotely associated with connectivity-based spreading of tau pathology. Moreover, amyloid-beta in regions that exhibit the highest tau pathology (tau epicenter) is associated with increased connectivity-based tau spreading to non-epicenter regions. The findings provide a further explanation for a long-standing question of how amyloid-beta can affect tau aggregate spreading through neuronal connections despite spatial incongruity. The results suggest that amyloid-beta pathology can remotely and locally facilitate connectivity-based spreading of tau aggregates.
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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 | 0.003 |
| 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".