Topographical Overlapping of the Amyloid-β and Tau Pathologies in the Default Mode Network Predicts Alzheimer’s Disease with Higher Specificity
Bibliographic record
Abstract
BACKGROUND: While amyloid-β (Aβ) plaques and tau tangles are the well-recognized pathologies of Alzheimer's disease (AD), they are more often observed in healthy individuals than in AD patients. This discrepancy makes it extremely challenging to utilize these two proteinopathies as reliable biomarkers for the early detection as well as later diagnosis of AD. OBJECTIVE: We hypothesize and provide preliminary evidence that topographically overlapping Aβ and tau within the default mode network (DMN) play more critical roles in the underlying pathophysiology of AD than each of the tau and/or Aβ pathologies alone. METHODS: We used our newly developed quantification methods and publicly available neuroimaging data from 303 individuals to provide preliminary evidence of our hypothesis. RESULTS: We first showed that the probability of observing overlapping Aβ and tau is significantly higher within than outside the DMN. We then showed evidence that using Aβ and tau overlap can increase the reliability of the prediction of healthy individuals converting to mild cognitive impairment (MCI) and to a lesser degree converting from MCI to AD. Finally, we provided evidence that while the initial accumulations of Aβ and tau seems to be started independently in the healthy participants, the accumulations of the two pathologies interact in the MCI and AD groups. CONCLUSION: These findings shed some light on the complex pathophysiology of AD and suggest that overlapping Aβ and tau pathologies within the DMN might be a more reliable biomarker of AD for early detection and later diagnosis of the disease.
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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 | 0.005 |
| 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 | 0.001 |
| 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".