O3‐07‐05: AMYLOID‐DEPENDENT AND AMYLOID‐INDEPENDENT EFFECTS OF TAU ON CLINICAL STATUS
Bibliographic record
Abstract
Recent tau-PET studies have emphasized that tau NFTs are more closely linked to atrophy and domain-specific cognitive dysfunction than Aβ deposition. Accordingly, recent research frameworks have emerged proposing “amyloid-dependent” and amyloid-independent” phases of AD. However, animal models have suggested that the interaction between Aβ and tau pathologies may better explain cognitive function as compared to the additive effects of both pathological hallmarks of AD. Here, we test whether the interaction between amyloid and tau pathologies is related to clinical function beyond their individual additive effects. We first examined cognitively normal (n=87), MCI (n=16) and AD (n=25) participants who underwent tau-PET with [F]MK6240 and amyloid-b-PET with [F]AZD4694. We also assessed an independent sample of cognitively normal (n=120), MCI (n=63) and AD (n=24) individuals who underwent tau-PET with [F]AV1451 and amyloid-b-PET with [F]Florbetapir from ADNI. Global cognitive status was assessed using the CDR Sum of Boxes (CDR-SoB). A voxel-wise interaction model was built to assess the main and interactive effects of Amyloid PET and Tau PET SUVRs on clinical status. Age and years of education were employed as covariates in each model. Voxel-wise analyses revealed dissociated regional effects for amyloid-PET, tau-PET, and the synergistic interaction between the two. In the TRIAD cohort (Fig.1), the synergistic effect of amyloid-β and tau SUVR was associated with worse clinical status in the posterior cingulate/precuneus, inferior parietal cortices and medial prefrontal cortex. In ADNI, a significant synergistic effect of amyloid-β and tau SUVR was associated with worse clinical status in the precuneus, anterior and posterior cingulate, lateral temporal, medial prefrontal and basal forebrain cortices was associated with worse clinical status across the AD spectrum (Fig.2).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".