P4‐107: REGIONAL PATTERNS OF TAU DEPOSITION DRIVEN BY LOCAL AMYLOID ACCUMULATION RECAPITULATE BRAAK STAGES IN AD
Bibliographic record
Abstract
The amyloid plaques and neurofibrillary tangles (NFTs) are the two major hallmarks of Alzheimer's disease (AD). The histopathological studies hint distinct spreading and progression of amyloidosis and NFTs across AD stage. Given the divergent spatial patterns of the hallmarks, the association between the two abnormal protein aggregates in different brain regions is elusive. Here, we reveal the association between amyloidosis and NFTs in cognitively normal (CN), mild cognitive impairment (MCI), and AD individuals with positron emission tomography (PET). The emergence of novel PET tracers of [F]AZD4694 for amyloidosis and [F]MK6240 for NFTs allows precise investigation of each pathological progression in temporal and spatial in vivo. Here, we hypothesize that association between the two hallmarks spreads from posterior to anterior regions following the disease progression. A total of 16 CN, 11 MCI, and 11 AD patients was used. Each subject underwent PET [F]AZD4694 and [F]MK6240 acquisitions. [F]AZD4694 image was processed from 40 minutes post-injection for 30 minutes. [F]MK6240 image was processed from 90 minutes post-injection for 20 minutes. All images were registered to individual MRI with lsq6. Then, they are transformed into ADNI template using lsq12 with nonlinear transformations. The SUVR parametric map was generated using cerebellar grey matter as a reference region for both images. For statistical analysis, we performed voxel-wise analysis to show the association between amyloidosis and NFTs following the model in each group using VoxelStats. [F]MK6240 SUVR ∼ [F]AZD4694 SUVR + age + gender + APOE + education. The unique association between amyloidosis and NFTs was present in entorhinal cortex and PCC in CN; precuneus, PCC, and parahippocampal gyrus in MCI; ACC, entorhinal cortex, parahippocampal gyrus, and orbitofrontal cortex in AD. All groups showed the association in lateral temporal and middle frontal gyrus.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".