P3‐441: LOGICAL MEMORY DEFICITS ACROSS ALZHEIMER'S DISEASE SPECTRUM ARE ASSOCIATED WITH PATTERNS OF A TAU PROPAGATION PREDICTED BY BRAAK STAGING
Bibliographic record
Abstract
The biological underpinnings of logical memory deficits remain debated in neurological conditions. Although the stages of neurofibrillary tangle (NFT) formation during the course of AD might possibly play a role in these deficits, the underlying mechanism of how these NFT formations affect this cognitive domain remains unclear. Here we propose to identify the brain regions with NFT deposits that contribute to the cognitive decline across the AD spectrum. [F]AV1451 images of 93 cognitively normal (CN), 66 mild cognitive impairment (MCI) and 16 Alzheimer's disease (AD) was acquired from the ADNI cohort with their corresponding demographic information and logical memory immediate recall and delayed recall subscores. SUVr images were created using cerebellum grey matter as the reference region. Lasso regularized regression was performed in each diagnosis group using 46 ROIs to identify the 10 most contributing brain regions for the decline in immediate recall and delayed recall separately. 50, 000 bootstrap runs were performed to remove bias introduced by the outliers in the dataset. Using the identified brain regions (10) lasso regularized regression was performed to identify the effect of regional TAU deposition to the cognitive measures. In CN stage, TAU deposition in the right lingual gyrus, right parahippocampal gyrus and bilateral hippocampus are associated with the decline in both immediate and delayed recall cognitive measures. In the MCI stage, TAU deposition in bilateral parahippocampai is associated with the decline in immediate recall, whereas the TAU deposition in bilateral parahippocampai and left hippocampus is associated with the decline in delayed recall. In the AD stage, TAU deposits in the right angular gyrus is associated with the decline in immediate recall whereas TAU deposition in right superior parietal gyrus is associated with the decline in delayed recall. [Figure 1, 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".