P1‐251: Synergism between Brain Amyloid Accumulation and Neuronal Injury in Cortical‐Subcortical Circuits Causes Memory Declines in Animal Models
Bibliographic record
Abstract
A decline in cerebrospinal fluid (CSF) Aβ1-42 is a pathological biomarker and has been reported to be inversely associated with Aβ plaque load in the brain. Also, the cerebral metabolic rate of glucose, as measured by [18F]Fluorodeoxyglucose ([18F]FDG) in Positron Emission Tomography (PET), is widely used as a biomarker of neurodegeneration, which has been shown to be closely related to the cognitive decline observed in Alzheimer’s Disease (AD). Here, we aim to show the synergistic effect of the regional cerebral hypometabolism with increase Aβ load, as represented as a decline in CSF Aβ1-42, on cognitive decline in McGill-R-Thy1-APP transgenic rat model. This transgenic rat models full AD like amyloid pathology, providing a platform to explore the impact of amyloid pathology on imaging biomarkers without the bias of tau pathology invariably present in the human brain. We hypothesized that regression of CSF Aβ1-42 level with the regional cerebral hypometabolism would have a synergistic effect on cognitive decline. A total of 9 Tg were used for this study. The FDG-PET acquisition, Morris Water Maze (MWM), and CSF collection were done longitudinally with 11.5 mo (baseline) and 16.8 mo (follow-up). Individual FDG SUVRs were generated using pons as a reference region. The synergistic effect of hypometabolism and Aβ is demonstrated using following model: ▵Cognition ∼ ▵CSF Aβ1-42*▵[18F]FDG + Sex. The analysis was perform using VoxelStats, which performs statistical analysis at each voxel. Our model revealed that regional hypometabolism in prefrontal cortex, cingulate cortex, entorhinal cortex, and thalams with the decline in CSF Aβ1-42 has a significant synergistic impact in decline in cognition in Tg (Figure 1).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".