P1‐159: Amyloidosis induces reorganization of the hippocampal metabolic network
Bibliographic record
Abstract
Rat transgenic models of human brain amyloidosis constitute a unique opportunity to explore the impact of amyloid pathology on imaging biomarkers without the bias of tau pathology invariably present in the human brain. The cerebral metabolic rate of glucose measured by Positron Emission Tomography using [F]FDG is often used as a biomarker of neurodegeneration in Alzheimer's disease (AD). Metabolic network refers to population-based maps depicting large-scale organization of brain glucose utilization. There has been growing evidence, suggesting that brain amyloidosis modulates metabolic changes observed in the progression of AD pathophysiology. Here, we investigate the effect of amyloidosis on hippocampal metabolic network in wild type (WT) and transgenic (Tg) Mcgill-R-Thy1-APP rats, which express amyloidosis in the absence of tangles or cell depletion. We hypothesized adaptations of brain metabolism in early stages of amyloidosis followed by declines in the metabolism in aged animals. A total of 17 rats (10 WT, 7 Tg) were used for this study. The FDG-PET acquisition was done longitudinally with 11.5 mo (baseline) and 16.8 mo (follow-up). Individual FDG SUVRs were generated using pons as a reference region. Population based correlation analyses were generated using the dorsal and ventral hippocampi. Tg and WT hippocampal metabolic networks maps were compared at voxel- levels using Fisher's Z transformation. WT hippocampal metabolic network [Baseline vs follow-up] contrast did not reveal significant differences. As compared to McGill-R-Thy1-APP rat showed increased strength of correlation and recruitment of additional cortical areas at baseline, while a drastic decline in the hippocampal metabolic network was noted at follow-up. When performed Fisher's Z transformations, baseline Tg showed significant correlation in subcortical structures such as thalamus and small regions in medial temporal lobe compared to baseline and follow-up WT. Baseline Tg showed significant correlation in medial temporal lobe, bilateral hippocampi, amygdala, and cingulate cortex compared to follow-up 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".