[IC‐P‐045]: AMYLOID‐BETA MODULATES CEREBRAL METABOLIC NETWORK IN RATS AND HUMANS
Bibliographic record
Abstract
The amyloid hypothesis proposes that the amyloid pathology affects a series of downstream AD pathophysiology; changes in the brain metabolism, aggregation of neurofibrillary tangles, dysfunctional network, structural changes, and cognitive decline. This is supported by the typical pattern of amyloid-beta (Aβ) deposition that overlaps with the regional hypometabolism based on FDG-PET. Felix et al. have reported abnormal metabolic network in MCI Aβ+ individuals compared to Aβ- individuals. However, the interregional association between Aβ and FDG as well as interregional metabolic network (IMN) is still elusive. Here, we used Alzheimer's disease neuroimaging initiative (ADNI) database and McGill-R-Thy1-APP (Tg) to investigate the difference in IMN between Aβ+ and Aβ-. Furthermore, we characterized the difference in interregional association between Aβ and metabolism (IAMN) between Aβ+ and Aβ-. A total of 392 subjects from ADNI cohort (228 CN- = Aβ-, 164 MCI+ = Aβ+) was used and 17 (7 WT = Aβ-, 10 Tg = Aβ+) were used for this study. The ROC-based Aβ cut-off (1.286) was used to characterize CN- = Aβ- or MCI+ = Aβ+ from CN (287) and AD (176). Human IMN was generated based on the 201 nodes in 228 CN- and 164 MCI+. Rat IMN was generated based on 61 nodes in 7 WT and 10 Tg. The same regions were used to generate IAMN. Fisher's Z-transformation is applied to compare the network association between CN- and MCI+ as well as WT and Tg. MCI+ showed greater association compared to CN- in hippocampus, medial and lateral temporal cortex, superior parietal cortex, and fornix in IMN. Similarly, Tg showed greater association compared to WT in basal temporal cortex, parietotemporal cortex, and hypothalamus in IMN. Furthermore, regional Aβ association in lateral frontal cortex, lateral temporal cortex, and PCC/Precuneus in IMN metabolism were greater in MCI+ compared to CN-. Analogous regional Aβ associations were greater in Tg compared to WT in IMN.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".