Reproducibility of a Parkinsonism‐related metabolic brain network in non‐human primates: A descriptive pilot study with FDG PET
Bibliographic record
Abstract
BACKGROUND: We have previously defined a parkinsonism-related metabolic brain network in rhesus macaques using a high-resolution research positron emission tomography camera. This brief article reports a descriptive pilot study to assess the reproducibility of network activity and regional glucose metabolism in independent parkinsonian macaques using a clinical positron emission tomography/CT camera. METHODS: [(18)F]fluorodeoxyglucose PET scans were acquired longitudinally over 3 months in three drug-naïve parkinsonian and three healthy control cynomolgus macaques. Group difference and test-retest stability in network activity and regional glucose metabolism were evaluated graphically, using all brain images from these macaques. RESULTS: Comparing the parkinsonian macaques with the controls, network activity was elevated and remained stable over 3 months. Normalized glucose metabolism increased in putamen/globus pallidus and sensorimotor regions but decreased in posterior parietal cortices. CONCLUSIONS: Parkinsonism-related network activity can be reliably quantified in different macaques with a clinical positron emission tomography/CT scanner and is reproducible over a period typically employed in preclinical intervention studies. This measure can be a useful biomarker of disease process or drug effects in primate models of Parkinson's disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".