Abnormal Metabolic Brain Networks in a Nonhuman Primate Model of Parkinsonism
Bibliographic record
Abstract
Parkinson's disease (PD) is associated with a characteristic regional metabolic covariance pattern that is modulated by treatment. To determine whether a homologous metabolic pattern is also present in nonhuman primate models of parkinsonism, 11 adult macaque monkeys with parkinsonism secondary to chronic systemic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and 12 age-matched healthy animals were scanned with [(18)F]fluorodeoxyglucose (FDG) positron emission tomography (PET). A subgroup comprising five parkinsonian and six control animals was used to identify a parkinsonism-related pattern (PRP). For validation, analogous topographies were derived from other subsets of parkinsonian and control animals. The PRP topography was characterized by metabolic increases in putamen/pallidum, thalamus, pons, and sensorimotor cortex, as well as reductions in the posterior parietal-occipital region. Pattern expression was significantly elevated in parkinsonian relative to healthy animals (P<0.00001). Parkinsonism-related topographies identified in the other derivation sets were very similar, with significant pairwise correlations of region weights (r>0.88; P<0.0001) and subject scores (r>0.74; P<0.01). Moreover, pattern expression in parkinsonian animals correlated with motor ratings (r>0.71; P<0.05). Thus, homologous parkinsonism-related metabolic networks are demonstrable in PD patients and in monkeys with experimental parkinsonism. Network quantification may provide a useful biomarker for the evaluation of new therapeutic agents in preclinical models of PD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".