Pharmacological characterization of psychosis‐like behavior in the MPTP‐lesioned nonhuman primate model of Parkinson's disease
Bibliographic record
Abstract
Investigation of the pathophysiology of psychosis in Parkinson's disease (PD), as well as the assessment of potential novel therapeutics, has been limited by the lack of a well-validated animal model. MPTP-lesioned primates exhibit abnormal behaviors that are distinct from dyskinesia and parkinsonism and may represent behavioral correlates of neural processes related to psychosis in PD. Here we assess four types of behavior--agitation, hallucinatory-like responses to nonapparent stimuli, obsessive grooming, and stereotypies that are termed "psychosis-like"--and define their pharmacology using a psychosis-like behavior rating scale. By assessing the actions of drugs known to enhance or attenuate psychosis in PD patients, we find that the pharmacology of these behaviors recapitulates, in several respects, the pharmacology of psychosis in PD. Thus, levodopa and apomorphine elicited psychosis-like behaviors. Amantadine significantly decreased levodopa-induced dyskinesia but exacerbated psychosis-like behaviors. Haloperidol reduced psychosis-like behaviors but at the expense of increased parkinsonian disability while the atypical neuroleptics clozapine and quetiapine reduced psychosis-like behaviors without significant effect on parkinsonian disability. The response of different components of the psychotomimetic behavior suggested the involvement of both dopaminergic and nondopaminergic mechanisms in their expression.
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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 | 0.001 | 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 | 0.001 |
| 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".