Behavioral analysis of motor and non-motor impairment in rodent models of Parkinson's disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by the degeneration of dopamine neurons in the substantia nigra pars compacta, leading to motor and non-motor symptoms. While motor symptoms such as rigidity, tremor, bradykinesia/akinesia, and postural instability are well-recognized, non-motor symptoms including cognitive decline, depression, and anxiety also significantly impact patients' quality of life. Preclinical research utilizing animal models has been instrumental in understanding PD pathophysiology and exploring therapeutic interventions. Various approaches, including genetic manipulations and toxin-induced insults, aim to recapitulate both motor and non-motor aspects of PD in animal models. However, no single model fully replicates the complex spectrum of PD symptoms. Behavioral assessments play a crucial role in evaluating motor impairments in PD animal models, focusing on the manifestation of Parkinsonian motor phenotype. These assessments encompass locomotor activities, motor behavior abnormalities, and induced rotational behavior. Similarly, non-motor features are assessed through tests evaluating behavioral alterations such as depression, anxiety, and cognitive impairment. Although numerous animal models of PD have been developed, including non-human primates and both mammalian and non-mammalian species, this review focuses on motor and non-motor testing methodologies in rodent models, which are the most commonly used. Emphasizing genetic and toxin-induced PD models in mice and rats, we highlight key testing strategies and the significance of each method in addressing specific research questions and interpreting experimental data. By providing a comprehensive overview of these testing approaches, this review aims to advance understanding and foster progress in PD research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".