Mood disturbances in Parkinson's disease: From prodromal origins to application of animal models
Bibliographic record
Abstract
Parkinson's disease (PD) is a complex illness with a constellation of environmental insults and genetic vulnerabilities being implicated. Strikingly, many studies only focus on the cardinal motor symptoms of the disease and fail to appreciate the major non-motor features which typically occur early in the disease process and are debilitating. Common comorbid psychiatric features, notably clinical depression, as well as anxiety and sleep disorders are thought to emerge before the onset of prominent motor deficits. In this review, we will delve into the prodromal stage of PD and how early neuropsychiatric pathology might unfold, followed by later motor disturbances. It is also of interest to discuss how animal models of PD capture the complexity of the illness, including depressive-like characteristics along with motor impairment. It remains to be determined how the underlying PD disease processes contributes to such comorbidity. But some of the environmental toxicants and microbial pathogens implicated in PD might instigate pro-inflammatory effects favoring α-synuclein accumulation and damage to brainstem neurons fueling the evolution of mood disturbances. We posit that comprehensive animal-based research approaches are needed to capture the complexity and time-dependent nature of the primary and co-morbid symptoms. This will allow for the possibility of early intervention with more novel and targeted treatments that fit with not only individual patient variability, but also with changes that occur over time with the evolution of the 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".