The Intersection of Central Dopamine System and Stroke: Potential Avenues Aiming at Enhancement of Motor Recovery
Bibliographic record
Abstract
Dopamine, a major neurotransmitter, plays a role in a wide range of brain sensorimotor functions. Parkinson's disease and schizophrenia are two major human neuropsychiatric disorders typically associated with dysfunctional dopamine activity levels, which can be alleviated through the druggability of the dopaminergic systems. Meanwhile, several studies suggest that optimal brain dopamine activity levels are also significantly impacted in other serious neurological conditions, notably stroke, but this has yet to be fully appreciated at both basic and clinical research levels. This is of utmost importance as there is a need for better treatments to improve recovery from stroke. Here, we discuss the state of knowledge regarding the modulation of dopaminergic systems following stroke, and the use of dopamine boosting therapies in animal stroke models to improve stroke recovery. Indeed, studies in animals and humans show stroke leads to changes in dopamine functioning. Moreover, evidence from animal stroke models suggests stimulation of dopamine receptors may be a promising therapeutic approach for enhancing motor recovery from stroke. With respect to the latter, we discuss the evidence for several possible receptor-linked mechanisms by which improved motor recovery may be mediated. One avenue of particular promise is the subtype-selective stimulation of dopamine receptors in conjunction with physical therapy. However, results from clinical trials so far have been more mixed due to a number of potential reasons including, targeting of the wrong patient populations and use of drugs which modulate a wide array of receptors. Notwithstanding these issues, it is hoped that future research endeavors will assist in the development of more refined dopaminergic therapeutic approaches to enhance stroke recovery.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".