Human evidence of a supra‐spinal modulating role of dopamine on pain perception
Bibliographic record
Abstract
BACKGROUND: Over the last decades, remarkable progresses have been accomplished regarding the understanding of the neurophysiologic and neuropharmacological bases of pain. A growing preclinical literature supports a role for substance P, endogenous opioids, glutamate, serotonin, and norepinephrine in pain perception. Recently, a series of studies explored the function of dopamine in pain perception, which we review here, while focusing on human studies. METHODS: The literature was screened using electronic databases. RESULTS: We found evidence from genetics, brain imaging, neuropsychiatry, and pharmacology of an involvement of dopamine in pain processing. Using positron emission tomography and molecular genetics, studies have been performed in healthy volunteers and patients suffering from chronic pain conditions, showing a key role of dopamine in pain perception. Moreover, abnormal pain perception has been documented in neuropsychiatric disorders, such as Parkinson's disease and schizophrenia, where dopamine has a pathophysiological role. Lastly, pharmacological studies have shown that dopaminergic drugs (antipsychotics, antiparkinsonian drugs, atypical antidepressants, psychostimulants) modify pain perception. DISCUSSION: Although there is growing evidence supporting a role of dopamine in pain perception, the mechanisms by which dopamine influences pain processing remains to be determined. On the basis of preliminary findings, we put forth the hypothesis that dopamine is involved in endogenous pain modulation systems, and further discuss the implications of this hypothesis for the understanding of the physiopathology of chronic pain disorders associated with dysfunctional endogenous pain modulation systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".