The role of serotonin in the antidyskinetic effects of deep brain stimulation: focus on antipsychotic-induced motor symptoms
Bibliographic record
Abstract
Treatment with the classic antipsychotic drugs (APDs), such as haloperidol (HAL), is associated with both acute and chronic motor side effects. Acutely, these drugs may induce extrapyramidal symptoms, whereas a prolonged treatment may result in tardive dyskinesia (TD). Atypical antipsychotics have a lower incidence of motor side effects, which have been partially ascribed to the antagonism of serotonin (5-HT) receptors. Although there is currently no satisfactory pharmacotherapy for TD, deep brain stimulation (DBS) has emerged as a promising therapy. However, the mechanisms underlying its effects remain largely unknown. DBS has been shown to affect several neurotransmitter systems, including 5-HT. In this review, we outline the involvement of 5-HT in the development of HAL-induced catalepsy and TD. We also discuss the evidence for DBS-induced alterations in 5-HT function and the relevance of serotonergic alterations to the antidyskinetic effects of DBS. The evidence suggests that the serotonergic mechanisms may be involved in the acute and chronic motor side effects of APDs as well as in adverse psychiatric effects that have been reported following DBS. However, the current evidence suggests that 5-HT alterations do not play an important role in the effectiveness of DBS in models of dyskinesias induced by chronic APDs.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".