Interactions Between the Serotonergic and Other Neurotransmitter Systems in the Basal Ganglia: Role in Parkinson’s Disease and Adverse Effects of L-DOPA
Bibliographic record
Abstract
Parkinson's disease (PD) is characterized by the progressive loss of dopaminergic neurons in the substantia nigra. However, other non-dopaminergic neuronal systems such as the serotonergic system are also involved. Serotonergic dysfunction is associated with non-motor symptoms and complications, including anxiety, depression, dementia, and sleep disturbances. This pathology reduces patient quality of life. Interaction between the serotonergic and other neurotransmitters systems such as dopamine, noradrenaline, glutamate, and GABA controls the activity of striatal neurons and are particularly interesting for understanding the pathophysiology of PD. Moreover, serotonergic dysfunction also causes motor symptoms. Interestingly, serotonergic neurons play an important role in the effects of L-DOPA in advanced PD stages. Serotonergic terminals can convert L-DOPA to dopamine, which mediates dopamine release as a "false" transmitter. The lack of any autoregulatory feedback control in serotonergic neurons to regulate L-DOPA-derived dopamine release contributes to the appearance of L-DOPA-induced dyskinesia (LID). This mechanism may also be involved in the development of graft-induced dyskinesias (GID), possibly due to the inclusion of serotonin neurons in the grafted tissue. Consistent with this, the administration of serotonergic agonists suppressed LID. In this review article, we summarize the interactions between the serotonergic and other systems. We also discuss the role of the serotonergic system in LID and if therapeutic approaches specifically targeting this system may constitute an effective strategy in PD.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".