Serotonin in mania and in the mechanism of action of mood stabilizers: a review of clinical studies
Bibliographic record
Abstract
OBJECTIVES: Serotonin (5-hydroxytryptamine, 5-HT) was implicated in the pathophysiology of manic-depressive illness as early as 1958. Although extensive evidence has accumulated since then to support 5-HT's role in depression, relatively fewer studies examined its role in mania. The purpose of this paper was to review and summarize the current state of knowledge on the role of 5-HT in mania and its treatment. METHODS: We systemically reviewed clinical studies of 1) 5-HT function in mania and 2) 5-HT in the mechanism of action of mood stabilizers, including lithium and anticonvulsants. RESULTS: Review showed that cerebrospinal fluid, postmortem, platelet, neuroendocrine challenge, and tryptophan depletion studies provided some evidence to support the hypothesis that a 5-HT deficit is involved in mania and that enhancement of 5-HT neurotransmission exerts a mood-stabilizing effect. CONCLUSIONS: There is some evidence from clinical studies for the contribution of 5-HT in mania and in the mechanism of action of mood stabilizers. However, it is very likely that other neurotransmitters also play important roles. Future directions for research include 1) in vivo study of 5-HT receptor subtypes using positron emission tomography, 2) investigation of the interaction between 5-HT and other neurotransmitter systems, and 3) determination of the relationships between diagnostic subtypes of mania and 5-HT function and other neurotransmitter 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".