The role of dopamine in bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: Despite effective pharmacological treatments for bipolar disorder, we still lack a comprehensive pathophysiological model of the illness. Recent neurobiological research has implicated a number of key brain regions and neuronal components in the behavioural and cognitive manifestations of bipolar disorder. Dopamine has previously been investigated in some depth in bipolar disorder, but of late has not been a primary focus of attention. This article examines the role of dopamine in bipolar disorder, incorporating recent advances into established models where possible. METHODS: A critical evaluation of the literature was undertaken, including a review of behavioural, neurochemical, receptor, and imaging studies, as well as genetic studies focusing on dopamine receptors and related metabolic pathways. In addition, pharmacologic manipulation of the central dopaminergic pathways and comparisons with other disease states such as schizophrenia were considered, principally as a means of exploring the hypothesised models. RESULTS: Multiple lines of evidence, including data from pharmacological interventions and structural and functional magnetic resonance imaging studies, suggest that the dopaminergic system may play a central role in bipolar disorder. CONCLUSION: Future research into the pathophysiological mechanisms of bipolar disorder and the development of new treatments for bipolar disorder should focus on the dopaminergic system.
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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 | 0.002 |
| 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) | 0.003 | 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".