Dopamine dysregulation syndrome: implications for a dopamine hypothesis of bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: Rational therapeutic development in bipolar is hampered by a lack of pathophysiological model. However, there is a wealth of converging data on the role of dopamine in bipolar disorder. This paper therefore examines the possibility of a dopamine hypothesis for bipolar disorder. METHOD: A literature search was conducted using standard search engines Embase, PyschLIT, PubMed and MEDLINE. In addition, papers and book chapters known to the authors were retrieved and examined for further relevant articles. RESULTS: Collectively, in excess of 100 articles were reviewed from which approximately 75% were relevant to the focus of this paper. CONCLUSION: Pharmacological models suggest a role of increased dopaminergic drive in mania and the converse in depression. In Parkinson's disease, administration of high-dose dopamine precursors can produce a 'maniform' picture, which switches into a depressive analogue on withdrawal. It is possible that in bipolar disorder there is a cyclical process, where increased dopaminergic transmission in mania leads to a secondary down regulation of dopaminergic receptor sensitivity over time. This may lead to a period of decreased dopaminergic transmission, corresponding with the depressive phase, and the repetition of the cycle. This model, if verified, may have implications for rational drug development.
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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 | 0.000 |
| 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) | 0.003 | 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".