The Psychopharmacology of Aggressive Behavior
Bibliographic record
Abstract
Patients with mental disorders are at an elevated risk for developing aggressive behavior. In the last 19 years, the psychopharmacological treatment of aggression has changed dramatically because of the introduction of atypical antipsychotics into the market and the increased use of anticonvulsants and lithium in the treatment of aggressive patients.Using a translational medicine approach, this review (part 1 of 2) examines the neurobiology of aggression, discussing the major neurotransmitter systems implicated in its pathogenesis, namely, serotonin, glutamate, norepinephrine, dopamine, and γ-aminobutyric acid, and also their respective receptors. The preclinical and clinical pharmacological studies concerning the role of these neurotransmitters have been reviewed, as well as research using transgenic animal models. The complex interaction among these neurotransmitters occurs at the level of brain areas and neural circuits such as the orbitoprefrontal cortex, anterior cortex, amygdala, hippocampus, periaqueductal gray, and septal nuclei, where the receptors of these neurotransmitters are expressed. The neurobiological mechanism of aggression is important to understand the rationale for using atypical antipsychotics, anticonvulsants, and lithium in treating aggressive behavior. Further research is necessary to establish how these neurotransmitter systems interact with brain circuits to control aggressive behavior at the intracellular level.
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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 | 0.001 |
| 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".