The Psychopharmacology of Aggressive Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it