Corticolimbic Structural Deficits in Violent Patients with Schizophrenia
Bibliographic record
Abstract
Background/Objectives: Violent behaviors are uncommon in patients with schizophrenia (Sch), but when present, exacerbate stigma and challenge treatment. The following study aimed to identify the structural abnormalities associated with violent behaviors in Sch by implementing a validated tool specifically designed to evaluate violent behaviors in psychiatric populations, as well as by performing region-of-interest neuroimaging analyses, focused on areas commonly associated with the neurobiology of violence and aggression. Methods: Eighty-three participants were divided into three groups: Sch with violent behaviors (Sch+V, n = 34), Sch without violent behaviors (Sch-V, n = 28), and healthy controls (HC, n = 21). Structural neuroimaging analyses were performed across groups to assess gray matter volume (GMV) and cortical thickness (CT) differences in regions previously implicated in aggressive behaviors. Results: The data revealed significant reductions in GMV in the right amygdala and diminished cortical thickness (CT) in the bilateral dorsolateral prefrontal cortices (dlPFC) in patients with Sch+V compared to patients with Sch-V and HCs. Right amygdalar volume also demonstrated a negative correlational trend with hostility scores in patients with Sch+V. Conclusions: These findings underscore disruptions in the structural integrity of the dlPFC—responsible for inhibitory control—and the amygdala—central to emotional processing in violent patients with Sch. Future research should aim to investigate potential functional interactions at a network level to gain a deeper understanding of the neurobiological underpinnings of violent behaviors in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".