Processing of social emotion in patients with schizophrenia and substance use disorder: An fMRI study
Bibliographic record
Abstract
The lifetime prevalence of substance use disorders among schizophrenia patients is close to 50%. The negative consequences of substance abuse in schizophrenia are well documented, but the aetiology of this comorbid condition remains unknown. Mounting evidence suggests that dual-diagnosis patients have fewer negative symptoms and better social skills, compared to non-abusing patients. We hypothesized that schizophrenia patients with substance use disorder (SCZ-SUD) would display increased cerebral activations in response to socioemotional stimuli, relative to patients with no SUD (SCZ). Schizophrenia patients (DSM-IV criteria) were divided into two groups: patients with (n=12) and without (n=11) substance use (alcohol and/or cannabis). Using functional magnetic resonance imaging (fMRI), patients were scanned during passive viewing of an emotional film excerpt with social content. Loci of activation were identified in the right mPFC (BA 10) and the right supramarginal gyrus (BA 40) in SCZ-SUD patients, and in the left pons in SCZ patients. Relative to SCZ patients, increased loci of activation were found in the right superior parietal cortex (BA 7) and the left medial prefrontal cortex (BA 10) in SCZ-SUD patients, who reported higher subjective emotional experience on a self-report scale. To our knowledge, this is the first fMRI study to assess social emotions in dual-diagnosis schizophrenia. Our results suggest that socioemotional processing may be less impaired in dual diagnosis, which recruited brain regions seemingly involved in "social cognition." Further studies on the topic are warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".