Reduced neural activity of the prefrontal cognitive control circuitry during response inhibition to negative words in people with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is characterized by deficits in executive control and impairments in emotion processing. This study assessed the nature and extent of potential alterations in the neural substrates supporting the interaction between cognitive control mechanisms and emotion attribution processes in people with schizophrenia. METHODS: Functional magnetic resonance imaging was performed during a verbal emotional go/no-go task. People with schizophrenia and healthy controls responded to word stimuli of a prespecified emotional valence (positive, negative or neutral) while inhibiting responses to stimuli of a different valence. RESULTS: We enrolled 20 people with schizophrenia and 23 controls in the study. Healthy controls activated an extensive dorsal prefrontal-parietal network while inhibiting responses to negative words compared to neutral words, but showed deactivation of the midcingulate cortex while inhibiting responses to positive words compared to neutral words. People with schizophrenia failed to activate this network during response inhibition to negative words, whereas during response inhibition to positive words they did not deactivate the cingulate, but showed increased responsivity in the frontal cortex. LIMITATIONS: Sample heterogeneity is characteristic of studies of schizophrenia and may have contributed to more variable neural responses in the patient sample despite the care taken to control for potentially confounding variables. CONCLUSION: Our results showed that schizophrenia is associated with aberrant modulation of neural responses during the interaction between cognitive control and emotion processing. Failure of the frontal circuitry to regulate goal-directed behaviour based on emotion attributions may contribute to deficits in psychosocial functioning in daily life.
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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 | 0.000 | 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".