Task-Related Functional Connectivity Analysis of Emotion Discrimination in a Family Study of Schizophrenia
Bibliographic record
Abstract
Poor emotion recognition is a core deficit in schizophrenia and is associated with poor functional outcome. Functional magnetic resonance imaging (fMRI) multivariate analysis methods were used to elucidate the neural underpinnings of face and emotion processing associated with both genetic liability and disease-specific effects. Schizophrenia patients, relatives, and controls completed a task that included 4 facial emotion discrimination conditions and an age discrimination condition during fMRI. Three functional networks were derived from the data: the first involved in visual attention and response generation, the second a default mode network (DMN), and a third involved in face and emotion processing. No differences in activation were found between groups for the visual attention and response generation network, suggesting that basic processes were intact. Both schizophrenia patients and relatives showed evidence for hyperdeactivation in the DMN compared to controls, with relatives being intermediate, suggesting a genetic liability effect. Both disease-specific and genetic liability effects were found for the face processing network, which included the amygdala. Patients exhibited lower coordinated network activity compared to controls and relatives across all facial discrimination conditions. Additionally, in relation to the other emotion discrimination conditions, a heightened coordinated response during fear and anger discrimination was observed in schizophrenia compared to other conditions, whereas relatives demonstrated heightened coordinated activity for anger discrimination only relative to other emotion conditions. With regards to brain functioning, this study found that schizophrenia is associated with abnormal processing of threat-related information, and that in part may be associated with the genetic risk for the disorder, suggesting that the facial and emotion processing network could be targeted for intervention.
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How this classification was reachedexpand
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".