Individuals Diagnosed with Schizophrenia Assign Emotional Importance to Neutral Stimuli: An fMRI Study
Bibliographic record
Abstract
The majority of functional neuroimaging studies investigating neural correlates of emotion processing in schizophrenia report a significant deficit in limbic structures activation in patients relative to control participants. Recently it has been suggested that this apparent "deficit" could be due to an enhanced sensitivity of the neutral material in individuals diagnosed with schizophrenia, rather than due to their inefficiency in emotion processing. The purpose of the present study was to test this supposition and verify if the potential effect is present in both men and women diagnosed with schizophrenia. In order to do that we examined the pattern of cerebral activation associated with processing of neutral stimuli in schizophrenia. Thirty-seven schizophrenia patients and 37 healthy controls viewed neutral and emotional images while in a functional magnetic resonance imaging scanner. Schizophrenia patients rated the neutral images as more emotionally salient than controls. Additionally, patients showed significant activation during processing of neutral images in limbic and prefrontal regions; similar areas were underactivated in patients relative to controls during processing of emotional information. Investigation of sex differences revealed that the enhanced responsiveness to the emotionally neutral material was attributed primarily to men with schizophrenia.
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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 | 0.001 | 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".