Neural Correlates of Sad Feelings in Schizophrenia with and without Blunted Affect
Bibliographic record
Abstract
OBJECTIVE: There have been reports that patients with schizophrenia have decreased activity in the prefrontal cortex during emotion processing. However, findings have been confounded by sample nonspecificity and explicit cognitive task interference with emotion processing. We aimed to further investigate this by examining the ventrolateral prefrontal cortex (VLPFC) activation in response to the passive viewing of sad film excerpts. METHODS: We presented film excerpts depicting sad and neutral social situations to 25 schizophrenia patients (14 with blunted affect [BA+] and 11 without blunted affect [BA-]) in an implicit perception task to evoke prefronto-limbic activity illustrated by blood oxygenation level-dependent functional magnetic resonance imaging. RESULTS: A random-effects analysis (2-sample t test) using statistical parametric mapping indicated that BA+ patients differed from BA- patients at a 0.05 level (P corrected for multiple comparisons). Consistent with our a priori hypothesis, BA- patients (relative to BA+ patients) showed significant activation in the right VLPFC. An exploratory analysis revealed the following loci of activation: caudate nucleus, VLPFC, middle prefrontal cortex, medial prefrontal cortex, anterior cingulate cortex, and anterior temporal pole in the BA- group; and hippocampus, cerebellum, anterior temporal pole, and midbrain in the BA+ group. CONCLUSIONS: We observed not only hypofrontality in the BA+ group but also dysfunctional circuitry distributed throughout the brain. The temporal and midbrain activation seen in the BA+ group may indicate that these brain regions were working harder to compensate for inactivation in other regions. These distributed dysfunctional circuits may form the neural basis of blunted affect through impairment of emotion processing in the brain that prevents it from processing input efficiently and producing output effectively, thereby leading to symptoms such as blunted affect.
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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) | 0.002 | 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".