Abnormal cortico-limbic connectivity during emotional processing correlates with symptom severity in schizophrenia
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Impaired emotional processing is a core feature of schizophrenia (SZ). Consistent findings suggested that abnormal emotional processing in SZ could be paralleled by a disrupted functional and structural integrity within the fronto-limbic circuitry. The effective connectivity of emotional circuitry in SZ has never been explored in terms of causal relationship between brain regions. We used functional magnetic resonance imaging and Dynamic Causal Modeling (DCM) to characterize effective connectivity during implicit processing of affective stimuli in SZ. METHODS: We performed DCM to model connectivity between amygdala (Amy), dorsolateral prefrontal cortex (DLPFC), ventral prefrontal cortex (VPFC), fusiform gyrus (FG) and visual cortex (VC) in 25 patients with SZ and 29 HC. Bayesian Model Selection and average were performed to determine the optimal structural model and its parameters. RESULTS: Analyses revealed that patients with SZ are characterized by a significant reduced top-down endogenous connectivity from DLPFC to Amy, an increased connectivity from Amy to VPFC and a decreased driving input to Amy of affective stimuli compared to HC. Furthermore, DLPFC to Amy connection in patients significantly influenced the severity of psychopathology as rated on Positive and Negative Syndrome Scale. CONCLUSIONS: Results suggest a functional disconnection in brain network that contributes to the symptomatic outcome of the disorder. Our findings support the study of effective connectivity within cortico-limbic structures as a marker of severity and treatment efficacy in SZ.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it