Dysfunctional insular connectivity during reward prediction in patients with first-episode psychosis
Bibliographic record
Abstract
Background: Increasing evidence indicates that psychosis is associated with abnormal reward processing. Imaging studies in patients with first-episode psychosis (FEP) have revealed reduced activity in diverse brain regions, including the ventral striatum, insula and anterior cingulate cortex (ACC), during reward prediction. However, whether these reductions in local brain activity are due to altered connectivity has rarely been explored. Methods: We applied dynamic causal modelling and Bayesian model selection to fMRI data during the Salience Attribution Task to investigate whether patients with FEP showed abnormal modulation of connectivity between the ventral striatum, insula and ACC induced by rewarding cues and whether these changes were related to positive psychotic symptoms and atypical antipsychotic medication. Results: The model including reward-induced modulation of insula–ACC connectivity was the best fitting model in each group. Compared with healthy controls ( n = 19), patients with FEP ( n = 29) revealed reduced right insula–ACC connectivity. After subdividing patients according to current antipsychotic medication, we found that the reduced insula–ACC connectivity relative to healthy controls was observed only in untreated patients ( n = 17), not in patients treated with antipsychotics ( n = 12), and that it correlated negatively with unusual thought content in untreated patients with FEP. Limitations: The modest sample size of untreated patients with FEP was a limitation of our study. Conclusion: This study indicates that insula–ACC connectivity during reward prediction is reduced in untreated patients with FEP and related to the formation of positive psychotic symptoms. Our study further suggests that atypical antipsychotics may reverse connectivity between the insula and the ACC during reward prediction.
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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".