Reduced activation in the ventral striatum during probabilistic decision-making in patients in an at-risk mental state
Bibliographic record
Abstract
BACKGROUND: Patients with schizophrenia display metacognitive impairments, such as hasty decision-making during probabilistic reasoning - the "jumping to conclusion" bias (JTC). Our recent fMRI study revealed reduced activations in the right ventral striatum (VS) and the ventral tegmental area (VTA) to be associated with decision-making in patients with schizophrenia. It is unclear whether these functional alterations occur in the at-risk mental state (ARMS). METHODS: We administered the classical beads task and fMRI among ARMS patients and healthy controls matched for age, sex, education and premorbid verbal intelligence. None of the ARMS patients was treated with antipsychotics. Both tasks request probabilistic decisions after a variable amount of stimuli. We evaluated activation during decision-making under certainty versus uncertainty and the process of final decision-making. RESULTS: We included 24 AMRS patients and 24 controls in our study. Compared with controls, ARMS patients tended to draw fewer beads and showed significantly more JTC bias in the classical beads task, mirroring findings in patients with schizophrenia. During fMRI, ARMS patients did not demonstrate JTC bias on the behavioural level, but showed a significant hypoactivation in the right VS during the decision stage. LIMITATIONS: Owing to the cross-sectional design of the study, results are constrained to a better insight into the neurobiology of risk constellations, but not prepsychotic stages. Nine of the ARMS patients were treated with antidepressants and/or lorazepam. CONCLUSION: As in patients with schizophrenia, a striatal hypoactivation was found in ARMS patients. Confounding effects of antipsychotic medication can be excluded. Our findings indicate that error prediction signalling and reward anticipation may be linked to striatal dysfunction during prodromal stages and should be examined for their utility in predicting transition risk.
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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".