Abnormal effective connectivity and psychopathological symptoms in the psychosis high-risk state
Bibliographic record
Abstract
BACKGROUND: Recent evidence has revealed abnormal functional connectivity between the frontal and parietal brain regions during working memory processing in patients with schizophrenia and first-episode psychosis. However, it still remains unclear whether abnormal frontoparietal connectivity during working memory processing is already evident in the psychosis high-risk state and whether the connection strengths are related to psychopathological outcomes. METHODS: Healthy controls and antipsychotic-naive individuals with an at-risk mental state (ARMS) performed an n-back working memory task while undergoing functional magnetic resonance imaging. Effective connectivity between frontal and parietal brain regions during working memory processing were characterized using dynamic causal modelling. RESULTS: Our study included 19 controls and 27 individuals with an ARMS. In individuals with an ARMS, we found significantly lower task performances and reduced activity in the right superior parietal lobule and middle frontal gyrus than in controls. Furthermore, the working memory-induced modulation of the connectivity from the right middle frontal gyrus to the right superior parietal lobule was significantly reduced in individuals with an ARMS, while the extent of this connectivity was negatively related to the Brief Psychiatric Rating Scale total score. LIMITATIONS: The modest sample size precludes a meaningful subgroup analysis for participants with a later transition to psychosis. CONCLUSION: This study demonstrates that abnormal frontoparietal connectivity during working memory processing is already evident in individuals with an ARMS and is related to psychiatric symptoms. Thus, our results provide further insight into the pathophysiological mechanisms of the psychosis high-risk state by linking functional brain imaging, computational modelling and psychopathology.
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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 | 0.000 | 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".