Voxel-wise meta-analysis of fMRI studies in patients at clinical high risk for psychosis
Bibliographic record
Abstract
BACKGROUND: Reliable neurofunctional markers of increased vulnerability to psychosis are needed to improve the predictive value of psychosis risk syndrome and inform preventive interventions. METHODS: I performed a signed differential mapping (SDM) voxel-wise meta-analysis of functional magnetic resonance imaging (fMRI) studies of patients at clinical high risk for psychosis. RESULTS: Ten studies were included in the analysis. Compared with controls, high-risk patients showed reduced neural activation in the left inferior frontal gyrus (Brodmann area [BA] 9) and in a cluster spanning the bilateral medial frontal gyrus (BA 8,6), bilateral superior frontal gyrus (BA 8,6)and the left anterior cingulate (BA 32). There was no publication bias. Heterogeneity across studies was low. Sensitivity analysis confirmed the robustness of the findings. LIMITATIONS: The cross-sectional nature of the included studies prevented the comparison of high-risk patients who later experienced a psychotic episode with those who did not. Other caveats are reflected in methodologic heterogeneity across tasks employed by different individual imaging studies. CONCLUSION: Reduced neurofunctional activation in prefrontal regions may represent a neurophysiologic correlate of increased vulnerability to psychosis.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".