Structural Brain Correlates of Cognitive Function in Schizophrenia: A Meta-Analysis
Bibliographic record
Abstract
Abstract Schizophrenia is characterized by cognitive impairments and widespread structural brain alterations (e.g., decreased volume, thickness, surface area). Brain structure-cognition associations have been extensively studied in schizophrenia, typically involving individual cognitive domains or brain regions of interest. Findings in overlapping and diffuse brain regions may point to structural alterations in large-scale brain networks. We performed a systematic review and meta-analysis examining whether brain structure-cognition associations can be explained in terms of biologically meaningful brain networks. Of 7,621 screened articles, 88 were included in a series of meta-analyses assessing publication bias, heterogeneity, and study quality. Significant associations were found between overall brain structure and eight cognitive domains (speed of processing, attention/vigilance, working/verbal/visual memory, executive function, social cognition, and verbal fluency). Brain structure within functionally defined networks (default, dorsal/ventral attention, frontoparietal, limbic, somatosensory, visual) and external structures (amygdala, hippocampus and cerebellum) typically showed associations with conceptually related cognitive domains, with higher-level domains (e.g., executive function, social cognition) associated with more networks. These findings suggest brain structure- cognition associations in schizophrenia may follow network architecture.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".