Disrupted Prefrontal Interhemispheric Structural Coupling in Schizophrenia Related to Working Memory Performance
Bibliographic record
Abstract
BACKGROUND: Prominent regional cortical thickness reductions have been shown in schizophrenia. In contrast, little is known regarding alterations of structural coupling between regions in schizophrenia and how these alterations may be related to cognitive impairments in this disorder. METHODS: T1-weighted magnetic resonance images were acquired in 54 patients with schizophrenia and 68 healthy control subjects aged 18-55 years. Cortical thickness was compared between groups using a vertex-wise approach. To assess structural coupling, seeds were selected within regions of reduced thickness, and brain-wide cortical thickness correlations were compared between groups. The relationships between identified patterns of circuit structure disruption and cognitive task performance were then explored. RESULTS: Prominent cortical thickness reductions were found in patients compared with controls at a 5% false discovery rate in a predominantly frontal and temporal pattern. Correlations of the left dorsolateral prefrontal cortex (DLPFC) with right prefrontal regions were significantly different in patients and controls. The difference remained significant in a subset of 20 first-episode patients. Participants with stronger frontal interhemispheric thickness correlations had poorer working memory performance. CONCLUSIONS: We identified structural impairment in a left-right DLPFC circuit in patients with schizophrenia independent of illness stage or medication exposure. The relationship between left-right DLPFC thickness correlations and working memory performance implicates prefrontal interhemispheric circuit impairment as a vulnerability pathway for poor working memory performance. Our findings could guide the development of novel therapeutic interventions aimed at improving working memory performance in patients with schizophrenia.
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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) | 0.001 | 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".