Symptom networks and working memory in schizophrenia: a multi-methodological cross-sectional study from phenotype to endophenotype
Bibliographic record
Abstract
Background: A notable deficit in working memory (WM) is well established in schizophrenia. Nevertheless, the intricate relationship between various symptoms and WM impairment is still not fully understood. We use three distinct methodologies-symptom network analysis (SNA), Connectome-Based Predictive Modeling (CPM), and brain gene annotation enrichment analysis-to explore the connectome patterns that link WM deficits and symptoms, and their related gene expression. Methods: 255 patients with schizophrenia were recruited as two distinct samples. SNA was used to pinpoint the core psychiatric symptoms influenced by WM performance. CPM identified the subnetwork of the functional connectome that was recruited under the 2-back load of the N-back WM task, and predicted the severity of the SNA-based key symptoms. Gene annotation enrichment analysis explored the likely molecular biological processes underlying the symptom-predictive functional WM network. Results: = 0.014). The predictive network primarily involved the frontoparietal and frontolimbic networks. Gene enrichment analysis revealed a preferential role for cytoplasmic protein binding, indicating a potential molecular basis for the WM-related, symptom-predictive functional connectivity. Conclusions: Impaired WM performance in schizophrenia relates to frontoparietal and frontolimbic connectivity and preferentially influences the severity of disorganized attention, a clinically observable phenomenon. The potential role of cytoplasmic protein binding in WM deficits and attentional disorganization in schizophrenia warrants further investigation.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".