ArticleBMC psychiatry2026
From dysconnectivity to symptoms: large-scale resting-state networks relate to psychotic phenomenology in schizophrenia.
Article in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundSchizophrenia is increasingly conceptualized as a disorder of large-scale brain network integration, yet how specific symptom phenotypes relate to reproducible resting-state functional connectivity (rsFC) signatures remains unclear.
methodsResting-state fMRI data from 386 patients with schizophrenia and 212 healthy controls were analyzed to characterize large-scale functional connectivity patterns. Group-level connectivity differences were first identified (uncorrected P < 0.05, for exploratory feature selection), followed by within-patient analyses examining associations between altered connectivity and symptom subitems while adjusting for demographic factors. Multivariate models were then used to evaluate whether connectivity patterns showed systematic associations with individual symptom profiles.
resultsGroup comparisons revealed a dysconnectivity pattern characterized by reduced cross-network coupling between the visual network and higher-order systems, alongside selective increases in frontoparietal circuits. Within patients, connectivity alterations showed distinct association patterns across delusion, hallucination, and negative-symptom subitems. Multivariate analyses further indicated modest associations with several hallucination (e.g., H8, R²≈0.09) and delusion (e.g., D3, R²≈0.07) subitems, whereas associations with negative symptoms were minimal and showed limited generalization.
conclusionsThese findings support a hierarchical dysconnectivity profile centered on impaired perceptual-cognitive integration and suggest that specific positive-symptom phenotypes may be associated with partially consistent rsFC signatures. Overall effect sizes were modest, indicating that rsFC captures only a limited component of symptom variability. CLINICAL TRIAL NUMBER: Not applicable.
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