Schizophr Bull 2016 11 28;42(6):1446-1456. Epub 2016 Jun 28.
Department of Psychosis Studies, Institute of Psychiatry, King's College London, London, UK.
Background: Converging evidence suggests that patients with first-episode psychosis who show a poor treatment response may have a higher degree of neurodevelopmental abnormalities than good Responders. Characterizing the disturbances in the relationship among brain regions (covariance) can provide more information on neurodevelopmental integrity than searching for localized changes in the brain. Graph-based connectomic approach can measure structural covariance thus providing information on the maturational processes. We quantified the structural covariance of cortical folding using graph theory in first-episode psychosis, to investigate if this systems-level approach would improve our understanding of the biological determinants of outcome in psychosis.
Methods: Magnetic Resonance Imaging data were acquired in 80 first-episode psychosis patients and 46 healthy controls. Response to treatment was assessed after 12 weeks of naturalistic follow-up. Gyrification-based connectomes were constructed to study the maturational organization of cortical folding.
Results: Nonresponders showed a reduction in the distributed relationship among brain regions (high segregation, poor integration) when compared to Responders and controls, indicating a higher burden of aberrant neurodevelopment. They also showed reduced centrality of key regions (left insula and anterior cingulate cortex) indicating a marked reconfiguration of gyrification. Nonresponders showed a vulnerable pattern of covariance that disintegrated when simulated lesions removed high-degree hubs, indicating an abnormal dependence on highly central hub regions in Nonresponders.
Conclusions: These findings suggest that a perturbed maturational relationship among brain regions underlies poor treatment response in first-episode psychosis. The information obtained from gyrification-based connectomes can be harnessed for prospectively predicting treatment response and prognosis in psychosis.