Background: Brain dynamics abnormalities in the triple-network, which involves the salience network (SN), the default mode network (DMN) and the central executive network (CEN), have been reported in schizophrenia. However, it remains to be clarified how antipsychotics affect dynamic functional connectivity (DFC) within the triple-network and whether differences in clinical outcomes are associated with varying levels of network model dysfunction.
Methods: Resting-state functional magnetic resonance imaging scans were obtained from 64 first-episode schizophrenia patients (SZ) and 67 healthy controls (HC). All patients were scanned before and after 12-week antipsychotic treatment and the HC were scanned only at baseline.
Results: At baseline, SZ participants showed significantly reduced dynamic functional interactions across the triple-network compared to HC. The SZ group displayed a pattern of reduction in resting-state DFC among the triple-network compared with HC. After medication, the mean dynamic network interaction index (dNII) value was improved. A significant quadratic relation was observed between longitudinal change of mean dNII and the reduction ratio of PANSS total score within the SZ group. The DFC within inter-network (between DMN and SN, and between DMN and CEN) and intra-network connections of DMN were significantly higher relative to baseline. Intra-SN DFC, intra-DMN DFC and DFC between SN and DMN were found to be predictive of clinical features at baseline. Intra-CEN DFC and DFC between DMN and CEN were predictive of treatment response.
Conclusions: Aberrant brain dynamics in the triple-network could be regulated with medication. DFC organization in the triple network was found to predict the clinical outcome.
Keywords: Dynamics; Machine learning; Resting-state functional magnetic resonance imaging; Schizophrenia; Triple-network.
Copyright © 2021. Published by Elsevier B.V.