Altered Brain Network Dynamics in Schizophrenia: A Cognitive Electroencephalography Study

Abstract Background Alterations in the dynamic coordination of widespread brain networks are proposed to underlie cognitive symptoms of schizophrenia. However, there is limited understanding of the temporal evolution of these networks and how they relate to cognitive impairment. The current study was designed to explore dynamic patterns of network connectivity underlying cognitive features of schizophrenia. Methods In total, 21 inpatients with schizophrenia and 28 healthy control participants completed a cognitive task while electroencephalography data were simultaneously acquired. For each participant, Pearson cross-correlation was applied to electroencephalography data to construct correlation matrices that represent the static network (averaged over 1200 ms) and dynamic network (1200 ms divided into four windows of 300 ms) in response to cognitive stimuli. Global and regional network measures were extracted for comparison between groups. Results Dynamic network analysis identified increased global efficiency; decreased clustering (globally and locally); reduced strength (weighted connectivity) around the frontal, parietal, and sensory-motor areas; and increased strength around the occipital lobes (a peripheral hub) in patients with schizophrenia. Regional network measures also correlated with clinical features of schizophrenia. Network differences were prominent 900 ms following the cognitive stimuli before returning to levels comparable to those of healthy control participants. Conclusions Patients with schizophrenia exhibited altered dynamic patterns of network connectivity across both global and regional measures. These network differences were time sensitive and may reflect abnormalities in the flexibility of the network that underlies aspects of cognitive function. Further research into network dynamics is critical to better understanding cognitive features of schizophrenia and identification of network biomarkers to improve diagnosis and treatment models.