Disrupted local beta band networks in schizophrenia revealed through graph analysis: A magnetoencephalography study

Aims Schizophrenia (SZ) is characterized by psychotic symptoms and cognitive impairment, and is hypothesized to be a 'dysconnection' syndrome due to abnormal neural network formation. Although numerous studies have helped elucidate the pathophysiology of SZ, many aspects of the mechanism underlying psychotic symptoms remain unknown. This study used graph theory analysis to evaluate the characteristics of the resting-state network (RSN) in terms of microscale and macroscale indices, and to identify candidates as potential biomarkers of SZ. Specifically, we discriminated topological characteristics in the frequency domain and investigated them in the context of psychotic symptoms in patients with SZ. Methods We performed graph theory analysis of electrophysiological RSN data using magnetoencephalography to compare topological characteristics represented by microscale (degree centrality and clustering coefficient) and macroscale (global efficiency, local efficiency, and small-worldness) indices in 29 patients with SZ and 38 healthy controls. In addition, we investigated the aberrant topological characteristics of the RSN in patients with SZ and their relationship with SZ symptoms. Results SZ was associated with a decreased clustering coefficient, local efficiency, and small-worldness, especially in the high beta band. In addition, macroscale changes in the low beta band are closely associated with negative symptoms. Conclusions The local networks of patients with SZ may disintegrate at both the microscale and macroscale levels, mainly in the beta band. Adopting an electrophysiological perspective of SZ as a failure to form local networks in the beta band will provide deeper insights into the pathophysiology of SZ as a 'dysconnection' syndrome.

Automated summary

This study used graph theory analysis to evaluate the characteristics of the resting-state network (RSN) in patients with schizophrenia (SZ) and identified potential biomarkers of SZ. The study found that the local networks of SZ patients may disintegrate at both the microscale and macroscale levels, especially in the beta band. This study provides deeper insights into the pathophysiology of SZ as a 'dysconnection' syndrome.