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

Abstract

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.

Aberrant thalamocortical connectivity and shifts between the resting state and task state in patients with schizophrenia

“…Fornito et al, 2012; Minzenberg et al, 2009; Wheeler & Voineskos, 2014) have led to the hypothesis that SZ is a brain ‘dysconnection’ syndrome (Friston et al, 2016; Stephan et al, 2009). Therefore, there is an increasing need to investigate the pathophysiology of SZ from a network or connectome perspective (Damiani et al, 2022; Gao et al, 2023; Kitajima et al, 2023; Tagawa et al, 2022). Clinically, SZ is a debilitating psychotic disorder characterized by a heterogeneous array of symptoms, such as auditory hallucinations, delusions, disorganized thoughts, ego disturbances, passivity phenomena and cognitive deficits (Onitsuka, Hirano, Nakazawa, et al, 2022).…”

Functional Network Characteristics in Adolescent Psychotic Mood Disorder: Associations with Symptom Severity and Treatment Effects

“…Moreover, other researchers have also revealed anomalies in the topological properties of functional networks in patients with mood disorders(18-20). Furthermore, there is evidence indicating a correlation between psychotic symptoms and changes in topological properties in patients with schizophrenia (21,22). The above ndings suggest that topological properties may re ect differences in neural network patterns between different diseases.…”

Abnormal phase entrainment of low- and high-gamma-band auditory steady-state responses in schizophrenia

“…Due to its remarkable temporal acuity and exceptional spatial resolution, magnetoencephalography (MEG) demonstrates remarkable proficiency in elucidating intricate neural dynamics ( Hironaga et al, 2020 ; Tagawa et al, 2022 ). Several previous studies using MEG attempted to identify the brain regions responsible for gamma-band ASSR deficits in SZ ( Hamm et al, 2011 ; Edgar et al, 2014 , 2018 ; Murphy et al, 2020 ; Grent-‘t-Jong et al, 2021 ).…”