Disrupted static and dynamic small-world brain network topologies in patients with schizophrenia.

Background: Schizophrenia is a common and severe mental disorder, but its pathogenesis remains unclear. This study aimed to explore the underlying neuropathological mechanisms of schizophrenia by simultaneously examining changes in the static and dynamic small-world brain network properties.

Methods: In this study, 82 patients with schizophrenia and 76 age- and sex-matched healthy controls were recruited. Both groups underwent resting-state functional magnetic resonance imaging scans. Measurements of both static and dynamic brain network topologies were calculated and compared between groups at the level of the whole brain, nodes and edges.

Results: At the whole-brain level, patients with schizophrenia showed significant alterations in both the static (decreased clustering coefficient and characteristic path length; increased global efficiency) and dynamic (decreased characteristic temporal path length) network metrics than healthy controls. At the nodal level, patients with schizophrenia displayed reduced nodal clustering coefficient and nodal efficiency within the somatomotor and dorsal attention networks, as well as reduced nodal temporal clustering coefficient within the somatomotor regions. Regarding edge-level changes in schizophrenia, lower static functional connectivities were detected mainly within the somatomotor network/between the somatomotor and other networks, while lower stabilities of dynamic functional connectivity were found to involve almost all brain networks.

Discussion: Our results suggest a significant trend toward more randomization in schizophrenia in the framework of both static and dynamic brain network models, which can provide deeper insights into schizophrenia's neuropathology. Our findings might also highlight the importance of dynamic brain network analysis since it offered valuable information not captured by static brain networks.