Recurrence network analysis of schizophrenia MEG under different stimulation states. (February 2023) - Bai, Dengxuan; Yao, Wenpo; Wang, Shuwang; Yan, Wei; Wang, Jun
Abstract: The goal of this study was to investigate nonlinear dynamics in the magnetoencephalogram (MEG) signals of schizophrenia. In this study, the geometric characteristics of MEG signals of schizophrenia were studied in phase space with recurrence network analysis (RNA) under different stimulation states. First, during negative stimulation, the MEG signals of schizophrenia had a higher global recurrence rate, higher invariant objective trajectory and higher phase space separability in the alpha 1 band (8–10 Hz). However, the MEG signals had a lower global recurrence rate and a higher invariant objective trajectory in the beta band (13–30 Hz). Moreover, during positive stimulation and gray cross stimulation, the global recurrence rate and invariant objective trajectory of MEG signals in the alpha 1 band (8–10 Hz) were significantly higher in schizophrenia patients than in controls; and the phase space separability was significantly higher in schizophrenia patients than in controls. Overall, recurrence network parameters enable identification of specific properties of schizophrenia MEG. The abnormal information in the MEG signals of schizophrenia identified with the RNA method has the potential for the development of subbiomarkers for diagnosing schizophrenia. Highlights: Recurrence network is used to analyze the MEG of schizophrenia in different stimulation states. Phase space geometric information of the schizophrenia MEG is measured. Significant differences during negative stimulation appeared in the Alpha 1 (8–10 Hz) and Beta (13–30 Hz) bands. Significant differences appeared in the Alpha 1 band during positive and gray cross stimuli.