Schizophrenia is a serious mental disorder. Network analysis of magnetoencephalogram signals may help to identify potential biomarkers of schizophrenia. The goal of this investigation was to identify potential biomarkers in the magnetoencephalogram signals of patients with schizophrenia, global brain connectivity measures was used for emotion recognition in discriminating the patients from controls. First, we employed a mutual information method to explore the topological characteristics of the brain network in patients with schizophrenia among different frequency bands in response to four different stimulus conditions. Second, multidimensional cross-recurrence quantification analysis was performed to investigate the differences in dynamic coupling among different frequencies of brain magnetic waves in patients with schizophrenia in response to four different stimulus conditions, as the major novel contribution of our study. We found that the differences in topological features of the brain network appear in different frequency bands under different stimulus conditions. The differences are evident in the alpha 1 (8-10 Hz) and beta (13-30 Hz) frequency bands in response to negative stimuli, in the alpha 1 (8-10 Hz) frequency band in response to positive stimuli, and in the theta (4-8 Hz) and alpha 1 (8-10 Hz) frequency bands in response to neutral and gray-cross stimuli. In addition, differences in dynamic coupling among pairs of frequency bands were the most prominent in response to positive stimuli. The characteristics identified by our methods may be potential markers of schizophrenia present in magnetoencephalogram data, which can facilitate the clinical identification of schizophrenia patients. Our method provides a comprehensive perspective of brain networks in patients with schizophrenia and has practical applications for the clinical diagnosis of this disease.

Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct directed weighted networks to characterize schizophrenia magnetoencephalography (MEG). We first generate data based on coupled autoregressive processes to test the effectiveness of MKGC in comparison with the bivariate linear Granger causality and bivariate inhomogeneous polynomial kernel Granger causality. The test results suggest that MKGC outperforms the other two methods. Based on these results, we apply MKGC to construct effective connectivity networks of MEG for patients with schizophrenia (SCZs). We measure three network features, i.e., strength, nonequilibrium, and complexity, to characterize schizophrenia MEG. Our results suggest that MEG of the healthy controls (HCs) has a denser effective connectivity network than that of SCZs. The most significant difference in the in-connectivity strength is observed in the right frontal network ($p=0.001$). The strongest out-connectivity strength for all subjects occurs in the temporal area, with the most significant between-group difference in the left occipital area ($p=0.0018$). The total connectivity strength of the frontal, temporal, and occipital areas of HCs exhibits higher values compared with SCZs. The nonequilibrium feature over the whole brain of SCZs is significantly higher than that of the HCs ($p=0.012$); however, the results of Shannon entropy suggest that healthy MEG networks have higher complexity than schizophrenia networks. Overall, MKGC provides a reliable approach to construct MEG brain networks and characterize the network characteristics.

Objective: Impaired cognitive function is a central symptom of schizophrenia and is often correlated with inferior global functional outcomes. However, the role of some neurobiological factors such as cortical structure alterations in the underlying cognitive damages in schizophrenia remains unclear. The present study attempted to explore the neurobiomarkers of cognitive function in drug-naive, first-episode schizophrenia by using structural magnetic resonance imaging (MRI). Methods: The present study was conducted in patients with drug-naive, first-episode schizophrenia (SZ) and healthy controls (HCs). MRI T1 images were pre-processed using CAT12. Surface-based morphometry (SBM) was utilised to evaluate structural parameters such as cortical thickness and sulcus depth. The positive and negative syndrome scale (PANSS) and Chinese version of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) consensus cognitive battery (MCCB) were employed to estimate the psychotic symptoms and cognition, respectively. Results: A total of 117 patients with drug-naive first-episode schizophrenia (SZ) and 98 healthy controls (HCs) were included. Both the cortical thickness and sulcus depth in the frontal lobe were lower in patients with SZ than in the HCs under family-wise error correction (p < 0.05). Attention and visual learning in MCCB were positively correlated with the right lateral orbitofrontal cortical thickness in the patients with SZ (p < 0.01). Conclusions: The reduced surface value of multiple cortical structures, particularly the cortical thickness and sulcus depth in the frontal lobe, could be the potential biomarkers for cognitive impairment in SZ.