Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series

Schizophrenia (SZ) is categorized as positive SZ and negative SZ in terms of the predominant symptom. In this study, we proposed the hypothesis that positive SZ had more information interaction and negative SZ had less information interaction between brain regions compared with normal controls. To verify the hypothesis, firstly, we recorded and preprocessed the electroencephalogram data with 32 electrodes from 28 schizophrenic patients (14 with positive symptoms and 14 with negative symptoms) and 14 normal controls. Then, mutual information (MI) was used to construct the functional brain networks, and these networks were analyzed by the method of graph theory. We calculated and analyzed several network metrics which reflected the ability of the information processing in the network. The results showed smaller clustering coefficient, larger average characteristic path length, lower global efficiency, lower local efficiency and smaller degrees in SZ functional brain networks compared with normal controls. We conclude that there are fewer information interactions in SZ patients than that in normal controls, while positive SZ has more information interactions than negative SZ does, and patients tend to have slower and less efficient information transfer between brain regions. These findings have a great significance to deeply understand the pathogenesis of SZ.