Yingjie Li1, Dan Cao2, Ling Wei2, Yingying Tang3, Jijun Wang4. 1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China. Electronic address: liyj@shu.edu.cn. 2. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China. 3. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China. 4. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China. Electronic address: jijunwang27@163.com.
Abstract
OBJECTIVE: This paper evaluates the large-scale structure of functional brain networks using graph theoretical concepts and investigates the difference in brain functional networks between patients with depression and healthy controls while they were processing emotional stimuli. METHODS: Electroencephalography (EEG) activities were recorded from 16 patients with depression and 14 healthy controls when they performed a spatial search task for facial expressions. Correlations between all possible pairs of 59 electrodes were determined by coherence, and the coherence matrices were calculated in delta, theta, alpha, beta, and gamma bands (low gamma: 30-50Hz and high gamma: 50-80Hz, respectively). Graph theoretical analysis was applied to these matrices by using two indexes: the clustering coefficient and the characteristic path length. RESULTS: The global EEG coherence of patients with depression was significantly higher than that of healthy controls in both gamma bands, especially in the high gamma band. The global coherence in both gamma bands from healthy controls appeared higher in negative conditions than in positive conditions. All the brain networks were found to hold a regular and ordered topology during emotion processing. However, the brain network of patients with depression appeared randomized compared with the normal one. The abnormal network topology of patients with depression was detected in both the prefrontal and occipital regions. The negative bias from healthy controls occurred in both gamma bands during emotion processing, while it disappeared in patients with depression. CONCLUSIONS: The proposed work studied abnormally increased connectivity of brain functional networks in patients with depression. By combing the clustering coefficient and the characteristic path length, we found that the brain networks of patients with depression and healthy controls had regular networks during emotion processing. Yet the brain networks of the depressed group presented randomization trends. Moreover, negative bias was detected in the healthy controls during emotion processing, while it was not detected in patients with depression, which might be related to the types of negative stimuli used in this study. SIGNIFICANCE: The brain networks from both patients with depression and healthy controls were found to hold a regular and ordered topology. Yet the brain networks of patients with depression had randomization trends.
OBJECTIVE: This paper evaluates the large-scale structure of functional brain networks using graph theoretical concepts and investigates the difference in brain functional networks between patients with depression and healthy controls while they were processing emotional stimuli. METHODS: Electroencephalography (EEG) activities were recorded from 16 patients with depression and 14 healthy controls when they performed a spatial search task for facial expressions. Correlations between all possible pairs of 59 electrodes were determined by coherence, and the coherence matrices were calculated in delta, theta, alpha, beta, and gamma bands (low gamma: 30-50Hz and high gamma: 50-80Hz, respectively). Graph theoretical analysis was applied to these matrices by using two indexes: the clustering coefficient and the characteristic path length. RESULTS: The global EEG coherence of patients with depression was significantly higher than that of healthy controls in both gamma bands, especially in the high gamma band. The global coherence in both gamma bands from healthy controls appeared higher in negative conditions than in positive conditions. All the brain networks were found to hold a regular and ordered topology during emotion processing. However, the brain network of patients with depression appeared randomized compared with the normal one. The abnormal network topology of patients with depression was detected in both the prefrontal and occipital regions. The negative bias from healthy controls occurred in both gamma bands during emotion processing, while it disappeared in patients with depression. CONCLUSIONS: The proposed work studied abnormally increased connectivity of brain functional networks in patients with depression. By combing the clustering coefficient and the characteristic path length, we found that the brain networks of patients with depression and healthy controls had regular networks during emotion processing. Yet the brain networks of the depressed group presented randomization trends. Moreover, negative bias was detected in the healthy controls during emotion processing, while it was not detected in patients with depression, which might be related to the types of negative stimuli used in this study. SIGNIFICANCE: The brain networks from both patients with depression and healthy controls were found to hold a regular and ordered topology. Yet the brain networks of patients with depression had randomization trends.