Analysis of functional connectivity in depression based on a weighted hyper-network method

Abstract

Abstract Objective. Brain connectivity network is a vital tool to reveal the interaction between different brain regions. Currently, most functional connectivity methods can only capture pairs of information to construct brain networks which ignored the high-order correlations between brain regions. Approach. Therefore, this study proposed a weighted connectivity hyper-network based on resting-state EEG data, and then applied to depression identification and analysis. The hyper-network model was build based on least absolute shrinkage and selection operator sparse regression method to effectively represent the higher-order relationships of brain regions. On this basis, by integrating the correlation-based weighted hyper-edge information, the weighted hyper-network is constructed, and the topological features of the network are extracted for classification. Main results. The experimental results obtained an optimal accuracy compared to the traditional coupling methods. The statistical results on network metrics proved that there were significant differences between depressive patients and normal controls. In addition, some brain regions and electrodes were found and discussed to highly correlate with depression by analyzing of the critical nodes and hyper-edges. Significance. These may help discover disease-related biomarkers important for depression diagnosis.