Brain Functional Network Based on Small-Worldness and Minimum Spanning Tree for Depression Analysis

Abstract

Since the outbreak and spread of corona virus disease 2019 (COVID-19), the prevalence of mental disorders, such as depression, has continued to increase. To explore the abnormal changes of brain functional connections in patients with depression, this paper proposes a depression analysis method based on brain function network (BFN). To avoid the volume conductor effect, BFN was constructed based on phase lag index (PLI). Then the indicators closely related to depression were selected from weighted BFN based on small-worldness (SW) characteristics and binarization BFN based on the minimum spanning tree (MST). Differences analysis between groups and correlation analysis between these indicators and diagnostic indicators were performed in turn. The resting state electroencephalogram (EEG) data of 24 patients with depression and 29 healthy controls (HC) was used to verify our proposed method. The results showed that compared with HC, the information processing of BFN in patients with depression decreased, and BFN showed a trend of randomization.

Keywords

depression / brain function network (BFN) / small-worldness (SW) / minimum spanning tree (MST)