EEG Based Depression Recognition by Employing Static and Dynamic Network Metrics

Abstract

Neural circuit dysfunction underlies the biological mechanisms of major depressive disorder (MDD). However, little is known about how the brain’s dynamic connectomes differentiate between depressed patients and normal controls. As a result, we collected resting-state Electroencephalography from 16 MDD patients and 16 controls using 128-electrode geodesic sensor net. Static and dynamic network metrics were later applied to explore the abnormal topological structure of MDD patients and identify them from normal controls using traditional machine learning algorithms with feature selection methods. Results showed that the MDD tend to have a more randomized formation both in static and dynamic network. We also found that the combined static-dynamic feature set usually outperformed others with a highest accuracy of 79.25% under delta band. Lower frequency band (delta, theta) showed relatively better outcomes compared to higher frequency band (alpha, beta). It also indicate the role of functional segregation features as a potential biomarker for depression. In conclusion, neuropathological mechanism of depression may be more objectively quantified and evaluated from the perspective of combining static and dynamic network.