Altered Brain Dynamics and Their Ability for Major Depression Detection Using EEG Microstates Analysis

Major depressive disorder (MDD) may be driven by dysfunction in intrinsic dynamic properties of the brain, and EEG microstate is a promising method for analyzing brain dynamics. However, the alterations in EEG microstate is still not entirely clear, and its ability for MDDs detection is worth probing. Moreover, the mechanism behind the neural networks contributing to microstates remains poorly understood in MDDs. Therefore, we applied microstate analysis and Topographic Electrophysiological State Source-imaging (TESS) on EEG data of 27 MDDs and 28 healthy controls (HCs). Compared to HCs, MDDs had apparent increase in microstate C and decrease in microstate D. Furthermore, TESS results showed that the underlying network of microstate C in MDDs overlapped with the anterior cingulate cortex and left insula gyrus, whereas main source of microstate D was in the orbital part of inferior frontal gyrus. The reduced transition probability from C to D in MDDs may reveal an imbalance between the networks of microstates. The microstate parameters as features reached good performance in identifying MDD (89.09% accuracy, 92.86% sensitivity, 85.19% specificity), indicating their potential as biomarkers of depression pathology. Collectively, these results highlight alteration of brain activity patterns and provide new insights into abnormal EEG dynamics in MDDs.

Automated summary

Research suggests that the alterations in EEG microstate are evident in patients with major depressive disorder (MDD), with increased microstate C and decreased microstate D. Analysis reveals that the network of microstate C in MDD overlaps with the anterior cingulate cortex and left insula gyrus, while the main source of microstate D is in the orbital part of the inferior frontal gyrus. The reduced transition probability from C to D may indicate an imbalance between the microstate networks. Microstate parameters demonstrate good performance in identifying MDD as biomarkers of depression pathology.