Divergent spatiotemporal signatures characterize impaired facial emotional recognition in major depressive disorder: An event-related microstate study.

Background: Major depressive disorder (MDD) is a chronic affective mental disorder with intricate neuropathological characteristics. Microstate analysis has proved its ability to reveal the relatively stable features in a specific brain process. However, the relationship between event-related microstate networks and affective dysfunctions in patients with MDD is not well known.

Methods: The 128-channel electroencephalogram (EEG) data from 24 MDD patients and 29 healthy controls (HCs) with facial emotion recognition (FER) tasks were used in this study. The analysis encompassed both event-related microstate parameters and specific microstate network metrics. The microstate parameters included Mean Global Field Power (mGFP), Mean Duration (mDur), Time Coverage (TC), and Segment Count Density (SegD). The network metrics evaluated were the clustering coefficient (CC), path length (Lp), global efficiency (Eg), and local efficiency (Eloc).

Results: Three event-related microstates (MS-P1, MS-N170, and MS-P2) were estimated. Compared with HCs, the MDD patients showed significantly increased mGFP in MS-P1 with the sad emotion and decreased microstate parameters in MS-P2 with happy (mDur and TC) and sad (SegD and TC) emotions. Correlation results showed that MS-P1 with the sad emotion was positively related to clinical outcomes. MS-P2 with happy and sad emotions negatively correlated with clinical scores. Additionally, the microstate networks confirmed that MDD patients had decreased network efficiency of the happy emotion in MS-P1 while increased efficiency in dealing with the negative emotion in MS-P2.

Conclusions: By analyzing event-related microstates and brain networks, we provided a novel approach to demonstrate the divergent patterns for FER processing and the atypical dynamic coordination and integration of affective mechanisms underlying emotional deficits in MDD.