EEG BASED FUNCTIONAL CONNECTIVITY REFLECTS COGNITIVE LOAD DURING EMOTION REGULATION

Patients suffering from anxiety disorders commonly have difficulties in emotion regulation. Understanding underlying brain network dysfunction can provide insight into their pathophysiology. Electroencephalography (EEG)-based functional connectivity contains high temporal information of brain network dynamics. We acquired EEG recordings in the resting state and during a series of emotion regulation tasks (ERT) from a sample of 20 subjects with anxiety disorders and 20 healthy controls. To generate EEG-based functional connectomes, we used the weighted phase lag index (WPLI), a phase based connectivity metric. Graph theory measures of clustering coefficient (CC) and characteristic path length (CPL) were computed to characterize properties of these complex functional networks. Results showed that in the theta band network integration measured using CPL increased as the cognitive load during the emotion regulation task increased, with a complementary trend in the CC. In summary, the EEG-based functional connectome is highly dynamic and task dependent, thus may serve as a promising non-invasive biomarker for diagnosis and treatment evaluation.