Exploration of a Network-Based EEG Marker for Major Depressive Disorder.

Major depressive disorder (MDD) is a highly prevalent and debilitating disorder that impacts an individual's quality of life and ability to function due to persistent sadness, hopelessness, and lack of interest or pleasure in daily activities. Despite its high prevalence, many MDD patients are misdiagnosed, lack adequate treatment, and will never enter remission. We consider scalp EEG as a promising imaging modality for a computationally-driven biomarker. With an open-source dataset from TDBrain including 118 MDD patients, we construct patient specific dynamic network models from short EEG recordings from each patient to capture how brain regions influence and are influenced by other regions. We explore the sink index, a novel EEG marker that quantifies this notion of regional influence, to understand changes in network dynamics between MDD severity levels. Our preliminary results show unique dynamics in the MDD brain and correlations between the sink index and severity of MDD. These findings outline the potential of utilizing the sink index as a biomarker for MDD.