Analysis of task-related MEG functional brain networks using dynamic mode decomposition

Abstract

ObjectiveFunctional connectivity networks explain the different brain states during diverse motor, cognitive, and sensory functions. Extracting spatial network configurations and their temporal evolution is crucial for understanding the brain function during diverse behavioral tasks. ApproachIn this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography (MEG) data during motor and memory tasks. Main ResultsThe framework generates dominant spatial brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the DMD-based approach provides a better temporal resolution than the PCA-based approach. SignificanceWe believe that DMD has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.