Persistent patterns of interconnection in time-varying cortical networks estimated from high-resolution EEG recordings in humans during a simple motor act

In this work, a novel approach based on the estimate of time-varying graph indices is proposed in order to capture the basic schemes of communication within the functional brain networks during a simple motor act. To achieve this, we used a cascade of computational tools able to estimate first the electrical activity of the cortical surface by using high-resolution EEG techniques. From the cortical signals of different regions of interests we estimated the time-varying functional connectivity patterns by means of the adaptive partial directed coherence. The time-varying connectivity estimation returns a series of networks evolving during the examined task which can be summarized and interpreted with the aid of mathematical indices based on graph theory. The combination of all these methods is demonstrated on a set of high-resolution EEG data recorded from a group of healthy subjects performing a simple foot movement. It can be anticipated that the combination of the time-varying connectivity with the theoretical graph analysis is able to reveal precious information about the interconnections of the cerebral network as the significant persistence of mutual links and three-node motifs.