Identification of small world topologies in neural functional connections quantified by phase synchrony measures

The brain is a complex biological system with dynamic interactions between its sub-systems. One particular challenge in the study of this complex system is the identification of dynamic functional networks underlying observed neural activity. Current imaging approaches index local neural activity very well, but there is an increasing need for methods that quantify the interaction between regional activations. In this paper, we focus on inferring the functional connectivity of the brain based on electroencephalography (EEG) data. The interactions between the different neuronal populations are quantified through a recently proposed dynamic measure of phase synchrony. Small world measures, which include clustering coefficient, path length, global efficiency, and local efficiency, are computed on graphs obtained through the phase synchrony measure to study the underlying functional networks. The proposed measures are applied to an EEG study containing the error-related negativity (ERN), a brain potential response that indexes endogenous action monitoring, to determine the organization of the brain during a decision making task and determine the differences between Error and Correct responses.