Graph analysis of neuronal interactions for the error-related negativity

Abstract

The brain is a biological system with dynamic interactions between its sub-systems. The complexity of this system poses a challenge for identifying 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 dynamic measure of phase synchrony which is used to form sparsely connected networks that can be evaluated using measures of graph theory. These 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 from subjects grouped according to an Externalizing Inventory. Results conclude weighted clustering coefficient and binary path length measures demonstrate significant differences between error low externalizers with all other response/externalizer types (error/high, correct/low, and correct/high).