A graph theoretic approach to dynamic functional connectivity tracking and network state identification.

With the advances in neuroimaging technology, it is now possible to measure human brain activity with increasing temporal and spatial resolution. This vast amount of spatio-temporal data requires the development of computational methods capable of building an integrated picture of the functional networks for a better understanding of the healthy and diseased brain [1]. Although the construction of these networks from neuroimaging data is well-established [2], current approaches are limited to the characterization of the global topology of static networks where the links between different brain regions represent average connectivity over a long time period [3], [2]. Recent studies suggest that human cognition arises from the rapid formation and dissociation of synchronized neural activity on short time scales in the order of milliseconds [4]. There is a strong need for new electroencephalogram (EEG)-based analytic frameworks for monitoring dynamic functional network activity. In this paper, we propose a graph theoretic approach for tracking the changing topology of functional connectivity networks across time. First, we introduce an event detection algorithm based on node level feature extraction and principal components analysis of time-dependent node correlation matrices. Then, we propose a k-means based clustering approach to characterize each time interval with the most common connectivity states. Finally, the proposed methodology is applied to the study of the dynamics of functional connectivity networks during error-related negativity (ERN).