Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 451516, 10 pages
Research Article

A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

1Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
2Department of Psychology, Florida State University, Tallahassee, FL 32306, USA

Received 30 March 2012; Revised 3 July 2012; Accepted 10 July 2012

Academic Editor: Tianzi Jiang

Copyright © 2012 Ali Yener Mutlu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.