Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 452503, 15 pages
http://dx.doi.org/10.1155/2012/452503
Research Article

Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

1Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Office 501 Galaxias Center, 33 Arch. Makarios III Avenue, 1065 Nicosia, Cyprus
2Artificial Intelligence & Information Analysis Laboratory, Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece

Received 2 April 2012; Revised 5 June 2012; Accepted 10 August 2012

Academic Editor: Tianzi Jiang

Copyright © 2012 Andreas A. Ioannides 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.

Abstract

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.