Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 130985, 13 pages
Research Article

How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

1Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
2Neuroelectrical Imaging and BCI Laboratory, Fondazione Santa Lucia Hospital, 00179 Rome, Italy
3Department of Physiology and Pharmacology, Sapienza University of Rome, 00185 Rome, Italy
4Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University of Rome, 00185 Rome, Italy

Received 2 April 2012; Accepted 1 June 2012

Academic Editor: Danielle Bassett

Copyright © 2012 J. Toppi 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.


The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.