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

• Published in: Computational and mathematical methods in medicine Vol. 2012; no. 2012; pp. 1 - 13
• Main Authors: Babiloni, Fabio, Astolfi, Laura, Toppi, Jlenia, Salinari, S., Mattia, D., Cincotti, Febo, De Vico Fallani, Fabrizio, Vecchiato, Giovanni, Maglione, Anton Giulio
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Puplishing Corporation 01.01.2012; Hindawi Publishing Corporation
• Subjects: Algorithms; Bioengineering; Brain; Brain - physiology; Brain Mapping; Brain Mapping - methods; Computational Biology; Computational Biology - methods; Computer Simulation; Electrodes; Electroencephalography; Electroencephalography - methods; Hemodynamics; Humans; Life Sciences; Magnetic Resonance Imaging; Magnetic Resonance Imaging - methods; Magnetoencephalography; Magnetoencephalography - methods; Models, Neurological; Models, Statistical; Neural Pathways; Neural Pathways - physiology; Probability; Signal Processing, Computer-Assisted; Software
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2012/130985

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.