How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network
Joint Authors
Maglione, Anton Giulio
Salinari, S.
Babiloni, Fabio
Astolfi, Laura
De Vico Fallani, Fabrizio
Cincotti, Febo
Toppi, Jlenia
Vecchiato, Giovanni
Mattia, D.
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-08-07
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Toppi, Jlenia& De Vico Fallani, Fabrizio& Vecchiato, Giovanni& Maglione, Anton Giulio& Cincotti, Febo& Mattia, D.…[et al.]. 2012. How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-448148
Modern Language Association (MLA)
Toppi, Jlenia…[et al.]. How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-13.
https://search.emarefa.net/detail/BIM-448148
American Medical Association (AMA)
Toppi, Jlenia& De Vico Fallani, Fabrizio& Vecchiato, Giovanni& Maglione, Anton Giulio& Cincotti, Febo& Mattia, D.…[et al.]. How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-448148
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-448148