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Chapter 10
Brain Network Analysis from High-Resolution EEG Signals
Fabrizio De Vico Fallani 1,2 and Fabio Babiloni 1,3
1 I RCCS “Fondazione Santa Lucia”, Rome, Italy
2
Research Centre for Models and Information Analysis in Biomedical Systems,
University “Sapienza”, Rome, Italy
3
Department of Human Physiology and Pharmacology,
University “Sapienza”, Rome, Italy
Over the last decade, there has been a growing interest in the detection of the
functional connectivity in the brain from different neuroelectromagnetic and
hemodynamic signals recorded by several neuro-imaging devices such as the
functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalo-
graphy (EEG) and magnetoencephalography (MEG) apparatus. Many methods
have been proposed and discussed in the literature with the aim of estimating
the functional relationships among different cerebral structures. However, the
necessity of an objective comprehension of the network composed by the
functional links of different brain regions is assuming an essential role in the
Neuroscience. Consequently, there is a wide interest in the development and
validation of mathematical tools that are appropriate to spot significant features
that could describe concisely the structure of the estimated cerebral networks.
The extraction of salient characteristics from brain connectivity patterns is an
open challenging topic, since often the estimated cerebral networks have a
relative large size and complex structure. Recently, it was realized that the
functional connectivity networks estimated from actual brain-imaging
technologies (MEG, fMRI and EEG) can be analyzed by means of the graph
theory. Since a graph is a mathematical representation of a network, which is
essentially reduced to nodes and connections between them, the use of a
theoretical graph approach seems relevant and useful as firstly demonstrated on
a set of anatomical brain networks. In those studies, the authors have employed
two characteristic measures, the average shortest path L and the clustering index
C , to extract respectively the global and local properties of the network
structure. They have found that anatomical brain networks exhibit many local
connections (i.e. a high C ) and few random long distance connections (i.e. a low
L ). These values identify a particular model that interpolate between a regular
lattice and a random structure. Such a model has been designated as “small-
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