  Complex network analysis has an increasing relevance in the study of
neurological disorders, enhancing the knowledge of brain's structural and
functional organization. Network structure and efficiency reveal different
brain states along with different ways of processing the information. This work
is structured around the exploratory analysis of the brain processes involved
in low-level auditory processing. A complex network analysis was performed on
the basis of brain coupling obtained from Electroencephalography (EEG) data,
while different auditory stimuli were presented to the subjects. This coupling
is inferred from the Phase-Amplitude coupling (PAC) from different EEG
electrodes to explore differences between controls and dyslexic subjects.
Coupling data allows the construction of a graph, and then, graph theory is
used to study the characteristics of the complex networks throughout time for
controls and dyslexics. This results in a set of metrics including clustering
coefficient, path length and small-worldness. From this, different
characteristics linked to the temporal evolution of networks and coupling are
pointed out for dyslexics. Our study revealed patterns related to Dyslexia as
losing the small-world topology. Finally, these graph-based features are used
to classify between controls and dyslexic subjects by means of a Support Vector
Machine (SVM).
