Exploring brain connectivity in auditory processing using complex network analysis. Application to dyslexia diagnosis

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 can reveal different brain states along with different ways of processing the information. In this work, complex network analysis was performed on the basis of brain connectivity obtained from Electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects. This connectivity is inferred from the Phase-Amplitude coupling (PAC) from different EEG electrodes, to explore differences between controls and dyslexic subjects. Connectivity data allows the construction of a graph, and then, graph theory can be 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 connectivity can be 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.