Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain's structural and URLanization. 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 .
Complex network analysis has an increasing relevance in the study of neurological disorders, enhancing the knowledge of brain's structural and URLanization. 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) .
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