Variability of Complex Networks Parameters on Effective Connectivity Analysis: An Absence Epilepsy Case

In recent years, some studies brought the complex networks theory into neuroscience. Connectivity patterns are widely studied and vary from the data acquisition method: fMRI, EEG, and so on. Those connectivity data can be classified into three main types: anatomical, functional, and effective. The first one relies only on anatomical connections, the second relies on the correlations of signals recorded from different parts of the brain, and the latter on the direction of the information flow. In this work, it is proposed to study the variability of the parameters of complex networks, for two methods of effective connectivity (namely: Transfer Entropy and Phase Transfer Entropy) and EEG data, of 10 patients with absence epilepsy. It is known that the topology of the complex network of the epilepsy crisis varies substantially between patients. However, for the same patient, effective connectivity topologies may vary, but they should not. The variation of network parameters, for the same complex network (the same patient), can lead to misinterpretations of the analyzed phenomena. In this work, preliminary results indicate that such variability indeed occurs, for the same patient, even in strictly similar measures, such as Transfer Entropy and Phase Transfer Entropy.