Resumen de Reliability analysis of complex brain networks based on chaotic time series

Gengxin Sun, Sheng Bin, Chi-Cheng Chen

• The human brain is an organic which whole consisting of hundreds of billions of neurons, and the neural network is the information path of the brain for processing information. Network characteristics will change along with network topology or node change, and the change may lead to brain lesions. Using EEG to construct brain network can effectively discover the internal mechanism of improvement in human brain, but the traditional method of constructing brain network is difficult to reflect the network complexity because of the small number of network nodes. In this paper, a new algorithm of brain network construction based on chaotic time series is proposed. The method estimates neuronal dynamic equations and neuronal networks topology by using only noisy time series. By analyzing the network constructed based on phase space reconstruction of Lorenz system, network characteristics that the average path length increase as embedding dimension, and the cluster coefficient on the contrary are found. The topology and statistical characteristics of the network constructed from neuronal chaotic bursting can reflect time-evolution of time series. Through analyzing the real epileptic EEG signals dataset using the proposed complex networks construction method, we found that epileptic seizure is different from seizure-free intervals in network topology and statistical characteristics. Epileptic EEG of before, during and after a tonic-clonic seizure indicate that cluster coefficients of the sliding local networks significantly increased during a seizure, so the result can provide clonic seizure prediction. The proposed method of complex network topology estimation and complex network construction of time series can depict the dynamic characteristics of brain network, and provide a new idea for the treatment of mental illness.