Dynamic analysis of epileptic causal brain networks based on directional transfer function / 生物医学工程学杂志

Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.

A metrics method for cellular network structure / 中国组织工程研究

BACKGROUND: Communication between cells is indispensable in a multicellular organism society. Many cells communicate with each other, forming a complex cellular network structure. It is very important to measure and evaluate the relevant attribute values of cellular network structure. OBJECTIVE: To propose a metrics method of cellular network structure based on a complex network. METHODS: Based on literature research and practical application, the metrics framework of cellular network structure was established. The structure of cellular network was measured in the aspects of the degree of node, the degree distribution of cellular network, the average path length of cellular network and the cluster coefficient of cellular network. A small experiment was taken as an example to verify the validity of the method. RESULTS AND CONCLUSION: In the degree distribution of cellular networks, the degree values of most cell nodes are relatively small, and only a small number of cell nodes have higher degree values. The more obvious power-law distribution of the degree distribution P(k) of cell nodes indicates the more reasonable structure of the cellular network as well as the more normal cellular network. At the same time, many cellular network structures have smaller average path lengths. The larger cluster coefficient of the cellular network indicates the higher aggregation characteristics of the cellular network. Generally speaking, the tighter the cell network structure is, the more obvious the clustering characteristics of the cell network structure are, and the more normal the cellular network is.