Research On Location Method Of Core Node Of Human Brain Based On Criticality Contribution Matrix

Human brain is one of the most complicated systems discovered by people for now. With the human brain reconstruction technology based on magnetic resonance imaging, people can explore the physiological principle and operating mechanism of human brain using complex network technology and information theory. This method has become a hot spot in neuroscience research field and received more and more attention from the whole world. Human brain network is reconstructed by taking different areas of cerebral cortex as brain network nodes, and taking specific connection index as brain network edges between two brain regions. In the brain network, there are some nodes which are very important for the whole network. At present, either in the cognitive science study or in clinical analysis for brain diseases, when talking about how to find this kind of nodes, the evaluation methods on the basis of node betweenness and the node degree is widely adopted. These locating methods pay more attention to the local information of single node but not for the global status and influence in the whole network, so the results for the key nodes(which is also called hubs)are not always be comprehensive.According to current research status, a weighted human brain network hubs location method based on importance contribution matrix is proposed in this paper. The weighted importance contribution matrix is built by the node degree, node efficiency, node strength, and the number of streamlines between brain regions and it can be regarded as the importance measurement of a single brain region for the whole human brain network. Three topics are discussed in this work:binary brain network and weighted brain network reconstruction based on magnetic resonance imaging data; the research of binary brain network hubs location method based on importance contribution matrix and the research of weighted brain network hubs location method based on weighted importance contribution matrix. Because both the local information and the global information of the node in brain network are considered, the brain network vulnerability test result shows that this method is more rational than current popular location methods. Finally, we also apply this method to the analysis of schizophrenia. By comparing the human brain structure networks of patients, siblings and controlled people, some difference of human brain network hubs are found and the experimental results are in accord with current researches, which also provides evidence for the rationality of our method.