Study On Simplified Method Of Human Brain Region Structure Network

In recent years, the structural and functional information of human brain can be obtained on the basis of Magnetic Resonance Imaging, Diffusion Tensor Imaging and functional Magnetic Resonance Imaging technologies, which make it possible to realize the reconstruction of human brain network technology. The development of brain imaging, complex network and information technologies provides the theory foundations and analysis methods for the study of human brain network. Because of the complexity, currently studies about human brain network reconstruction are mainly based on the large scale network and most researches focus on the local features of human brain network such as node degree, clustering coefficient, central degree and modularization, but the study about the global characteristics of human brain structural network is relatively infrequent. In view of the system complexity of human brain network, when comparing the similarities and the specificities of different brain networks, if we use the network which contains all nodes and all connections, the feasibility of the extraction method of status characteristics may be reduced due to its high dimension. By this reason, we want to find the important structure of the human brain network and then simplify the network structure. The research of the simplification method for human brain network structure is an important problem to be solved and it is also the key technology for further study of the human brain network structure characteristics.In this context, the simplification methods for the brain region structural network have been discussed in this thesis. Firstly the core nodes of the human brain structural network are identified by using the node centrality evaluation method based on the local characteristics of the network nodes; then the K-shell decomposition method which is based on the global characteristics of nodes is used to identify the core nodes in the human brain, through analysis and comparison of the above two methods, a new method based on the K-shell decomposition method and the important node identification method based on betweenness centrality is proposed. The comparison and analysis of the experiment results show the rationality of this method.Because the implementation process of the method based on K-shell decomposition and the important node identification is a little complicated, at the same time the results will be affected by the choice of parameters. In order to find a more effective human brain structural network simplification method, homomorphic graph theory is introduced into our work to launch the preliminary study of the simplification of the human brain network. Considering the unidirectional properties of the brain network we build a kind of homomorphic operator and complete the human brain structural network simplification based on this homomorphic operator. The simplified results are analyzed in detail and it may present us another way to carry out our further research work. These works provide feasible means for identifying and simplifying of human brain structural network and they also give the foundations for further research on the dynamic performance of the human brain network.