The Network Of The Deactivations In Brain

It's well known that the brain is the most complicated and perfect dynamic information processing system. In a sense, the brain can be considered as a multi-layered dynamic distributed network. In order to execute a series of complex functions, it can establish and reestablish its functional connections incessantly. Many studies about the brain functions were focused on the task-related activations, until recent years, the deactivations have been obtained extensive attention gradually. At present, however, the research into deactivations is still limit to locating its functional areas, and exploring physiological mechanism of deactivations.As reported in the recent literatures, the brain also has its functional activation patterns during clear-headed, conscious resting state that without any tasks. The voxel nodes that activate in the resting state always deactivate in the tasking state. So we attempt to study the active pattern of the brain in the resting state by the information of deactivations in the testing state.The research into the complex networks is a part of systemic complexity theory and it can be served as a powerful tool for the study of complex science and system. So it offers a novel viewpoint for this theme. In this dissertation, the main work about the deactivations in the brain can be summarized as follows:1. The deactivations in the brain were introduced briefly, and the physiological mechanism was also analyzed, and the brain networks in resting state were also discussed primarily here.2. The complex network theory is introduced in the research of the deactivations for the first time. Here we pay more attention to the two different cognitive tasks (the task of spatial orientation for the symbol and the numeric addition task) and extract the information from deactivations spots to establish the network. We found that both the two networks have larger cluster coefficient than the random networks, and its average path length is shorter than regular networks too. That is to say, they have the small-world attributes. To a certain extent, it can explain the reason that the information transfer of the brain has so high speed and efficiency.3. Finally, we calculated the global variable of the network--- the betweenness of the nodes and located the 10 nodes that have the largest betweenness in the brain. Integration the relationship of the brain network in the deactivations and the resting...