Research And Analysis Of Brain Function Based Based On Complex Network

The brain is a dynamic system constructed by large-scale complex networks composed of connections between neurons. The structures and the dynamic characteristics of neuron network of different scales cause the complexity of brain on structure and function. This thesis introduces the current research status of complex networks and some important concepts, and introduces the several important network topology model. This thesis also introduces the complexity of brain, both structural and functional. Then this thesis introduces the recently research on brain structural network from two aspects of cortex thickness measurement and diffusion tensor imaging, and give our research on brain functional network through the EEG signals and fMRI signals.Based on the statistical dynamics theory, this thesis applies diffusion entropy method on the analysis of the dynamic scale performance and probability distribution function (PDF) of EEG signals, studying the evolution process and the scale invariance behavior of the system, and verifies that the PDF fits the sharp-peak and fat-tail Levy distribution. Then based on phase synchronization theory, this thesis established motor-cortical function network using the ECoG signals of finger flextion. Through the analysis of the two networks under the rest state and task state, we found the cluster coefficient is larger and the connectivity is stronger under the task state. The results suggest that the brain in cognitive task state showed more significant activity.Based on the mutual information theory, this thesis studies the brain functional networks under the rest state and stimulated state by the moving time window method using fMRI data of two states, and analyses the correlation of brain functional areas. Results show that both the brain functional networks under two states represent small world features. Then based on Granger causality theory, this thesis establishes causal brain functional network using fMRI data of image recognition task. The causality between different voxels characterizes the directed edge of the network. The analysis of causal directed network shows that the activities of neurons widely distributed in the brain ultimately lead to activities of several central regions, and the areas of the brain related to vision and synthesize are more active under image recognition task.