Construct Resting-state Brain Functional Network And Research On Community Struture

The brain can receive complex information from the external world. All kinds of information can be organized, processed and handled by the brain, which provides guidance to relevant organs to execute the task. It is meaningful to explore the relationship between organizational structure and executive function in the human brain. It has significance to understand the brain mechanisms and explore the thought process of human, which is helpful to open the "black box" of human brain functional activity.If we see the brain region as a node, and the functional connection between two brain regions as an edge, then the human brain can be described as a complex network. Community structure is one of the most basic properties of complex networks. The structural properties and the intrinsic activity of real system can be understood by analyzing the community structure of complex networks. Many different community detecting algorithm are proposed by scholars in recent years. These algorithms are successfully used in real world complex networks such as members of society networks and biological networks and so on. Putting the community structure into human brain functional network is an important inspiration to understand how the human brain operating.The brain functional data of healthy subjects were collected by fMRI, and the brain functional network of human being based on time series is constructed in our research. The community structure of human brain functional network was detected by using the community detecting algorithm and analyzed then, which helped us to explore the brain functional mechanisms. Main researches are as follows:(1) The brain functional activity data were collected by using functional magnetic resonance imaging. The raw data were preprocessed according to realign, normalize, Gaussian smooth and low-frequency filtering. These steps can reduce the data noise and remove some of interfering signals.(2)The brain functional network based on time sequences was constructed. The whole brain region was divided into90regions as the network nodes according to a priori template called AAL. By calculating the average time series of each brain node, the functional connectivity strength between two nodes was measured by computing the coefficient according to Pearson correlation method. The functional connectivity can be obtained by setting threshold. The brain functional network is obtained and describes as a binary matrix.(3) The community structure of brain functional network was detected and analyzed. A threshold range of vertices in the network is designated according to modularity and full connected network theory. The community structures were detected by using the hierarchical clustering algorithm and the greedy algorithm respectively. Result shows that similar community structures are obtained, moreover, the community structure of human brain functional network is conformed, while these modules have realistic meanings other than randomness. Then different performances can be explored across the threshold by analyzing the modularity. An effective threshold range of vertices in brain network is proposed. Some nodes and functional connectivity that play a key role in the brain network are designate by analyzing the role they played relying on community structure. These key nodes and edges make a contribution for human to comprehend the global network properties and how information coordinating in whole brain.Our research provides an effective method for human to comprehend the human brain mechanisms. It supplies an effective tool to explore the brain regions lesion of neuropathology as well.