Modelling and analysis of brain functional network

Brain is a typical complex system. Understanding the structure and working mechanism of the brain is of great significance to the development of life science, medical engineering, artificial intelligence and other frontier scientific studies. Functional magnetic resonance imaging (fMRI) and some other techniques which can achieve non-invasive acquisition of brain neural activity signals, are important auxiliary techniques for observing the behaviors and functions of human brain. While, complex network theory and the related methods provide the main theoretical and technical framework for exploring and understanding the mechanisms of brain. In this paper, we review the recent achievements in functional network analysis and modeling based on fMRI and the other techniques. The methods include correlation, causal brain functional network, time-varying behavior analysis, energy landscape and data-based dynamical modeling, and so on. The related principles, applicability and limitations of these methods are introduced. At present, the brain network modelling based on data analysis has achieved abundant outcomes, while, exploring the brain and neuroscience along the line of complex system theory is still promising due to the continuous development of neuroimaging technology and the rapidly accumulating high-quality neural activity data.