Resting-state functional networks with ICA and Granger causality

Brain networks in resting state can be calculated from functional magnetic resonance imaging (fMRI) signals, which is one of the most important techniques for understanding the mechanism in neural activities and has been widely used for basic research and clinic. In 2001, neurologist Marcus E. Raichle and his coworkers at Washington University School of Medicine proposed a hypothesis called "default mode brain networks" activity, which would be closely involved in monitoring external and internal conditions, reviewing past knowledge and episodic memory processing. But its function of this network is unclear. Some analysis of resting-state functional MRI data from human of all ages are shown in this paper. Several analysis tools are used: ICA is applied to find out the responding component of default mode network, and Granger causality analysis is used to estimate the functional connectivity not only in each region of classical DMN, but also among these regions. In addition, three algorithms for ICA: maximum likelihood, fast fixed point and joint approximate diagonalization of eigenmatries are compared in effects of abstracting resting state network components.