**6. Conclusion**

The human brain is modeled as a functionally inter-connected network. Restingstate functional magnetic resonance imaging enables observing brain spontaneous activity *in vivo*. In this chapter, we reviewed the process of rs-fMRI data as well as group analysis methods. Different node definitions and edge estimation were discussed during the network construction stage. Nodes can be defined at the voxel level or with the help of a brain atlas. Lesions, such as glioma segmentation result, can also guide node definition. Edges are estimated in static, dynamic as well as directed scenarios. We presented two major methods to compare groups of brain networks data, significance analysis, and network-based statistics. Combined with the brain atlas, whole-brain networks are characterized by graph theory attributes developed in network science. Network-based statistics enables the direct comparison of groups of brain networks. We also discussed the clinical application of rs-fMRI data analysis in neurorehabilitation, multiple system atrophy, and glioma patients. At last, future research directions are discussed, with an emphasis on network science, novel deep learning models, and individualized clinical applications.
