**1. Introduction**

Magnetic resonance imaging (MRI) is a multimodal technique that can noninvasively reflect the structure and function of the human brain. Structural MRI (sMRI), including longitudinal (spin-lattice) relaxation time T1-weighted and transverse (spin-spin) relaxation time T2-weighted imaging, has been applied to investigate the structural features of the brain. Based on the different relaxation times of different tissue, T1-weighted and T2-weighted images can be used to reflect the volume of grey matter, white matter, as well as lesions caused by infarction or hemorrhage. Diffusion MRI (dMRI), such as diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI), can be used to measure water diffusion along different directions and tract neural fiber counts and orientation. Functional MRI (fMRI) reflects neural activity during a period of time by measuring the relative amount of deoxygenated hemoglobin and oxygenated hemoglobin in the blood flow, which is also called

the blood-oxygen-level-dependent (BOLD) signal. The fMRI is becoming popular in clinical situations to investigate the functional alterations following disease or treatment.

The fMRI experiment can be categorized into task fMRI and resting-state fMRI (rs-fMRI). For task fMRI, subjects need to perform a specific task, such as finger tapping or receive external stimulation like heat or sound during the scanning session. Resting-state fMRI, on the other hand, is collected when the subject lies still in the scanner, without doing any movement or thinking anything particular, and keeping awake at all time. Researchers focus on the spontaneous neural activity reflected by the BOLD signal under resting conditions. The correlation of signals related to spatially distinct regions is commonly defined as functional connectivity (FC) [1].

In the recent two decades, several methods have been developed to analyze functional connectivity in the resting state, including seed-based analysis, independent component analysis (ICA) [2], and resting-state network (RSN) method [3, 4]. The network method characterizes brain spontaneous activity as a graph, where nodes are defined as brain regions and edges are represented as connectivity between regions. There are different ways to calculate the connectivity, including static and dynamic functional connectivity and directed connectivity. Furthermore, features proposed in network science can be adopted to characterize the brain network topology, such as graph theory attributes [5].

Resting-state fMRI has been applied to clinical research and applications [6, 7]. In clinical situations, a common research paradigm is performing group comparison and searching for inter-group significant different features. Researchers are interested in whether a group of patients is significantly different from a group of healthy controls, or whether the same group of patients shows significant recovery after treatment. The identified significant different features may be the potential biomarker to aid in diagnosis as well as treatment. More importantly, the location of the significant different feature is of great interest, since each brain region has its unique function. As a result, this requires comparing groups of brain networks and other extracted network features. In clinical research, there are two key techniques of brain network analysis, the method of network construction and significant difference analysis of groups of brain networks.

In the following sections, we first describe how to construct brain networks from resting-state fMRI data, including different node definitions and a range of connectivity measurements. Then, we present common group analysis methods of brain networks. The clinical application of brain network analysis is also reported. We also propose several future directions in brain network research and end the chapter with a conclusion.
