The structural network of the human brain can be constructed based on structural MRI images (morphological indicators of gray matter, such as cortical thickness, cortical curvature area, etc.; process shown by blue arrows) and diffusion MRI images (white matter fiber bundles, process shown by green arrows), respectively. Functional networks of the human brain can be based on functional MRI images (time series of functional brain activity, flow shown by red arrows) and EEG/magnetic brain signals (flow shown by yellow arrows), respectively. Network node definition: Structural, diffusion, and functional MRI data require network nodes to be defined using a priori mapping of brain regions or image voxels, while EEG/EMG data are directly based on recording electrodes/channels as network nodes. Network connections (edges) are defined as statistical relationships between morphological indicators of network nodes for structural MRI data, anatomical connections between network nodes are determined by deterministic or probabilistic fiber-tracking techniques for diffusion MRI data, and network connections can be measured by Pearson correlation, partial correlation, and simultaneous likelihood for functional MRI and EEG/EMM data. The statistical relationships between the neural activity signals of The correlation matrix obtained in step 3 can be binarized to obtain binary matrices at different thresholds, i.e., structural and functional brain networks. To date, structural magnetic resonance imaging has been used extensively to study morphological changes in local brain regions during normal development, aging, and disease in the human brain. Notably, several studies have now found that the morphological data of the human brain contains a large amount of information on brain connectivity. In 2005, Mechelli et al. in the United Kingdom used structural magnetic resonance imaging to find coordinated changes in gray matter density between certain brain regions (e.g., the two hemispheres of the brain), and the researchers speculated that this coordination might be related to the bundles of white matter fibers (corpus callosum) that connect them. In 2006, Lerch et al. found that the Broca and Wernicke areas of the cerebral cortex (two language-related brain areas) have a very high coordination in gray matter cortical thickness. To this end, they constructed a correlation map of cortical thickness that showed striking similarity to the map of the arcuate fasciculus (white matter fibers connecting Broca and Wernicke areas) in the human brain. Although the exact physiological significance of the morphological correlation between brain regions is not yet clear, several studies have suggested that this coordinated variation in morphological features may be related to congenital genetics and acquired plasticity. In 2007, He et al. successfully constructed the first structural network of the human brain by examining the correlation between the cortical thicknesses of 54 brain regions in the cerebral cortex using structural imaging data from 124 individuals, and found that the network has “small-world” properties and its nodal degree distribution follows an exponentially truncated power-law distribution. This study was the first to propose the idea of using morphological indicators to construct a structural connectivity group of the brain, and confirmed the “small-world” property of the human brain structural network constructed by cortical thickness correlation, which provided a new way to describe the structural connectivity network of the living human brain. In 2008, Chen et al. further found that the cortical thickness network has an organizational pattern corresponding to the functional modules of the human brain (e.g., language, memory, and vision), suggesting that the cortical thickness correlation changes are mainly located in different functional modules. Furthermore, by comparing the betweenness of nodes and edges in the network, it was found that the core nodes of the cortical thickness network are mainly located in the parietal, temporal and frontal joint cortical regions, while most of the important pathways in the network are connected to the core nodes in different modules. In 2008, Schmitt et al. established a genetic correlation matrix of brain structures in 600 children subjects and found that the structural connections between different brain regions regulated by genetic factors formed a complex network with “small world” properties, and the core brain regions in this network were mainly located in the superior frontal gyrus, middle frontal gyrus, precentral gyrus and postcentral gyrus. The core brain regions in this network are mainly located in the superior frontal gyrus, middle frontal gyrus, precentral gyrus and postcentral gyrus. Lenroot et al. investigated the effect of brain development on the topological properties of the structural (cortical thickness) network using longitudinal structural data from 787 healthy subjects using the method proposed by He et al. The data set was divided into three age groups, namely, children (mean age 6.9 years), adolescents (mean age 11 years) and young adults (mean age 16.4 years). The clustering coefficients and hierarchical properties of the network increased significantly with age, suggesting that brain development is closely related to the regional differentiation of brain structures. The above study based on structural magnetic resonance data shows that the structural brain network has “small-world” properties and modular structure and other topological properties. The method of constructing structural brain networks based on morphological data provides a simple and effective way to describe the structural connectivity patterns of the human brain, which not only helps to reveal the topological patterns of structural networks of the human brain, but also provides an experimental basis to explore the interrelationship between structure and function of the human brain. It should be noted that there are some limitations in this method of constructing structural brain networks based on morphological data. For example, the current structural networks of brain morphology usually describe the connectivity patterns between whole brain regions (e.g., N<200), and n="">10000), can the above-mentioned network properties at the region level still be found? And what relationships exist between these different levels of brain networks?