The article by Stam and Reijneveld explores the application of graph theory to the study of complex networks in the brain. It highlights the significant progress made in understanding the relationship between the structural properties of networks and the dynamics that occur on these networks, particularly in the context of synchronizability in coupled oscillator systems. The authors discuss how graph analysis has been applied to models of neural networks, anatomical connectivity, and functional connectivity based on fMRI, EEG, and MEG data. They suggest that the human brain can be modeled as a complex network with a small-world structure, which is hypothesized to optimize rapid synchronization, information transfer, and balance between local processing and global integration. The topological structure of functional networks is influenced by genetic and anatomical factors but can be modified during tasks. Additionally, the article reviews the historical development of network theory, including the discovery of small-world and scale-free networks, and discusses various measures used to characterize network properties, such as clustering coefficients, path lengths, and degree distributions. The authors also review studies that apply network theory to neuroscience, focusing on the dynamics of simulated neural networks, neuroanatomical networks, and the relationship between anatomical and functional connectivity.The article by Stam and Reijneveld explores the application of graph theory to the study of complex networks in the brain. It highlights the significant progress made in understanding the relationship between the structural properties of networks and the dynamics that occur on these networks, particularly in the context of synchronizability in coupled oscillator systems. The authors discuss how graph analysis has been applied to models of neural networks, anatomical connectivity, and functional connectivity based on fMRI, EEG, and MEG data. They suggest that the human brain can be modeled as a complex network with a small-world structure, which is hypothesized to optimize rapid synchronization, information transfer, and balance between local processing and global integration. The topological structure of functional networks is influenced by genetic and anatomical factors but can be modified during tasks. Additionally, the article reviews the historical development of network theory, including the discovery of small-world and scale-free networks, and discusses various measures used to characterize network properties, such as clustering coefficients, path lengths, and degree distributions. The authors also review studies that apply network theory to neuroscience, focusing on the dynamics of simulated neural networks, neuroanatomical networks, and the relationship between anatomical and functional connectivity.