The human brain is an enormous conundrum composed of billions of neurons with trillions of connections. The functional brain network is extremely complicated, with multiple statistical, structural, and dynamic features. Complex networks theory provides a sensible and robust technique for understanding and analyzing the functions and structures of complex systems, including the brain. This paper investigates a functional brain network based on the large resting-state fMRI dataset to discover its features using complex networks theory and methodologies at various spatial resolutions. The resting-state functional brain network follows a broad-scale distribution, which contains both small-world and scale-free features besides its community structure. However, the network's degree and betweenness are largely varied among different scales, yet the majority of the other complex brain-network measures are primarily conserved.
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