This paper focuses on identifying and classifying hub regions in brain networks, which are crucial for coordinating information flow. The authors examine the cerebral cortices of both cat and macaque monkeys, using motif fingerprints and centrality indices to characterize the network contributions of all regions. They find that a combination of degree, motif participation, betweenness centrality, and closeness centrality reliably identifies hub regions, many of which are functionally classified as polysensory or multimodal. Hubs are classified into two types: provincial hubs, which link vertices within a single module, and connector hubs, which link different modules. Lesioning provincial hubs decreases the small-world index, while lesioning connector hubs increases it. The study provides a method for identifying and classifying hub regions based on multiple network attributes and suggests potential links between their structural embedding and functional roles.This paper focuses on identifying and classifying hub regions in brain networks, which are crucial for coordinating information flow. The authors examine the cerebral cortices of both cat and macaque monkeys, using motif fingerprints and centrality indices to characterize the network contributions of all regions. They find that a combination of degree, motif participation, betweenness centrality, and closeness centrality reliably identifies hub regions, many of which are functionally classified as polysensory or multimodal. Hubs are classified into two types: provincial hubs, which link vertices within a single module, and connector hubs, which link different modules. Lesioning provincial hubs decreases the small-world index, while lesioning connector hubs increases it. The study provides a method for identifying and classifying hub regions based on multiple network attributes and suggests potential links between their structural embedding and functional roles.