This study identifies and classifies hub regions in brain networks of macaque and cat cortices. Hub regions are defined as areas with high structural and functional importance, playing key roles in information flow coordination. The researchers examined structural motif distributions and centrality measures for high-degree regions, finding that a combination of degree, motif participation, betweenness, and closeness centrality reliably identifies hub regions. These hubs are classified as provincial (intra-cluster) or connector (inter-cluster) hubs. Lesioning provincial hubs decreases the small-world index, while lesioning connector hubs increases it. The study analyzed two data sets: macaque and cat cortices. Macaque cortex includes visual, somatosensory, and motor regions, while cat cortex includes isocortical regions. Structural motif frequency spectra for motifs of size M=3 were calculated, revealing a highly correlated pattern between macaque and cat cortices. Motif class M93 was overrepresented in both, with significant contributions from specific brain regions. Motif fingerprints were used to cluster brain regions with similar motif patterns, revealing distinct clusters in both species. High-degree areas in both species showed high apex ratios for motif M93, indicating their central role in information processing. These areas also exhibited high betweenness and closeness centrality, suggesting their importance in network connectivity. The study found that areas with high participation in motif M93 and high centrality are likely to be functionally classified as polysensory or multimodal. The classification of hubs into provincial and connector types was based on their connections within or between network modules. Provincial hubs are primarily connected within a single module, while connector hubs link multiple modules. Lesioning provincial hubs disrupts functional integration, while lesioning connector hubs increases the small-world index. The study highlights the importance of structural attributes in identifying and classifying hub regions. It suggests that hub regions with high structural centrality may play a key role in the robustness of brain networks. The findings have implications for understanding brain evolution and functional organization, as they suggest that structural and functional characteristics of hub regions may be conserved or elaborated during brain evolution. The study also emphasizes the potential of graph-theoretical descriptors in providing new insights into brain evolution beyond traditional measures like brain size or wiring length.This study identifies and classifies hub regions in brain networks of macaque and cat cortices. Hub regions are defined as areas with high structural and functional importance, playing key roles in information flow coordination. The researchers examined structural motif distributions and centrality measures for high-degree regions, finding that a combination of degree, motif participation, betweenness, and closeness centrality reliably identifies hub regions. These hubs are classified as provincial (intra-cluster) or connector (inter-cluster) hubs. Lesioning provincial hubs decreases the small-world index, while lesioning connector hubs increases it. The study analyzed two data sets: macaque and cat cortices. Macaque cortex includes visual, somatosensory, and motor regions, while cat cortex includes isocortical regions. Structural motif frequency spectra for motifs of size M=3 were calculated, revealing a highly correlated pattern between macaque and cat cortices. Motif class M93 was overrepresented in both, with significant contributions from specific brain regions. Motif fingerprints were used to cluster brain regions with similar motif patterns, revealing distinct clusters in both species. High-degree areas in both species showed high apex ratios for motif M93, indicating their central role in information processing. These areas also exhibited high betweenness and closeness centrality, suggesting their importance in network connectivity. The study found that areas with high participation in motif M93 and high centrality are likely to be functionally classified as polysensory or multimodal. The classification of hubs into provincial and connector types was based on their connections within or between network modules. Provincial hubs are primarily connected within a single module, while connector hubs link multiple modules. Lesioning provincial hubs disrupts functional integration, while lesioning connector hubs increases the small-world index. The study highlights the importance of structural attributes in identifying and classifying hub regions. It suggests that hub regions with high structural centrality may play a key role in the robustness of brain networks. The findings have implications for understanding brain evolution and functional organization, as they suggest that structural and functional characteristics of hub regions may be conserved or elaborated during brain evolution. The study also emphasizes the potential of graph-theoretical descriptors in providing new insights into brain evolution beyond traditional measures like brain size or wiring length.