Abstract: Advancements in neuroimaging technologies, particularly diffusion MRI, now allow reconstructing the long-range fiber connection patterns in the human brain. We study the network whose connection weights are determined by the number of fibers between individual brain regions. We study networks from the Human Connectome Project (HCP) and networks extracted from individual imaging subjects through a data processing pipeline developed in our group. By applying an algorithm to detect highly connected “communities” in these networks, we find a discrete set of communities appear robustly in the human brain. A specific community in the occipital lobe systematically displays high eigenvector centrality (EVC), a measure of the influence of nodes within a network. We explore the variations in these network structures among individuals and in retested subjects to isolate the sources of inter-individual variability. This result consistently appears across nearly all subjects and in a test-retest dataset. Similar community structure also appears in connectomes from macaque and marmoset brains, but the existence of an occipital lobe community with high EVC is specific to human connectomes. Taken together, these results reveal novel organization in the structural connectivity of the brain, derived from a fully data-driven approach, where clear community organization appears. This community organization relates to known functional divisions, such as visual and auditory sensory pathways, but also reveals community structure within higher-order areas, whose functional relevance can be studied in future work.