Human brain function is the result of a highly organized network of connections linking unique areas across the brain. While much research has been devoted to identifying the specialization of distinct functional areas, recent work has argued that we may be able to significantly supplement our current understanding of brain function by characterizing it in terms of its underlying network organization. The mathematical discipline of graph theory is a powerful tool that has recently been used to conduct this work. One area of interest receiving considerable attention is the detection and characterization of community structure in networks. Community structure refers to the appearance of densely connected groups of nodes (i.e. brain regions), with only sparse connections between the groups. The ability to detect such groups has been of significant practical importance for understanding the nature of complex systems, including the identification of large-scale brain systems and it relation to complex behavior. Another such topology recently identified in the healthy human brain is the so-called rich-club organization. This phenomenon describes a system organization whereby the most highly connected nodes within a network show a strong tendency to connect with other highly connected nodes. We argue that illumination of such phenomena will likely have significant practical importance for understanding the nature of typical development and to identifying the etiologic underpinnings of atypical developmental trajectories, such as children with ADHD and Autism Spectrum Disorders.