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Neuroscientists face the challenge of explaining how functional brain states emerge from the interactions of dozens, perhaps hundreds, of brain regions, each containing millions of neurons. Much evidence supports the view that highly evolved nervous systems are capable of rapid, real-time integration of information across segregated sensory channels and brain regions. This integration happens without the need for a central controller or executive: It is the functional outcome of dynamic interactions within and between the complex structural networks of the brain. In this issue of PNAS, the study by Bassett et al. (1) reveals the existence of large-scale functional networks in magnetoencephalographic (MEG) recordings with attributes that are preserved across multiple frequency bands and that flexibly adapt to task demands. These networks exhibit “small-world” structure, i.e., high levels of clustering and short path lengths. The authors' analysis reveals that the small-world topology of brain functional networks is largely preserved across multiple frequency bands and behavioral tasks. The structure of networks has been analyzed extensively in the social sciences (2) and in physics and information technology (3). In the life sciences, network approaches already have provided quantitative insights into cellular metabolism and transcriptional regulation (4). In neuroscience, researchers have examined the structure of axonal networks connecting individual neurons (5, 6) and whole-brain networks of interregional pathways (7–9). Across these systems and disciplines, network analysis is founded on the graph-theoretic characterization of a network in terms of nodes and connections (vertices and edges). A landmark study by Watts and Strogatz (10) revealed that a disparate set of natural and artificial networks shared small-world attributes. The canonical small-world network is one in which the majority of …

Small worlds inside big brains