title: Small-world brain networks.
abstract: Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.
authors.affiliation: Brain Mapping Unit, University of Cambridge, Department of Psychiatry, Addenbrooke's Hospital, Cambridge, United Kingdom.
authors.forename: Danielle Smith
authors.initials: DS
authors.lastname: Bassett
authors.forename: Ed
authors.initials: E
authors.lastname: Bullmore
mesh_terms: D000818:Animals; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D001931:Brain Mapping; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology*