Скачать или смотреть Complex brain networks: graphtheoretical analysis of structural andfunctional systems

Complex brain networks: graph
theoretical analysis of structural and
functional systems
Ed Bullmore*‡ and Olaf Sporns§
Abstract | Recent developments in the quantitative analysis of complex networks, based
largely on graph theory, have been rapidly translated to studies of brain network organization.
The brain’s structural and functional systems have features of complex networks — such as
small-world topology, highly connected hubs and modularity — both at the whole-brain
scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we
review studies investigating complex brain networks in diverse experimental modalities
(including structural and functional MRI, diffusion tensor imaging, magnetoencephalography
and electroencephalography in humans) and provide an accessible introduction to the
basic principles of graph theory. We also highlight some of the technical challenges and key
questions to be addressed by future developments in this rapidly moving field.

Graph theory
A branch of mathematics that
deals with the formal
description and analysis of
graphs. A graph is defined
simply as a set of nodes
(vertices) linked by connections
(edges), and may be directed
or undirected. When describing
a real-world system, a graph
provides an abstract
representation of the system’s
elements and their interactions.

*University of Cambridge,
Behavioural & Clinical
Neurosciences Institute,
Department of Psychiatry,
Addenbrooke’s Hospital,
Cambridge, CB2 2QQ, UK.
‡GlaxoSmithKline,
Clinical Unit Cambridge,
Addenbrooke’s Hospital,
Cambridge, CB2 2QQ, UK.
§Department of Psychological
and Brain Sciences, Indiana
University, Bloomington,
Indiana 47405, USA.
Correspondence to E.B.
e‑mail: [email protected]
doi:10.1038/nrn2575
Published online
4 February 2009