Trends in Cognitive Sciences
Volume 8, Issue 9, September 2004, Pages 418-425
Journal home page for Trends in Cognitive Sciences

Organization, development and function of complex brain networks

https://doi.org/10.1016/j.tics.2004.07.008Get rights and content

Recent research has revealed general principles in the structural and functional organization of complex networks which are shared by various natural, social and technological systems. This review examines these principles as applied to the organization, development and function of complex brain networks. Specifically, we examine the structural properties of large-scale anatomical and functional brain networks and discuss how they might arise in the course of network growth and rewiring. Moreover, we examine the relationship between the structural substrate of neuroanatomy and more dynamic functional and effective connectivity patterns that underlie human cognition. We suggest that network analysis offers new fundamental insights into global and integrative aspects of brain function, including the origin of flexible and coherent cognitive states within the neural architecture.

Section snippets

Structural organization of cortical networks

Most structural analyses of brain networks have been carried out on datasets describing the large-scale connection patterns of the cerebral cortex of rat [20], cat 21, 22, and monkey [23] – structural connection data for the human brain is largely missing [24]. These analyses have revealed several organizational principles expressed within structural brain networks. All studies confirmed that cerebral cortical areas in mammalian brains are neither completely connected with each other nor

Network growth and development

The physical structure of biological systems often reflects their assembly and function. Brain networks are no exception, containing structures that are shaped by evolution, ontogenetic development, experience-dependent refinement, and finally degradation as a result of brain injury or disease.

Intuitively plausible growth mechanisms have been proposed for the large classes of small-world [10] and scale-free networks [11]. Such topological algorithms, however, are not biologically realistic and

Scale-free functional brain networks

Dodel [51] developed a deterministic clustering method that combines cross-correlations between fMRI signal time courses, and elements of graph theory to reveal brain functional connectivity. Image voxels form nodes of a graph, and their temporal correlation matrix forms the weight matrix of the edges between the nodes. Thus a network can be implemented based entirely on fMRI data, defining as ‘connected’ those voxels that are functionally linked, that is correlated beyond a certain threshold rc

Conclusion: links between complex networks and cognition

Highly evolved neural structures like the mammalian cerebral cortex are complex networks that share several general principles of organization with other complex interconnected systems. These principles reflect systematic and global regularities in the structural interconnections and functional activations of brain areas. The work reviewed in this article has suggested some emerging links between network organization and cognition, illuminating the structural basis for the coexistence of

Acknowledgements

Work by O.S. was supported by US government contract NMA201–01-C-0034. The views, opinions and findings contained in this paper are those of the authors and should not be construed as official positions, policies or decisions of NGA or the US government. M.K. was supported by a fellowship from the German National Academic Foundation. D.C. was supported by Ministerio de Ciencia y Tecnologia (Spain) and FEDER (EU) through projects BFM200–11–0, BFM2001–0341-C02–01 and BFM2002–12792-E, as well as

Glossary: Graph theory and networks

For the following definitions of graph theory terms used in this review we essentially follow the nomenclature of ref. 4 (see also [27] for additional definitions and more detail). A Matlab toolbox allowing the calculation of these and other graph theory measures is available at http://www.indiana.edu/(cortex/connectivity.html.

Adjacency (connection) matrix:
The adjacency matrix of a graph is a n×n matrix with entries aij=1 if node j connects to node i, and aij=0 is there is no connection from

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