Trends in Cognitive Sciences
Organization, development and function of complex brain networks
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
- 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
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.
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