Comparing brain networks of different size and connectivity density using graph theory.

ABSTRACT: Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via fixed thresholds, average degrees or edge densities, and the use of weighted graphs.

SUBMITTER: van Wijk BC 

PROVIDER: S-EPMC2965659 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature