|
Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions
and the edges their functional connectivity. Recent studies showed that these networks present a robust small world topological structure,
characterized by highly integrated modules connected sparsely by long range links. On the other hand, other large-scale properties such as the degree distribution and the presence (or absence) of a hierarchical structure show different intriguing behaviors. In this work we present a simple model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions in the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of different human brain networks obtained from functional magnetic resonance imaging (fMRI) studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network a scale-free non-hierarchical network with well defined modules emerges. On the other hand, when the dynamical rules restrict the information to a local neighborhood, modules cluster together into larger modules, giving rise to a hierarchical structure, with a truncated power law degree distribution. |
|