  Structure entails function and thus a structural description of the brain
will help to understand its function and may provide insights into many
properties of brain systems, from their robustness and recovery from damage, to
their dynamics and even their evolution. Advances in the analysis of complex
networks provide useful new approaches to understanding structural and
functional properties of brain networks. Structural properties of networks
recently described allow their characterization as small-world, random
(exponential) and scale-free. They complement the set of other properties that
have been explored in the context of brain connectivity, such as topology,
hodology, clustering, and hierarchical organization. Here we apply new network
analysis methods to cortical inter-areal connectivity networks for the cat and
macaque brains. We compare these corticocortical fibre networks to benchmark
rewired, small-world, scale-free and random networks, using two analysis
strategies, in which we measure the effects of the removal of nodes and
connections on the structural properties of the cortical networks. The brain
networks' structural decay is in most respects similar to that of scale-free
networks. The results implicate highly connected hub-nodes and bottleneck
connections as structural basis for some of the conditional robustness of brain
systems. This informs the understanding of the development of brain networks'
connectivity.
