One thing they noticed was a bias towards movement in the medial/lateral as opposed to and anterior/posterior axes of the flattened cortical hemisphere. They quantify this bias as anisotropy, using two delta functions defined on a circle. (Anisotropy is big in diffusion tensor imaging, too). When they compare the anisotropy in the development of grey matter vs white matter, they find that white matter is more anisotropic:
By analyzing simulated networks they show the effects of anisotropy on growing axon connections to other nodes:
They also consider the modularity of their networks. Formally, modules are non-overlapping communities delineated by their location. If chosen well, there should be more within- than between-community edges in a given module than expected due to chance. The authors find good evidence for modularity in their axon traces, mainly because there are so many short connections, which are increased when axons are more anisotropic.
This is a great way to quantify networks, and it would be nice to see this type of structural data correlated with function. For example, how do more modular networks act? One suggestion is that modular structures might lead to more specialization in sub-problems, increasing rapid adaptation to a specified goal. More modular tasks may take less effort, whereas more global tasks like working memory would take more effort.
This makes sense, but what’s the trade-off or downside to modularity? If modularity is so good, why isn’t the brain more modular? Possibly because given finite resources, specialization is antagonistic to plasticity.
Cahalane DJ, Clancy B, Kingsbury MA, Graf E, Sporns O, et al. (2011) Network Structure Implied by Initial Axon Outgrowth in Rodent Cortex: Empirical Measurement and Models. PLoS ONE 6(1): e16113. doi:10.1371/journal.pone.0016113
Meunier D, et al. 2010 Modular and hierarchically modular organization of brain networks. Frontiers in Neuro, link.