The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales.
Kolchinsky A, Van Den Heuvel M, Griffa A, Hagmann P, Rocha L, Sporns O and Goñi J (2014). Multi-scale Integration and Predictability in Resting State Brain Activity. Front. Neuroinform. 8:66. http://dx.doi.org/10.3389/fninf.2014.00066