%0 Journal Article
%T Multi-scale integration and predictability in resting state brain activity
%A Artemy Kolchinsky
%A Martijn P. van den Heuvel
%A Alessandra Griffa
%A Patric Hagmann
%A Luis M. Rocha
%A Olaf Sporns
%A Joaqu¨ªn Go£¿i
%J Frontiers in Neuroinformatics
%D 2014
%I Frontiers Media
%R 10.3389/fninf.2014.00066
%X 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.
%K human connectome
%K resting-state
%K integrative regions
%K information theory
%K multivariate mutual information
%K complexity measures
%U http://www.frontiersin.org/Journal/10.3389/fninf.2014.00066/abstract