Frontiers in Neuroinformatics (Jul 2014)

Multi-scale Integration and Predictability in Resting State Brain Activity

  • Artemy eKolchinsky,
  • Artemy eKolchinsky,
  • Martijn P. eVan Den Heuvel,
  • Alessandra eGriffa,
  • Patric eHagmann,
  • Luis M. eRocha,
  • Luis M. eRocha,
  • Olaf eSporns,
  • Joaquin eGoñi

DOI
https://doi.org/10.3389/fninf.2014.00066
Journal volume & issue
Vol. 8

Abstract

Read online

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.

Keywords