Multi-scale integration and predictability in resting state brain activity

Artemy Kolchinsky, Martijn P. Van den Heuvel, Alessandra Griffa, Patric Hagmann, Luis M. Rocha, Olaf Sporns, Joaquín Goñi*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Article number66
JournalFrontiers in Neuroinformatics
Issue numberJULY
Publication statusPublished - 1 Jan 2014
Externally publishedYes


  • Complexity measures
  • Human connectome
  • Information theory
  • Integrative regions
  • Multivariate mutual information
  • Resting-state


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