Artemy Kolchinsky1,4, Martijn P Van Den Heuvel2, Alessandra Griffa3, Patric Hagmann3, Luis M. Rocha1,4, Olaf Sporns1, and Joaquin Goni1
1Indiana University, USA
2 University Medical Center, Netherlands
3 Ecole Polytechnique Fédérale de Lausanne, Switzerland
4Instituto Gulbenkian de Ciência, Portugal
Citation: A. Kolchinsky, M. P. Van Den Heuvel, A. Griffa, P. Hagmann, L.M. Rocha, O. Sporns, J. Goni [2014]. "Multi-scale Integration and Predictability in Resting State Brain Activity". Frontiers in Neuroinformatics, 8:66. doi: 10.3389/fninf.2014.00066.
The full text and pdf re-print are available from the Frontiers in Neuroinformatics site. Due to mathematical notation and graphics, only the abstract is presented here.
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: Luis M. Rocha, Drug-drug interaction, literature mining, text mining, text classification, machine learning, pharmacokinetics, translational science