Original Research ARTICLE

Front. Neuroinform., 24 July 2014 | doi: 10.3389/fninf.2014.00066

Multi-scale integration and predictability in resting state brain activity

  • 1Department of Informatics, School of Informatics and Computing, Indiana University, Bloomington, IN, USA
  • 2Instituto Gulbenkian de Ciência, Oeiras, Portugal
  • 3Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, Netherlands
  • 4Signal Processing Laboratory 5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
  • 5Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
  • 6Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA

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: human connectome, resting-state, integrative regions, information theory, multivariate mutual information, complexity measures

Citation: Kolchinsky A, van den Heuvel MP, Griffa A, Hagmann P, Rocha LM, Sporns O and Goñi J (2014) Multi-scale integration and predictability in resting state brain activity. Front. Neuroinform. 8:66. doi: 10.3389/fninf.2014.00066

Received: 17 January 2014; Accepted: 26 June 2014;
Published online: 24 July 2014.

Edited by:

Daniele Marinazzo, University of Ghent, Belgium

Reviewed by:

Petra Ritter, Charité - Universitätsmedizin Berlin, Germany
Jaroslav Hlinka, Academy of Sciences of the Czech Republic, Czech Republic

Copyright © 2014 Kolchinsky, van den Heuvel, Griffa, Hagmann, Rocha, Sporns and Goñi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Joaquín Goñi, Department of Psychological and Brain Sciences, Indiana University, 1101 E 10th St., Bloomington, IN 47405, USA e-mail: jgonicor@indiana.edu

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