Journal of Computational Neuroscience

, Volume 30, Issue 1, pp 85–107 | Cite as

Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity

  • Joseph T. LizierEmail author
  • Jakob Heinzle
  • Annette Horstmann
  • John-Dylan Haynes
  • Mikhail Prokopenko


The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.


fMRI Visual cortex Motor cortex Movement planning Information transfer Transfer entropy Information structure Neural computation 



JL and JH thank Thorsten Kahnt for discussions on the statistical analysis. JL thanks Mikail Rubinov for helpful suggestions. JL thanks the Australian Research Council Complex Open Systems Research Network (COSNet) for a travel grant that partially supported this work. JDH thanks the Max Planck Society, the Bernstein Computational Neuroscience Program of the German Federal Ministry of Education and Research (BMBF Grant 01GQ0411) and the Excellence Initiative of the German Federal Ministry of Education and Research (DFG Grant GSC86/1-2009). MP is grateful for a 2009 Research Grant from The Max Planck Institute for Mathematics in the Sciences (Leipzig, Germany) on Information-driven Self-Organization and Complexity Measures.

Author contributions: J.-D.H., J.H. and A.H. conceived the fMRI experiment. A.H. performed the fMRI experimental work. J.H. and A.H. pre-processed the data. J.L. and M.P. conceived the information-theoretical analysis. J.L. performed the information-theoretical analysis. J.H. performed the statistical analysis. J.L. and J.H. wrote the paper.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Joseph T. Lizier
    • 1
    • 2
    Email author
  • Jakob Heinzle
    • 3
  • Annette Horstmann
    • 4
  • John-Dylan Haynes
    • 3
    • 4
    • 5
  • Mikhail Prokopenko
    • 2
    • 6
  1. 1.School of Information TechnologiesThe University of SydneySydneyAustralia
  2. 2.CSIROInformation and Communications Technology CentreEppingAustralia
  3. 3.Bernstein Center for Computational NeuroscienceCharité-Universitätsmedizin BerlinBerlinGermany
  4. 4.Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
  5. 5.Graduate School of Mind and BrainHumboldt Universität zu BerlinBerlinGermany
  6. 6.Max Planck Institute for Mathematics in the SciencesLeipzigGermany

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