English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

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

Lizier, J. T., Heinzle, J., Horstmann, A., Haynes, J.-D., & Prokopenko, M. (2011). Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity. Journal of Computational Neuroscience, 30(1), 85-107. doi:10.1007/s10827-010-0271-2.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0011-27CE-E Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-FF9B-A
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Lizier, Joseph T.1, 2, Author
Heinzle, Jakob3, Author
Horstmann, Annette4, Author              
Haynes, John-Dylan3, 5, 6, Author              
Prokopenko, Mikhail2, 7, Author
Affiliations:
1School of Information Technologies, University of Sydney, Australia, ou_persistent22              
2Commonwealth Scientific and Industrial Research Organisation (CSIRO), Epping, Australia, ou_persistent22              
3Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
4Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              
5Max Planck Fellow Research Group Attention and Awareness, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634553              
6Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
7Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany, ou_persistent22              

Content

show
hide
Free keywords: fMRI; Visual cortex; Motor cortex; Movement planning; Information transfer; Transfer entropy; Information structure; Neural computation
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2011-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 537837
DOI: 10.1007/s10827-010-0271-2
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of Computational Neuroscience
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Boston : Kluwer Academic Publishers
Pages: - Volume / Issue: 30 (1) Sequence Number: - Start / End Page: 85 - 107 Identifier: ISSN: 0929-5313
CoNE: https://pure.mpg.de/cone/journals/resource/954925568787