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

Overview of attention for article published in Journal of Computational Neuroscience, August 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 211)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

news
1 news outlet
twitter
1 tweeter
wikipedia
1 Wikipedia page

Citations

dimensions_citation
104 Dimensions

Readers on

mendeley
194 Mendeley
citeulike
2 CiteULike
Title
Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity
Published in
Journal of Computational Neuroscience, August 2010
DOI 10.1007/s10827-010-0271-2
Pubmed ID
Authors

Joseph T. Lizier, Jakob Heinzle, Annette Horstmann, John-Dylan Haynes, Mikhail Prokopenko

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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 194 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 6%
Germany 7 4%
United Kingdom 5 3%
Sweden 2 1%
Poland 2 1%
Netherlands 2 1%
Brazil 2 1%
Canada 2 1%
Japan 2 1%
Other 4 2%
Unknown 154 79%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 26%
Researcher 46 24%
Student > Master 24 12%
Professor > Associate Professor 15 8%
Professor 14 7%
Other 45 23%
Readers by discipline Count As %
Engineering 30 15%
Psychology 29 15%
Computer Science 28 14%
Unspecified 26 13%
Agricultural and Biological Sciences 25 13%
Other 56 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 August 2014.
All research outputs
#948,730
of 12,320,131 outputs
Outputs from Journal of Computational Neuroscience
#5
of 211 outputs
Outputs of similar age
#9,769
of 128,636 outputs
Outputs of similar age from Journal of Computational Neuroscience
#1
of 5 outputs
Altmetric has tracked 12,320,131 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 211 research outputs from this source. They receive a mean Attention Score of 2.5. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 128,636 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them