Evidence map›Paper›PMID 27860095›Full record

ArticleHuman brain mapping2017

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

Robin A A Ince, Bruno L Giordano, Christoph Kayser, Guillaume A Rousselet, Joachim Gross, Philippe G Schyns

Abstract read
In one paragraph

Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 156 papers.

0numbers the graph read from it
0cells of the map it votes in
156citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

156 citing papers in PubMed.

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  10. Metabolism-weighted brain connectome reveals synaptic integration and vulnerability to neurodegeneration.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  14. Review
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  16. Delayed, Reduced, and Redundant: Information Processing of Prediction Errors during Human Sleep.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
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  17. Review
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96 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Robin A A InceInstitute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
Bruno L GiordanoInstitute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
Christoph KayserInstitute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
Guillaume A RousseletInstitute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
Joachim GrossInstitute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
Philippe G SchynsInstitute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.

Funding

Biotechnology and Biological Sciences Research Council BB/D01400X1Biotechnology and Biological Sciences Research Council BB/J018929/1Biotechnology and Biological Sciences Research Council BB/L027534/1Biotechnology and Biological Sciences Research Council BB/M009742/1Wellcome Trust 098433Wellcome Trust 107802
6 · The paper itself

Abstract

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc.

Indexed as

Brain MappingInformation TheoryNormal DistributionBrainComputer SimulationElectroencephalographyEntropyHumansNeuroimagingSensitivity and Specificityconditional mutual informationcopulaEEGinteraction informationMEGmultivariate statisticsmutual informationredundancysynergy

Identifiers

PMID27860095
PMCPMC5324576

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.