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

By Robin A. A. Ince, Bruno L. Giordano, Christoph Kayser, Guillaume A. Rousselet, Joachim Gross, Philippe G. Schyns. bioRxiv, 2016

353 citations recorded by the source corpus.

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, uni-and multi-dimensional 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. Highlights Novel estimator for mutual information and other information theoretic quantities Provides general, efficient, flexible and robust multivariate statistical framework Validated statistical performance on EEG and MEG data Applications to spectral power and phase, 2D magnetic field gradients, temporal derivatives Interaction information relates information content in different responses

Subfields: Cognitive Neuroscience, Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/133c61552bdbecc6b12eef9be8fac037d2d3aee3

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Citations in the corpus

Neural interactions in the human frontal cortex dissociate reward and punishment learning (2024, methodology): “It allows to compute the MI on uni-and multivariate variables that can either be continuous or discrete (see Table I in (Ince et al., 2017)).”
Distinct developmental changes in linear and nonlinear neural interactions across infancy and adulthood (2025, unknown): “…towards information-theoretic approaches to study brain interactions (Blume et al., 2024; Canales-Johnson et al., 2023; Chidichimo et al., 2025; Ince et al., 2017; Roberts et al., 2025) that capture nonlinear transformation across regions, independent of oscillatory synchrony or spectral…”
Task‐specific topology of brain networks supporting working memory and inhibition (2024, methodology): “Within each interval the Gaussian-Copula mutual-information estimator (Ince et al., 2017) was used to calculate the functional connectivity for each stimulus that was then averaged within each interval to obtain the averaged connectivity matrix for the interval.”