Mar 16, 2016

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

BioRxiv : the Preprint Server for Biology
Robin A A IncePhilippe G Schyns

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, 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 novel 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 te...Continue Reading

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Mentioned in this Paper

Diagnostic Radiology Modality
Electroencephalography
Copula
Statistical Test
Term Source
Genetic Vectors
Brain Function
Copula (Body Structure)
Neuroimaging
Analysis

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