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

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 m

SUBMITTER: Ince RA 

PROVIDER: S-EPMC5324576 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature