A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.
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DOI：
10.1002/hbm.23471
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发表时间：
2017-03
影响因子：
4.8
通讯作者：
Schyns PG
中科院分区：
文献类型：
--
作者：
Ince RA;Giordano BL;Kayser C;Rousselet GA;Gross J;Schyns PG
我们首先回顾适用于神经影像数据分析的信息论统计框架。阻碍该框架在神经影像学中更广泛采用的一个主要因素是在实践中估计信息理论量的困难。我们提出了一种新颖的估计技术，它将联结统计理论与高斯变量熵的封闭形式解相结合。这产生了一个通用的、计算高效的、灵活的、鲁棒的多元统计框架，该框架在共同有意义的尺度上提供效应大小，允许对离散、连续、一维和多维变量进行统一处理，并能够直接比较任何记录模式的行为和大脑反应的表示。我们验证了使用该估计作为神经影像学背景下的统计测试，同时考虑离散刺激类别和连续刺激特征。我们还介绍了这些发展所促进的分析示例，包括对 MEG 平面磁场梯度的多变量分析的应用，以及诱发 EEG 反应中的成对时间相互作用。我们展示了将瞬时时间导数与 M/EEG 信号的原始值一起考虑为多元响应的好处，我们如何单独量化向量的幅度和方向的调制，以及我们如何测量诱发反应中随着时间的推移新信息的出现。本文附带实现新方法的开源 Matlab 和 Python 代码。 Hum Brain Mapp 38:1541–1573, 2017。© 2016 Wiley periodicals, Inc.
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.