Elsevier

NeuroImage

Volume 92, 15 May 2014, Pages 381-397
NeuroImage

Permutation inference for the general linear model

https://doi.org/10.1016/j.neuroimage.2014.01.060Get rights and content
Under a Creative Commons license
open access

Highlights

  • Permutation for the GLM in the presence of nuisance or non-independence.

  • A generalised statistic that performs well even under heteroscedasticity.

  • Permutation and/or sign-flipping, exchangeability blocks and variance groups.

  • The “randomise” algorithm, as well as various practical examples.

Abstract

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm.

Keywords

Permutation inference
Multiple regression
General linear model
Randomise

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