Permutation inference for the general linear model

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: General linear model; Multiple regression; Permutation inference; Randomise.

Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Fig. 1

Examples of a permutation matrix (a), of a sign flipping matrix (b), and of a matrix that does permutation and sign flipping (c). Pre-multiplication by a permutation matrix shuffles the order of the data, whereas by a sign flipping matrix changes the sign of a random subset of data points.

Fig. 2

Left: Example of a permutation matrix that shuffles data within block only. The blocks are not required to be of the same size. The elements outside the diagonal blocks are always equal to zero, such that data cannot be swapped across blocks. Right: Example of a sign flipping matrix. Differently than within-block permutation matrices, here sign flipping matrices are transparent to the definitions of the blocks, such that the block definitions do not need to be taken into account, albeit their corresponding variance groups are considered when computing the statistic.

Fig. 3

(a) Example of a permutation matrix that shuffles whole blocks of data. The blocks need to be of the same size. (b) Example of a sign flipping matrix that changes the signs of the blocks as a whole. Both matrices can be constructed by the Kronecker product (represented by the symbol ⊗) of a permutation or a sign flipping matrix (with size determined by the number of blocks) and an identity matrix (with size determined by the number of observations per block).

Fig. 4

Heatmaps for the comparison of the distributions obtained under different variance settings for identical sample sizes. In each map, the cells below the main diagonal contain the results for the pairwise F statistic, and above, for the G statistic. The percentages refer to the fraction of the 1000 tests in which the distribution of the statistic for one variance setting was found different than for another in the same simulation scenario. Each variance setting is indicated by letters (a–e), corresponding to the same letters in Table 5. Smaller percentages indicate robustness of the statistic to heteroscedasticity. Confidence intervals (95%) are shown in parenthesis.