Journal
NEUROIMAGE
Volume 92, Issue -, Pages 381-397Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2014.01.060
Keywords
Permutation inference; Multiple regression; General linear model; Randomise
Funding
- GlaxoSmithKline plc
- Marie Curie ITN
- Medical Research Council [MR/J014257/1]
- Wellcome Trust [091593/Z/ 10/Z, 100309/Z/12/Z, 098369/Z/12/Z]
- MRC [G0900908]
- NIH [R01 EB015611-01]
- MRC [MR/J014257/1, G0900908] Funding Source: UKRI
- Medical Research Council [G0900908, MR/J014257/1] Funding Source: researchfish
- Wellcome Trust [100309/A/12/Z] Funding Source: researchfish
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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 am 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 am. (C) 2014 The Authors. Published by Elsevier Inc.
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