Winkler et al. - Faster permutation inference in brain imaging: Supplementary material

Winkler, A

Documentation:: This is the accompanying Supplementary Material for the paper "Faster permutation inference in brain imaging", published in the journal Neuroimage by Winkler et al. (2016) [Open Access]. The complete description and context are in the paper itself. The DOI for the paper is 10.1016/j.neuroimage.2016.05.068.

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APA Style

Winkler, A. (2016). Winkler et al. - Faster permutation inference in brain imaging: Supplementary material. University of Oxford.

MLA Style

Winkler, A. Winkler Et Al. - Faster Permutation Inference in Brain Imaging: Supplementary Material. University of Oxford, 2016.

Chicago Style

Winkler, A. 2016. Winkler Et Al. - Faster Permutation Inference in Brain Imaging: Supplementary Material. University of Oxford.
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Files:: Winkler_etal_NeuroImage_Suppl.tar.gz

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Authors/Creators

+ Winkler, A More by this author/creator

Department:: University of Oxford
Role:: Creator, Researcher

Contributors

+ Ridgway, G

Department:: University of Oxford
Role:: Researcher, Contributor

+ Douaud, G

Department:: University of Oxford
Role:: Researcher, Contributor

+ Nichols, T

Department:: University of Warwick
Role:: Contributor, Researcher, Supervisor

+ Smith, S

Department:: University of Oxford
Role:: Principal Investigator (PI), Supervisor, Contributor

+ Medical Research Council More from this funder

Grant:: MR/J014257/2; MR/K006673/1; G0900908; MR/K013599/1

+ Wellcome Trust More from this funder

Grant:: 098369/Z/12/Z; 100309/Z/12/Z

+ National Research Council of Brazil (CNPq) More from this funder

Grant:: 211534/2013- 7

Publisher:: University of Oxford
Publication date:: 2016
Version number:: 1
DOI:: 10.5287/bodleian:v0wY6e6Y0

Language:: English
Keywords:: gamma distribution

negative binomial distribution

statistical inference

tail approximation

brain imaging

Pearson type III distribution

low rank matrix completion

permutation tests

acceleration

generalised Pareto distribution
Subjects:: Statistics

Brain--Imaging
Pubs id:: 1577622
UUID:: uuid:5e41f59f-6067-4dc0-b638-811d434984a7
Local pid:: pubs:1577622
Deposit date:: 2016-06-07
ARK identifier:: ark:/29072/ora_5e41f59f60674dc0b638811d434984a7

Related item:: Faster permutation inference in brain imaging
Description:: Permutation tests are increasingly being used as a reliable method for inference in neuroimaging analysis. However, they are computationally intensive. For small, non-imaging datasets, recomputing a model thousands of times is seldom a problem, but for large, complex models this can be prohibitively slow, even with the availability of inexpensive computing power. Here we exploit properties of statistics used with the general linear model (GLM) and their distributions to obtain accelerations irrespective of generic software or hardware improvements. We compare the following approaches: (i) performing a small number of permutations; (ii) estimating the p-value as a parameter of a negative binomial distribution; (iii) fitting a generalised Pareto distribution to the tail of the permutation distribution; (iv) computing p-values based on the expected moments of the permutation distribution, approximated from a gamma distribution; (v) direct fitting of a gamma distribution to the empirical permutation distribution; (vi) permuting a reduced number of voxels, with completion of the remainder using low rank matrix theory. Using synthetic data we assessed the different methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). We also conducted a re-analysis of a voxel-based morphometry study as a real-data example. All methods yielded exact error rates. Likewise, power was similar across methods. Resampling risk was higher for methods (i), (iii) and (v). For comparable resampling risks, the method in which no permutations are done (iv) was the absolute fastest. All methods produced visually similar maps for the real data, with stronger effects being detected in the family-wise error rate corrected maps by (iii) and (v), and generally similar to the results seen in the reference set. Overall, for uncorrected, p-values method (iv) was found the best as long as symmetric errors can be assumed. In all other settings, including for familywise error corrected p-values, we recommend the tail approximation (iii). The methods considered are freely available in the tool PALM - Permutation Analysis of Linear Models.

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