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Faster permutation inference in brain imaging

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...

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Autores principales: Winkler, Anderson M., Ridgway, Gerard R., Douaud, Gwenaëlle, Nichols, Thomas E., Smith, Stephen M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035139/
https://www.ncbi.nlm.nih.gov/pubmed/27288322
http://dx.doi.org/10.1016/j.neuroimage.2016.05.068
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author Winkler, Anderson M.
Ridgway, Gerard R.
Douaud, Gwenaëlle
Nichols, Thomas E.
Smith, Stephen M.
author_facet Winkler, Anderson M.
Ridgway, Gerard R.
Douaud, Gwenaëlle
Nichols, Thomas E.
Smith, Stephen M.
author_sort Winkler, Anderson M.
collection PubMed
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; and (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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spelling pubmed-50351392016-11-01 Faster permutation inference in brain imaging Winkler, Anderson M. Ridgway, Gerard R. Douaud, Gwenaëlle Nichols, Thomas E. Smith, Stephen M. Neuroimage Article 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; and (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. Academic Press 2016-11-01 /pmc/articles/PMC5035139/ /pubmed/27288322 http://dx.doi.org/10.1016/j.neuroimage.2016.05.068 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Winkler, Anderson M.
Ridgway, Gerard R.
Douaud, Gwenaëlle
Nichols, Thomas E.
Smith, Stephen M.
Faster permutation inference in brain imaging
title Faster permutation inference in brain imaging
title_full Faster permutation inference in brain imaging
title_fullStr Faster permutation inference in brain imaging
title_full_unstemmed Faster permutation inference in brain imaging
title_short Faster permutation inference in brain imaging
title_sort faster permutation inference in brain imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035139/
https://www.ncbi.nlm.nih.gov/pubmed/27288322
http://dx.doi.org/10.1016/j.neuroimage.2016.05.068
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