Gutierrez-Barragan, Felipe, Ithapu, Vamsi K., Hinrichs, Chris, Maumet, Camille, Johnson, Sterling C., Nichols, Thomas E. and Singh, Vikas (2017) Accelerating permutation testing in voxel-wise analysis through subspace tracking : a new plugin for SnPM. NeuroImage, 159 . pp. 79-98. doi:10.1016/j.neuroimage.2017.07.025

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Abstract

Permutation testing is a non-parametric method for obtaining the max null distribution used to compute corrected p-values that provide strong control of false positives. In neuroimaging, however, the computational burden of running such an algorithm can be significant. We find that by viewing the permutation testing procedure as the construction of a very large permutation testing matrix, T, one can exploit structural properties derived from the data and the test statistics to reduce the runtime under certain conditions. In particular, we see that T is low-rank plus a low-variance residual. This makes T a good candidate for low-rank matrix completion, where only a very small number of entries of T (∼0.35% of all entries in our experiments) have to be computed to obtain a good estimate. Based on this observation, we present RapidPT, an algorithm that efficiently recovers the max null distribution commonly obtained through regular permutation testing in voxel-wise analysis. We present an extensive validation on a synthetic dataset and four varying sized datasets against two baselines: Statistical NonParametric Mapping (SnPM13) and a standard permutation testing implementation (referred as NaivePT). We find that RapidPT achieves its best runtime performance on medium sized datasets (50≤n≤200), with speedups of 1.5× - 38× (vs. SnPM13) and 20x-1000× (vs. NaivePT). For larger datasets (n≥200) RapidPT outperforms NaivePT (6× - 200×) on all datasets, and provides large speedups over SnPM13 when more than 10000 permutations (2× - 15×) are needed. The implementation is a standalone toolbox and also integrated within SnPM13, able to leverage multi-core architectures when available.

Item Type:	Journal Article
Subjects:	R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
SWORD Depositor:	Library Publications Router
Library of Congress Subject Headings (LCSH):	Brain -- Imaging, Neuroscience
Journal or Publication Title:	NeuroImage
Publisher:	Elsevier
ISSN:	1053-8119
Official Date:	1 October 2017
Dates:	Date Event 1 October 2017 Published 15 July 2017 Available 12 July 2017 Accepted
Volume:	159
Page Range:	pp. 79-98
DOI:	10.1016/j.neuroimage.2017.07.025
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	** From PubMed via Jisc Publications Router. ** History: ** received: 02-06-2017 ** revised: 11-07-2017 ** accepted: 12-07-2017
Access rights to Published version:	Restricted or Subscription Access
Funder:	National Institutes of Health (U.S.) (NIH), National Science Foundation (U.S.) (NSF), University of Wisconsin--Madison, National Library of Medicine (U.S.) (NLM), Wellcome Trust (London, England)
Grant number:	R01 AG040396, R01 EB022883, R01 AG021155 (NIH), Career Grant 1252725, RI 1116584 (NSF), ADRC P50 AG033514, ICTR 1UL1RR025011 (University of Wisconsin--Madison), 2T15LM007359 (NLM), 100309/Z/12/Z (Wellcome Trust (London, England))

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