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Winkler, Anderson M., Webster, Matthew A., Brooks, Jonathan C. 
ORCID: https://orcid.org/0000-0003-3335-6209, Tracey, Irene, Smith, Stephen M. and Nichols, Thomas E.
(2016)
Non-parametric combination and related permutation tests for neuroimaging.
Human Brain Mapping, 37 (4).
pp. 1486-1511.
ISSN 1065-9471
Abstract
In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well-known definition of union-intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume-based representations of the brain, including non-imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non-parametric combination (NPC) methodology, such that instead of a two-phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one-way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: The authors declare no conflicts of interest. Contract grant sponsor: Brazilian National Research Council (CNPq); Contract grant number: 211534/2013-7; Contract grant sponsor: MRC; Contract grant number: G0900908; Contract grant sponsor: NIH; Contract grant numbers: R01 EB015611-01, NS41287; Contract grant sponsor: Wellcome Trust; Contract grant numbers: 100309/Z/12/Z, 098369/Z/12/Z; Contract grant sponsor: Marie Curie Initial Training Network; Contract grant number: MC-ITN-238593; Contract grant sponsors: GlaxoSmithKline plc, The Dr. Hadwen Trust for Humane Research, and the Barrow Neurological Institute. Publisher Copyright: © 2016 Wiley Periodicals, Inc. |
| Uncontrolled Keywords: | conjunctions,general linear model,multiple testing,non-parametric combination,permutation tests,anatomy,radiological and ultrasound technology,radiology nuclear medicine and imaging,neurology,clinical neurology ,/dk/atira/pure/subjectarea/asjc/2700/2702 |
| Faculty \ School: | Faculty of Social Sciences > School of Psychology |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 07 Sep 2022 11:31 |
| Last Modified: | 20 Oct 2022 18:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/87787 |
| DOI: | 10.1002/hbm.23115 |
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