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Open AccessArticle

Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research

1
Educational Psychology Program, University of Alabama, Tuscaloosa, AL 35487, USA
2
Center for the Prevention of Youth Behavior Problems, University of Alabama, Tuscaloosa, AL 35487, USA
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(8), 198; https://doi.org/10.3390/brainsci9080198
Received: 24 June 2019 / Revised: 1 August 2019 / Accepted: 8 August 2019 / Published: 12 August 2019
(This article belongs to the Section Neuroimaging)
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Abstract

A significant challenge for fMRI research is statistically controlling for false positives without omitting true effects. Although a number of traditional methods for multiple comparison correction exist, several alternative tools have been developed that do not rely on strict parametric assumptions, but instead implement alternative methods to correct for multiple comparisons. In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images. We assessed the false alarm rate and hit rate of each method after noise was applied to the original images. View Full-Text
Keywords: fMRI; multiple comparison correction; statistical non-parametric mapping; 3DClustSim; threshold-free cluster enhancement fMRI; multiple comparison correction; statistical non-parametric mapping; 3DClustSim; threshold-free cluster enhancement
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Han, H.; Glenn, A.L.; Dawson, K.J. Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research. Brain Sci. 2019, 9, 198.

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