Pernet, Cyril, Latinus, Marianne, Nichols, Thomas E. and Rousselet, Guillaume Alexis (2015) Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields : a simulation study. Journal of Neuroscience Methods, 250 . pp. 85-93. doi:10.1016/j.jneumeth.2014.08.003

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Abstract

Background

In recent years, analyses of event related potentials/fields have moved from the selection of a few components and peaks to a mass-univariate approach in which the whole data space is analyzed. Such extensive testing increases the number of false positives and correction for multiple comparisons is needed.

Method

Here we review all cluster-based correction for multiple comparison methods (cluster-height, cluster-size, cluster-mass, and threshold free cluster enhancement – TFCE), in conjunction with two computational approaches (permutation and bootstrap).

Results

Data driven Monte-Carlo simulations comparing two conditions within subjects (two sample Student's t-test) showed that, on average, all cluster-based methods using permutation or bootstrap alike control well the family-wise error rate (FWER), with a few caveats.

Conclusions

(i) A minimum of 800 iterations are necessary to obtain stable results; (ii) below 50 trials, bootstrap methods are too conservative; (iii) for low critical family-wise error rates (e.g. p = 1%), permutations can be too liberal; (iv) TFCE controls best the type 1 error rate with an attenuated extent parameter (i.e. power < 1).

Item Type:	Journal Article
Subjects:	R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Evoked potentials (Electrophysiology), Multivariate analysis
Journal or Publication Title:	Journal of Neuroscience Methods
Publisher:	Elsevier BV
ISSN:	0165-0270
Official Date:	30 July 2015
Dates:	Date Event 30 July 2015 Published 13 August 2014 Available 5 August 2014 Accepted 26 May 2014 Submitted
Volume:	250
Page Range:	pp. 85-93
DOI:	10.1016/j.jneumeth.2014.08.003
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC)
Grant number:	BB/K014218/1 (BBSRC), BB/K01420X/1 (BBSRC)

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