Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study

Pernet, C.R., Latinus, M., Nichols, T.E. and Rousselet, G.A. (2014) Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study. Journal of Neuroscience Methods, 25(SI), pp. 85-93. (doi: 10.1016/j.jneumeth.2014.08.003) (PMID:25128255) (PMCID:PMC4510917)

96534.pdf - Accepted Version
Available under License Creative Commons Attribution.



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 samples 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:Articles
Glasgow Author(s) Enlighten ID:Rousselet, Dr Guillaume and Latinus, Dr Marianne
Authors: Pernet, C.R., Latinus, M., Nichols, T.E., and Rousselet, G.A.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Journal of Neuroscience Methods
Publisher:Elsevier B.V.
ISSN (Online):1872-678X
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Journal of Neuroscience Methods 25(S1):85-93
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
618161Statistical analysis tool for time/frequency state spacesGuillaume RousseletBiotechnology and Biological Sciences Research Council (BBSRC)BB/K014218/1INP - CENTRE FOR COGNITIVE NEUROIMAGING