title: Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study.
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.
  
  
  METHODS
  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).
authors.affiliation: Centre for Clinical Brain Sciences, Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK. Electronic address: cyril.pernet@ed.ac.uk.
authors.forename: C R
authors.initials: CR
authors.lastname: Pernet
authors.affiliation: Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13385 Marseille, France.
authors.forename: M
authors.initials: M
authors.lastname: Latinus
authors.affiliation: Department of Statistics, Warwick University, Coventry, UK.
authors.forename: T E
authors.initials: TE
authors.lastname: Nichols
authors.affiliation: Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK.
authors.forename: G A
authors.initials: GA
authors.lastname: Rousselet
mesh_terms: D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D066264:Datasets as Topic; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials; D006801:Humans; D009010:Monte Carlo Method; D012815:Signal Processing, Computer-Assisted; D012984:Software
keywords: Cluster-based statistics; ERP; Family-wise error rate; Monte-Carlo simulations; Multiple comparison correction; Threshold free cluster enhancement