Mass univariate analysis of event-related brain potentials/fields I: a critical tutorial review.

Event-related potentials (ERPs) and magnetic fields (ERFs) are typically analyzed via ANOVAs on mean activity in a priori windows. Advances in computing power and statistics have produced an alternative, mass univariate analyses consisting of thousands of statistical tests and powerful corrections for multiple comparisons. Such analyses are most useful when one has little a priori knowledge of effect locations or latencies, and for delineating effect boundaries. Mass univariate analyses complement and, at times, obviate traditional analyses. Here we review this approach as applied to ERP/ERF data and four methods for multiple comparison correction: strong control of the familywise error rate (FWER) via permutation tests, weak control of FWER via cluster-based permutation tests, false discovery rate control, and control of the generalized FWER. We end with recommendations for their use and introduce free MATLAB software for their implementation.

[1]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[2]  J. Pernier,et al.  ERP Manifestations of Processing Printed Words at Different Psycholinguistic Levels: Time Course and Scalp Distribution , 1999, Journal of Cognitive Neuroscience.

[3]  P. Hall,et al.  Robustness of multiple testing procedures against dependence , 2009, 0903.0464.

[4]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[5]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[6]  Anders M. Dale,et al.  Electrical and magnetic readings of mental functions , 1997 .

[7]  G. Rees Statistical Parametric Mapping , 2004, Practical Neurology.

[8]  B. Efron Large-Scale Simultaneous Hypothesis Testing , 2004 .

[9]  S. Hillyard,et al.  Electrical Signs of Selective Attention in the Human Brain , 1973, Science.

[10]  A. W. Kemp,et al.  Randomization, Bootstrap and Monte Carlo Methods in Biology , 1997 .

[11]  Karl J. Friston,et al.  Statistical parametric mapping for event-related potentials: I. Generic considerations , 2004, NeuroImage.

[12]  V. Guiard,et al.  Multiple test procedures using an upper bound of the number of true hypotheses and their use for evaluating high-dimensional EEG data , 2008, Journal of Neuroscience Methods.

[13]  Y. Benjamini,et al.  Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics , 1999 .

[14]  Thomas E. Nichols,et al.  Controlling the familywise error rate in functional neuroimaging: a comparative review , 2003, Statistical methods in medical research.

[15]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[16]  Bruno A Olshausen,et al.  Timecourse of neural signatures of object recognition. , 2003, Journal of vision.

[17]  Sabine Weiss,et al.  Multivariate tests for the evaluation of high-dimensional EEG data , 2004, Journal of Neuroscience Methods.

[18]  Y. Benjamini,et al.  Adaptive linear step-up procedures that control the false discovery rate , 2006 .

[19]  Marti J. Anderson,et al.  Permutation tests for univariate or multivariate analysis of variance and regression , 2001 .

[20]  Alessio Farcomeni,et al.  A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion , 2008, Statistical methods in medical research.

[21]  Azeem M. Shaikh,et al.  FORMALIZED DATA SNOOPING BASED ON GENERALIZED ERROR RATES , 2007, Econometric Theory.

[22]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[23]  T. J. Breen,et al.  Biostatistical Analysis (2nd ed.). , 1986 .

[24]  E. Ziegel Permutation, Parametric, and Bootstrap Tests of Hypotheses (3rd ed.) , 2005 .

[25]  M. Newton Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis , 2008 .

[26]  Mark A. van de Wiel,et al.  Comments on: Control of the false discovery rate under dependence using the bootstrap and subsampling , 2008 .

[27]  Thomas E. Nichols,et al.  Everything You Never Wanted to Know about Circular Analysis, but Were Afraid to Ask , 2010, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[28]  Friedemann Pulvermüller,et al.  [Q:] When Would You Prefer a SOSSAGE to a SAUSAGE? [A:] At about 100 msec. ERP Correlates of Orthographic Typicality and Lexicality in Written Word Recognition , 2006, Journal of Cognitive Neuroscience.

[29]  R. Blair,et al.  An alternative method for significance testing of waveform difference potentials. , 1993, Psychophysiology.

[30]  Zijiang J. He,et al.  Vertical and horizontal references determined by linear perspective and optic flow information , 2010 .

[31]  J. Troendle,et al.  Stepwise normal theory multiple test procedures controlling the false discovery rate , 2000 .

[32]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[33]  John Suckling,et al.  Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[34]  R. Simon,et al.  Controlling the number of false discoveries: application to high-dimensional genomic data , 2004 .

[35]  P. Good Permutation, Parametric, and Bootstrap Tests of Hypotheses , 2005 .

[36]  A. Gelman,et al.  Of Beauty, Sex and Power , 2009 .

[37]  David M. Groppe,et al.  Robust estimation of event-related brain potentials via 20% trimmed means , 2013 .

[38]  Marta Kutas,et al.  Mass univariate analysis of event-related brain potentials/fields II: Simulation studies. , 2011, Psychophysiology.

[39]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[40]  D. Loiselle,et al.  Event-Related Potentials: A Methods Handbook , 2006, Neurology.

[41]  Eduardo Martínez-Montes,et al.  False discovery rate and permutation test: An evaluation in ERP data analysis , 2010, Statistics in medicine.

[42]  Marta Kutas,et al.  Identifying reliable independent components via split-half comparisons , 2009, NeuroImage.

[43]  M. Lindquist The Statistical Analysis of fMRI Data. , 2008, 0906.3662.

[44]  S. Weiss,et al.  New concepts of multiple tests and their use for evaluating high-dimensional EEG data , 2005, Journal of Neuroscience Methods.

[45]  Marta Kutas,et al.  The phonemic restoration effect reveals pre-N400 effect of supportive sentence context in speech perception , 2010, Brain Research.

[46]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[47]  Juha O. Rinne,et al.  Semantic Processing of Spoken Words in Alzheimer's Disease: An Electrophysiological Study , 1998, Journal of Cognitive Neuroscience.