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Related Experiment Video

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

C R Pernet1, M Latinus2, T E Nichols3

  • 1Centre for Clinical Brain Sciences, Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.

Journal of Neuroscience Methods
|August 17, 2014
PubMed
Summary

Cluster-based correction methods effectively control family-wise error rates in mass-univariate analyses of event-related potentials/fields. Threshold-free cluster enhancement (TFCE) offers robust Type 1 error control, especially with adjusted parameters.

Keywords:
Cluster-based statisticsERPFamily-wise error rateMonte-Carlo simulationsMultiple comparison correctionThreshold free cluster enhancement

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Psychophysiology

Background:

  • Mass-univariate analyses of event-related potentials/fields are increasingly common.
  • Extensive statistical testing necessitates correction for multiple comparisons to minimize false positives.

Purpose of the Study:

  • To review and compare cluster-based correction methods for multiple comparisons.
  • To evaluate the performance of permutation and bootstrap approaches in conjunction with these methods.

Main Methods:

  • Review of cluster-based correction methods: cluster-height, cluster-size, cluster-mass, and threshold-free cluster enhancement (TFCE).
  • Comparison using permutation and bootstrap computational approaches via data-driven Monte-Carlo simulations.
  • Simulations involved comparing two conditions within subjects using a two-sample Student's t-test.

Main Results:

  • Cluster-based methods generally control the family-wise error rate (FWER) well, regardless of using permutation or bootstrap.
  • Stable results require a minimum of 800 iterations for simulations.
  • Bootstrap methods can be overly conservative with fewer than 50 trials.

Conclusions:

  • Threshold-free cluster enhancement (TFCE) demonstrates superior control of Type 1 error rates, particularly with an attenuated extent parameter.
  • Permutation methods may be too liberal at very low family-wise error rates (e.g., p=1%).
  • Recommendations are provided regarding iteration numbers and method suitability based on trial count and desired error rates.