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Permutation-based group sequential analyses for cognitive neuroscience.

John P Veillette1, Letitia Ho1, Howard C Nusbaum1

  • 1Department of Psychology, University of Chicago, United States.

Neuroimage
|June 22, 2023
PubMed
Summary
This summary is machine-generated.

Sequential analysis with a novel permutation scheme offers a solution for neuroimaging power analyses. This method controls false positive rates and can reduce sample sizes needed for detecting effects in electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies.

Keywords:
InferenceReproducibilitySample size

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Area of Science:

  • Cognitive Neuroscience
  • Statistical Methodology

Background:

  • Neuroimaging studies face challenges with complex data and analysis pipelines, leading to arbitrary sample size determination and underpowered designs.
  • Existing methods for sequential analysis in clinical trials often violate parametric assumptions common in neuroimaging data.
  • Meta-analyses for neuroimaging frequently suggest prohibitively large sample sizes, straining lab resources.

Purpose of the Study:

  • To propose sequential analyses as an efficient approach to address experimental design problems in cognitive neuroscience.
  • To introduce a general permutation scheme enabling sequential designs with arbitrary statistical tests, suitable for neuroimaging.
  • To demonstrate the utility of this approach for improving statistical power and reducing sample size requirements in neuroimaging research.

Main Methods:

  • Developed a general permutation scheme for sequential analysis compatible with arbitrary statistical tests.
  • Validated the permutation scheme through simulations to ensure control of false positive rates across interim analyses.
  • Conducted power analyses for seven evoked response effects in electroencephalography (EEG) data, comparing sequential to fixed-sample designs.

Main Results:

  • The proposed permutation scheme effectively controls the false positive rate in sequential neuroimaging analyses.
  • Sequential analysis designs required fewer subjects on average compared to fixed-sample designs for detecting true effects in EEG studies.
  • The methodology was implemented in a Python package 'niseq' for practical application.

Conclusions:

  • Sequential analysis, coupled with the novel permutation scheme, offers a statistically valid and efficient alternative for neuroimaging study design.
  • This approach can mitigate issues of underpowered studies and reduce the sample sizes required for robust findings.
  • The 'niseq' package facilitates the adoption of these advanced sequential methods in cognitive neuroscience research using EEG and fMRI.