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Valid and powerful second-level group statistics for decoding accuracy: Information prevalence inference using the

Satoshi Hirose1

  • 1Faculty of Psychology, Otemon Gakuin University, 2-1-15 Nishiai, Ibaraki-shi, Osaka 567-8502, Japan; Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, 1-4, Yamadaoka, Suita City, Osaka, Japan.

Neuroimage
|August 7, 2021
PubMed
Summary

A new statistical test, the i-test, enhances functional magnetic resonance imaging (fMRI) decoding by inferring information prevalence in the population, offering higher statistical power than previous methods.

Keywords:
Decoding accuracyGroup statistical testInformation prevalence inferenceInformation-like measureOrder statisticStatistical poweri-test

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Modeling

Background:

  • Standard functional magnetic resonance imaging (fMRI) decoding uses group statistical tests, often a one-sample t-test, to assess if brain activation contains information about experimental conditions.
  • These tests may only indicate if *some* individuals show above-chance decoding, not if the effect is typical within the population.
  • Previous work proposed prevalence inference as an alternative to address this limitation.

Purpose of the Study:

  • To extend prevalence inference methods for fMRI decoding.
  • To introduce a novel statistical test, the "information prevalence inference using the i-th order statistic" (i-test).
  • To provide a method that infers the typical effect size in the population.

Main Methods:

  • The i-test compares the i-th lowest sample decoding accuracy (i-th order statistic) to a null distribution.
  • This verifies if the population's information prevalence (proportion of above-chance decoding) exceeds a threshold.
  • Theoretical details and numerical calculations were used to assess statistical power.

Main Results:

  • The i-test demonstrates higher statistical power compared to the method proposed by Allefeld et al. (2016).
  • Numerical calculations confirmed the i-test's high statistical power.
  • The method was successfully applied and demonstrated in an fMRI decoding study.

Conclusions:

  • The i-test provides a more robust inference about information prevalence in fMRI decoding.
  • A significant i-test result implies that a majority of the population exhibits information about the cognitive label in brain activity.
  • This method offers improved statistical power for detecting typical effects in fMRI decoding studies.