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Valid population inference for information-based imaging: From the second-level t-test to prevalence inference.

Carsten Allefeld1, Kai Görgen1, John-Dylan Haynes2

  • 1Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging, Department of Neurology, and Excellence Cluster NeuroCure, Charité - Universitätsmedizin Berlin, Germany.

Neuroimage
|July 25, 2016
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Summary

Standard t-tests for neuroimaging classification accuracy fail to generalize findings to the population. A new method, permutation-based information prevalence inference, offers a more accurate approach for population-level conclusions.

Keywords:
Effect prevalenceInformation-based imagingMultivariate pattern analysisPopulation inferencet-Test

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Area of Science:

  • Neuroimaging
  • Multivariate Pattern Analysis
  • Statistical Inference

Background:

  • Second-level inference in neuroimaging often uses t-tests on classification accuracies.
  • This approach assumes random-effects analysis for population inference.
  • However, classification accuracy cannot be below chance, altering the null hypothesis.

Purpose of the Study:

  • To critically evaluate the validity of using t-tests for population inference with classification accuracies in neuroimaging.
  • To propose and detail an alternative method for robust population-level inference in information-based imaging.

Main Methods:

  • Theoretical arguments and simulations were used to analyze the limitations of t-tests.
  • A novel method, permutation-based information prevalence inference using the minimum statistic, was developed.
  • This method was applied to empirical neuroimaging data.

Main Results:

  • T-tests on classification accuracies do not provide true population inference for information-like measures.
  • The standard t-test, in this context, is equivalent to a fixed-effects analysis.
  • The proposed permutation-based method allows inference on the prevalence of effects in the population.

Conclusions:

  • Standard t-tests are inappropriate for population-level generalization of neuroimaging classification accuracy.
  • Population inference for information-based imaging should focus on effect prevalence, not the mean.
  • Permutation-based information prevalence inference offers a statistically sound alternative.