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Summary
This summary is machine-generated.

Canonical correlation analysis (CCA) permutation tests can inflate error rates, especially after adjusting for nuisance variables or for multiple canonical correlations. New methods ensure valid inference by transforming residuals or using stepwise estimation, improving neuroimaging research reliability.

Keywords:
Closed testing procedurePermutation testcanonical Correlation analysis

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Area of Science:

  • Neuroimaging
  • Population Neuroscience
  • Statistical Genetics

Background:

  • Canonical correlation analysis (CCA) is vital for neuroimaging, linking brain data with other variables.
  • Adjusting for nuisance variables (e.g., age, sex) is common but complicates statistical inference.
  • Existing permutation tests for CCA on residualized data have inflated error rates.

Purpose of the Study:

  • To identify and address inflated error rates in permutation inference for Canonical Correlation Analysis (CCA) in neuroimaging.
  • To propose valid permutation testing strategies for CCA, particularly when accounting for nuisance variables or multiple canonical correlations.

Main Methods:

  • Demonstrated inflated error rates of standard permutation tests on residualized neuroimaging data.
  • Proposed transforming residuals to a lower-dimensional basis to restore exchangeability for valid permutation testing.
  • Introduced a stepwise estimation approach to account for explained variance in subsequent canonical correlations.

Main Results:

  • Standard permutation tests for CCA on nuisance-variable-adjusted data yield invalid results due to violated exchangeability.
  • Simple permutation tests also overstate significance for higher-order canonical correlations.
  • The proposed methods, including basis transformation and stepwise estimation, provide valid permutation inference for CCA.

Conclusions:

  • Correcting for nuisance variables in CCA requires careful consideration of permutation test assumptions.
  • The developed stepwise and basis transformation methods offer valid and reliable statistical inference for neuroimaging studies using CCA.
  • These advancements enhance the robustness of findings in population neuroimaging research.