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Evaluating permutation-based inference for partial least squares analysis of neuroimaging data.

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Summary

Partial least squares (PLS) permutation testing with Procrustes rotation may inflate significance, masking true brain-behavior associations. Complementary metrics like LV strength and stability offer more reliable insights for neuroimaging research.

Keywords:
multivariate analysispartial least squarespermutation testingstatistical inference

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Area of Science:

  • Neuroimaging
  • Brain-Behavior Associations
  • Statistical Modeling

Background:

  • Partial Least Squares (PLS) is a common method in neuroimaging to identify brain-behavior associations via latent variables (LVs).
  • Statistical significance in PLS is typically determined by permutation testing, often involving Procrustes rotation for comparing original and permuted LVs.
  • The impact of Procrustes rotation on the sensitivity of permutation tests and the sufficiency of significance alone for characterizing PLS decompositions remain unclear.

Purpose of the Study:

  • To investigate the effect of Procrustes rotation on PLS permutation testing in neuroimaging.
  • To determine if significance alone is adequate for characterizing PLS decompositions.
  • To evaluate the utility of complementary metrics like LV strength and stability.

Main Methods:

  • PLS analyses were conducted on simulated datasets with known latent effects.
  • Permutation tests with and without Procrustes rotation were applied.
  • Analyses were extended to UK Biobank datasets with varying sample sizes.
  • Latent variable (LV) strength and split-half stability metrics were calculated.

Main Results:

  • Procrustes rotation systematically weakened null distributions, leading to an over-inflation of significance for the first LV, irrespective of true effect presence or strength.
  • Without rotation, significance increased with sample size in UK Biobank data.
  • LV strength and stability metrics effectively reflected confidence in the presence of effects in simulated data and provided nuanced assessments for real-world data.

Conclusions:

  • The use of Procrustes rotation in PLS permutation testing may lead to inflated significance, potentially misrepresenting brain-behavior associations.
  • Complementary metrics such as LV strength and stability are crucial for a more accurate and nuanced interpretation of PLS results.
  • Researchers should carefully consider these findings and explore alternative statistical approaches for robust neuroimaging analyses.