Faster Family-wise Error Control for Neuroimaging with a Parametric Bootstrap

TL;DR

This paper introduces a parametric bootstrap method for neuroimaging multiple testing that is faster than permutation tests and reliably controls the family-wise error rate, improving analysis efficiency.

Contribution

The paper presents a novel parametric bootstrap joint testing procedure that offers faster FWER control in neuroimaging compared to existing permutation methods.

Findings

Controls FWER reliably in finite samples

Reduces computational time significantly

Effective in region- and voxel-wise analyses

Abstract

In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most common family-wise error (FWE) controlling procedures in imaging, which rely on classical mathematical inequalities or Gaussian random field theory, yield FWE rates that are far from the nominal level. Depending on the approach used, the FWER can be exceedingly small or grossly inflated. Given the widespread use of neuroimaging as a tool for understanding neurological and psychiatric disorders, it is imperative that reliable multiple testing procedures are available. To our knowledge, only permutation joint testing procedures have been shown to reliably control the FWER at the nominal level. However, these procedures are computationally intensive due to the increasingly available large sample sizes and dimensionality of the images, and analyses can take days to complete. Here, we develop a parametric bootstrap joint testing procedure. The parametric bootstrap procedure works directly with the test statistics, which leads to much faster estimation of adjusted \emph{p}-values than resampling-based procedures while reliably controlling the FWER in sample sizes available in many neuroimaging studies. We demonstrate that the procedure controls the FWER in finite samples using simulations, and present region- and voxel-wise analyses to test for sex differences in developmental trajectories of cerebral blood flow.