# Selective inference for fMRI cluster-wise analysis, issues, and recommendations for critical vector selection: A comment on Blain et al

## TL;DR

This paper compares two methods for analyzing fMRI data and finds that one performs better under specific conditions.

## Contribution

The paper provides the first extensive comparison between Notip and pARI, two permutation-based methods for fMRI analysis.

## Key findings

- pARI outperforms Notip when both are used under their recommended settings.
- Notip and pARI have distinct advantages and drawbacks depending on the analysis context.

## Abstract

Two permutation-based methods for simultaneous inference on the proportion of active voxels in cluster-wise brain imaging analysis have recently been published: Notip and pARI. Both rely on the definition of a critical vector of orderedp-values, chosen from a family of candidate vectors, but differ in how the family is defined: computed from randomization of external data for Notip and determined a priori for pARI. These procedures were compared to other proposals in the literature, but an extensive comparison between the two methods is missing due to their parallel publication. We provide such a comparison and find that pARI outperforms Notip if both methods are applied under their recommended settings. However, each method carries different advantages and drawbacks.