# Reply to Cox et al. and Kessler et al.: Data and code sharing is the way forward for fMRI

## TL;DR

This paper advocates for data and code sharing in fMRI research, emphasizing the importance of non-parametric statistical methods as valid alternatives to parametric ones, supported by recent software developments.

## Contribution

It highlights the adoption of non-parametric methods in major fMRI software, promoting transparency and robustness in data analysis.

## Key findings

- Non-parametric methods are valid alternatives for fMRI data analysis.
- Major software now support non-parametric group inference.
- Sharing data and code enhances reproducibility in fMRI research.

## Abstract

We are glad that our paper has generated intense discussions in the fMRI field, on how to analyze fMRI data and how to correct for multiple comparisons. The goal of the paper was not to disparage any specific fMRI software, but to point out that parametric statistical methods are based on a number of assumptions that are not always valid for fMRI data, and that non-parametric statistical methods are a good alternative. Through AFNIs introduction of non-parametric statistics in the function 3dttest++, the three most common fMRI softwares now all support non-parametric group inference (SPM through the toolbox SnPM, and FSL through the function randomise).