Random field theory-based p-values: A review of the SPM implementation
Abstract
P-values and null-hypothesis significance testing are popular data-analytical tools in functional neuroimaging. Sparked by the analysis of resting-state fMRI data, there has been a resurgence of interest in the validity of some of the p-values evaluated with the widely used software SPM in recent years. The default parametric p-values reported in SPM are based on the application of results from random field theory to statistical parametric maps, a framework commonly referred to as RFT. While RFT was established two decades ago and has since been applied in a plethora of fMRI studies, there does not exist a unified documentation of the mathematical and computational underpinnings of RFT as implemented in current versions of SPM. Here, we provide such a documentation with the aim of contributing to contemporary efforts towards higher levels of computational transparency in functional neuroimaging.
Cite
@article{arxiv.1808.04075,
title = {Random field theory-based p-values: A review of the SPM implementation},
author = {Dirk Ostwald and Sebastian Schneider and Rasmus Bruckner and Lilla Horvath},
journal= {arXiv preprint arXiv:1808.04075},
year = {2021}
}