  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.
