Although spatial smoothing of fMRI data can serve multiple purposes, increasing
the sensitivity of activation detection is probably its greatest benefit.
However, this increased detection power comes with a loss of specificity when
non-adaptive smoothing (i.e.\ the standard in most software packages) is used.
Simulation studies and analysis of experimental data was performed using the
R packages neuRosim and fmri. In these studies, we
systematically investigated the effect of spatial smoothing on the power and
number of false positives in two particular cases that are often encountered in
fMRI research: (1) Single condition activation detection for regions that differ
in size, and (2) multiple condition activation detection for neighbouring
regions. Our results demonstrate that adaptive smoothing is superior in both
cases because less false positives are introduced by the spatial smoothing
process compared to standard Gaussian smoothing or FDR inference of unsmoothed
data