  Recently, Eklund et al. (2016) analyzed clustering methods in standard FMRI
packages: AFNI (which we maintain), FSL, and SPM [1]. They claimed: 1) false
positive rates (FPRs) in traditional approaches are greatly inflated,
questioning the validity of "countless published fMRI studies"; 2)
nonparametric methods produce valid, but slightly conservative, FPRs; 3) a
common flawed assumption is that the spatial autocorrelation function (ACF) of
FMRI noise is Gaussian-shaped; and 4) a 15-year-old bug in AFNI's 3dClustSim
significantly contributed to producing "particularly high" FPRs compared to
other software. We repeated simulations from [1] (Beijing-Zang data [2], see
[3]), and comment on each point briefly.
