  Given the recent controversies in some neuroimaging statistical methods, we
compare the most frequently used functional Magnetic Resonance Imaging (fMRI)
analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation
modeling. This process, sometimes known as pre-whitening, is conducted in
virtually all task fMRI studies. We employ eleven datasets containing 980 scans
corresponding to different fMRI protocols and subject populations. Though
autocorrelation modeling in AFNI is not perfect, its performance is much higher
than the performance of autocorrelation modeling in FSL and SPM. The residual
autocorrelated noise in FSL and SPM leads to heavily confounded first level
results, particularly for low-frequency experimental designs. Our results show
superior performance of SPM's alternative pre-whitening: FAST, over SPM's
default. The reliability of task fMRI studies would increase with more accurate
autocorrelation modeling. Furthermore, reliability could increase if the
packages provided diagnostic plots. This way the investigator would be aware of
pre-whitening problems.
