Given the recent controversies in some neuroimaging statistical methods, we compared the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. We used both resting state and task-based fMRI data, altogether ten datasets containing 780 scans corresponding to different scanning sequences and subject populations. In analyses of each fMRI scan we considered different assumed experimental designs and smoothing levels. For data with no expected experimentally-induced activation, FSL and SPM resulted in much higher false positive rates than AFNI. We showed it was because of residual positive autocorrelation left after pre-whitening. On the other hand, due to SPM modeling temporal autocorrelation in the least flexible way, it can introduce negative autocorrelations during pre-whitening for scans with long repetition times. As a result, for one task-based dataset we observed a large loss of sensitivity when SPM was used. Interestingly, because pre-whitening in FSL and SPM does not remove a substantial part of the temporal autocorrelation in the noise, we found a strong relationship in which the lower the assumed experimental design frequency, the more likely it was to observe significant activation. Though temporal autocorrelation modeling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modeling in FSL and SPM.  FSL and SPM could improve their autocorrelation modeling approaches for example by adopting a noise modelsimilar to the one used by AFNI .
Given the recent controversies in some neuroimaging statistical methods, we compared 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 employed eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. Though autocorrelation modeling in AFNI was not perfect, its performance was much higher than the performance of autocorrelation modeling in FSL and SPM. The residual autocorrelated noise in FSL and SPM led to heavily confounded first level results, particularly for low-frequency experimental designs. Also, we observed very severe problems for scans with short repetition times. The resulting false positives and false negatives can be expected to propagate to the group level, especially if the group analysis is performed with a mixed effects model. Our results show superior performance of SPM's alternative pre-whitening: FAST, over the default SPM's method. The reliability of task fMRI studies would increase with more accurate autocorrelation modeling. Furthermore, reliability could increase if the analysis packages provided diagnostic plots. This way the investigator would be aware of residual autocorrelated noise in the GLM residuals. We provide a MATLAB script for the fMRI researchers to check if their analyses might be affected by imperfect pre-whitening .
R
We used both resting state and task-based fMRI data, altogether ten datasets containing 780
This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. We employed eleven datasets containing 980
R
scanning sequences
fMRI protocols
R
In analyses of each fMRI scan we considered different assumed experimental designs and smoothing levels. For data with no expected experimentally-induced activation, FSL and SPM resulted in much higher false positive rates than AFNI. We showed it was because of residual positive autocorrelation left after pre-whitening. On the other hand, due to SPM modeling temporal autocorrelation in the least flexible way, it can introduce negative autocorrelations during pre-whitening for scans with long repetition times. As a result, for one task-based dataset we observed a large loss of sensitivity when SPM was used. Interestingly, because pre-whitening in FSL and SPM does not remove a substantial part of the temporal autocorrelation in the noise, we found a strong relationship in which the lower the assumed experimental design frequency, the more likely it was to observe significant activation. Though temporal
Though
D
temporal
A
The residual autocorrelated noise in
R
could improve their autocorrelation modeling approaches for example by adopting a noise modelsimilar to the one used by AFNI
led to heavily confounded first level results, particularly for low-frequency experimental designs. Also, we observed very severe problems for scans with short repetition times. The resulting false positives and false negatives can be expected to propagate to the group level, especially if the group analysis is performed with a mixed effects model. Our results show superior performance of SPM's alternative pre-whitening: FAST, over the default SPM's method. The reliability of task fMRI studies would increase with more accurate autocorrelation modeling. Furthermore, reliability could increase if the analysis packages provided diagnostic plots. This way the investigator would be aware of residual autocorrelated noise in the GLM residuals. We provide a MATLAB script for the fMRI researchers to check if their analyses might be affected by imperfect pre-whitening