Abstract
Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high-pass filtering. However, these standard steps do not remove many sources of noise. Thus, noise-reduction techniques, for example, CompCor, FIX, and ICA-AROMA have been developed to further improve the ability to draw meaningful conclusions from the data. The ability of these techniques to minimize noise while conserving signals of interest has been tested almost exclusively in resting-state fMRI and, only rarely, in task-related fMRI. Application of noise-reduction techniques to task-related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. Little remains known about the impact of these techniques on the retention of signal in tasks that may be associated with systemic physiological changes. In this paper, we compared two ICA-based, that is FIX and ICA-AROMA, two CompCor-based noise-reduction techniques, that is aCompCor, and tCompCor, and standard preprocessing using a large (n = 101) fMRI dataset including noxious heat and non-noxious auditory stimulation. Results show that preprocessing using FIX performs optimally for data obtained using noxious heat, conserving more signals than CompCor-based techniques and ICA-AROMA, while removing only slightly less noise. Similarly, for data obtained during non-noxious auditory stimulation, FIX noise-reduction technique before analysis with a covariate of interest outperforms the other techniques. These results indicate that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise during task-related fMRI.