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 such as CompCor and FIX 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 datasets and, only rarely, in task-related fMRI datasets. Application of noise-reduction techniques to task-related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. However, little remains known about the impact of different noise reduction techniques on the retention of signal, particularly during tasks that may be associated with systemic physiological changes. In this paper, we compared two noise-reduction techniques, i.e. FIX and CompCor, in an fMRI dataset including noxious heat stimulation and non-noxious auditory stimulation. Results show that preprocessing including FIX noise-reduction technique conserves significantly more signal than a preprocessing protocol including CompCor noise-reduction technique in both noxious heat and non-noxious auditory stimulations, while removing only slightly less noise. These results suggest that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise.