Abstract—Noise removal is a critical step to recover the signal of interest from resting-state fMRI data. Several pre-processing pipelines have been developed mainly based on nuisance regression or independent component analysis. The aim of this work was to evaluate the ability in removing spurious non- BOLD signals of different cleaning pipelines when applied to a dataset of healthy controls and temporal lobe epilepsy patients. Increased tSNR and power spectral density in the resting-state frequency range (0.01-0.1 Hz) were found for all pre-processing pipelines with respect to the minimally pre-processed data, suggesting a positive gain in terms of temporal properties when optimal cleaning procedures are applied to the acquired fMRI data. All the pre-processing pipelines considered were able to recover the DMN through group ICA. By visually comparing this network across all the pipelines and groups, we found that AROMA, SPM12, FIX and FIXMC were able to better delineate the posterior cingulate cortex.
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