Data-driven methods have been suggested to remove heartbeat and respiration noises from fMRI signals. We compared the effectiveness of these methods (global-signal regression (GS), white matter and CSF (cerebrospinal fluid) regression, anatomical and temporal CompCor, ICA AROMA) in removing the noise. GS, AROMA, and aCompCor removed the most physiological fluctuation, but GS and AROMA also removed most signals under 0.1 Hz. We also observed that all methods removed less noise power and more low-frequency power from young adult data compared to older adults.
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