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Abstract #3334

Comparing the efficiency of data-driven noise regression in removing cardiac and respiratory signals from rs-fMRI: Difference across age groups

Ali Golestani1 and Jean Chen2,3
1University of Toronto, Toronto, ON, Canada, 2Rotman Research Institute at Baycrest, Toronto, ON, Canada, 3Department of Biophysics, University of Toronto, Toronto, ON, Canada


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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