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 Chen^2,3

¹University of Toronto, Toronto, ON, Canada, ²Rotman Research Institute at Baycrest, Toronto, ON, Canada, ³Department of Biophysics, University of Toronto, Toronto, ON, Canada

Synopsis

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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