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Cardiac pulsation is a physiological confound of functional magneticresonance imaging (fMRI) time-series that introduces spurious signalfluctuations in proximity to blood vessels. fMRI alone is not sufficiently fastto resolve cardiac pulsation. Depending on the ratio between the instantaneousheart-rate and the acquisition sampling frequency (1/TR, with TR being therepetition time), the cardiac signal may alias into the frequency band ofneural activation. The introduction of simultaneous multi-slice (SMS) imaginghas significantly reduced the chances of cardiac aliasing. However, thenecessity of covering the entire brain at high spatial resolution restrain theshortest TR to just over 0.5 seconds, which is in turn not sufficiently shortto resolve cardiac pulsation beyond 60 beats per minute. Recently,hyper-sampling of the fMRI time-series has been introduced to overcome thisissue. While each anatomical location is sampled every TR seconds, the timebetween consecutive excitations is shorter and thus adequate to resolve cardiacpulsation. In this study, we show that it is feasible to temporally andspatially resolve cardiac waveforms at each voxel location by combining adedicated hyper-sampling decomposition scheme with SMS. We developed thetechnique on 774 healthy subjects selected from the Human Connectome Project(HCP) and validated the technology against the RETROICOR method. The proposedapproach, which we name Data-driven WHOle-brain CArdiac signal REgression fromhighly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), isfully data-driven, does not make specific assumptions on cardiac pulsatility,and is independent from external physiological recordings so that theretrospective correction of fMRI data becomes possible when such measurementsare not available. WHOCARES is freely available athttps://github.com/gferrazzi/WHOCARES.

WHOCARES: data-driven WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions