  Cardiac pulsation is a physiological confound of functional magnetic
resonance imaging (fMRI) time-series that introduces spurious signal
fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast
to resolve cardiac pulsation. Depending on the ratio between the instantaneous
heart-rate and the acquisition sampling frequency (1/TR, with TR being the
repetition time), the cardiac signal may alias into the frequency band of
neural activation. The introduction of simultaneous multi-slice (SMS) imaging
has significantly reduced the chances of cardiac aliasing. However, the
necessity of covering the entire brain at high spatial resolution restrain the
shortest TR to just over 0.5 seconds, which is in turn not sufficiently short
to resolve cardiac pulsation beyond 60 beats per minute. Recently,
hyper-sampling of the fMRI time-series has been introduced to overcome this
issue. While each anatomical location is sampled every TR seconds, the time
between consecutive excitations is shorter and thus adequate to resolve cardiac
pulsation. In this study, we show that it is feasible to temporally and
spatially resolve cardiac waveforms at each voxel location by combining a
dedicated hyper-sampling decomposition scheme with SMS. We developed the
technique on 774 healthy subjects selected from the Human Connectome Project
(HCP) and validated the technology against the RETROICOR method. The proposed
approach, which we name Data-driven WHOle-brain CArdiac signal REgression from
highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), is
fully data-driven, does not make specific assumptions on cardiac pulsatility,
and is independent from external physiological recordings so that the
retrospective correction of fMRI data becomes possible when such measurements
are not available. WHOCARES is freely available at
https://github.com/gferrazzi/WHOCARES.
