Simultaneous multislice echo-planar imaging (SMS-EPI) can enhance the spatiotemporal resolution of resting-state functional MRI (rs-fMRI) by encoding and simultaneously imaging “groups” of slices. However, phenomena, including respiration, cardiac pulsatility, respiration volume per time (RVT), and cardiac rate variation (CRV), referred to as “physiological processes,” impact SMS-EPI rs-fMRI in a manner that is yet to be well characterized. In particular, physiological noise may incur aliasing and introduce spurious signals from one slice into another within the “slice group” in rs-fMRI data, resulting in a deleterious effect on resting-state functional connectivity MRI (rs-fcMRI) maps. In the present work, we aimed to quantitatively compare the effects of physiological noise on regular EPI and SMS-EPI in terms of rs-fMRI data and resulting functional connectivity measurements. We compare SMS-EPI and regular EPI data acquired from 11 healthy young adults with matching parameters. The physiological noise characteristics were compared between the two data sets through different combinations of physiological regression steps. We observed that the physiological noise characteristics differed between SMS-EPI and regular EPI, with cardiac pulsatility contributing more to noise in regular EPI data but low-frequency heart rate variability contributing more to SMS-EPI. In addition, a significant slice-group bias was observed in the functional connectivity density maps derived from SMS-EPI data. We conclude that making appropriate corrections for physiological noise is likely more important for SMS-EPI than for regular EPI acquisitions.
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