Simultaneous Multi-slice Resting-state fMRI at 3 Tesla: Slice-Acceleration Related Biases in Physiological Effects.

Brain Connect. 2017 Dec 10;:

Authors: Golestani AM, Faraji-Dana Z, Kayvanrad MA, Setsompop K, Graham S, Chen JJ

Abstract
Simultaneous multi-slice echo-planar imaging (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 has 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 datasets 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. Additionally, 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.

PMID: 29226689 [PubMed - as supplied by publisher]