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  <title>NITRC News Group Forum: simultaneous-multi-slice-resting-state-fmri-at-3-tesla--slice-acceleration-related-biases-in-physiological-effects.</title>
  <link>http://www.nitrc.org/forum/forum.php?forum_id=8076</link>
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	&lt;table border=&quot;0&quot; width=&quot;100%&quot;&gt;&lt;tr&gt;&lt;td align=&quot;left&quot;/&gt;&lt;/tr&gt;&lt;/table&gt;
        &lt;p&gt;&lt;b&gt;Simultaneous Multi-slice Resting-state fMRI at 3 Tesla: Slice-Acceleration Related Biases in Physiological Effects.&lt;/b&gt;&lt;/p&gt;          
        &lt;p&gt;Brain Connect. 2017 Dec 10;:&lt;/p&gt;
        &lt;p&gt;Authors:  Golestani AM, Faraji-Dana Z, Kayvanrad MA, Setsompop K, Graham S, Chen JJ&lt;/p&gt;
        &lt;p&gt;Abstract&lt;br/&gt;
        Simultaneous multi-slice echo-planar imaging (EPI) can enhance the spatiotemporal resolution of resting-state functional MRI (rs-fMRI) by encoding and simultaneously imaging &quot;groups&quot; of slices. However, phenomena including respiration, cardiac pulsatility, respiration volume per time (RVT) and cardiac-rate variation (CRV), referred to as &quot;physiological processes&quot;, 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 &quot;slice-group&quot; 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.&lt;br/&gt;
        &lt;/p&gt;&lt;p&gt;PMID: 29226689 [PubMed - as supplied by publisher]&lt;/p&gt;
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