<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>5103</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>354</pageFirst>
    <pageLast>366</pageLast>
    <pageNumber/>
    <edition/>
    <issue>June</issue>
    <volume>133</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement</title>
    <abstract language="eng">Simultaneous EEG-fMRI provides an increasingly attractive research tool to investigate cognitive processes with high temporal and spatial resolution. However, artifacts in EEG data introduced by the MR scanner still remain a major obstacle. This study, employing commonly used artifact correction steps, shows that head motion, one overlooked major source of artifacts in EEG-fMRI data, can cause plausible EEG effects and EEG–BOLD correlations. Specifically, low-frequency EEG (&lt; 20 Hz) is strongly correlated with in-scanner movement. Accordingly, minor head motion (&lt; 0.2 mm) induces spurious effects in a twofold manner: Small differences in task-correlated motion elicit spurious low-frequency effects, and, as motion concurrently influences fMRI data, EEG–BOLD correlations closely match motion-fMRI correlations. We demonstrate these effects in a memory encoding experiment showing that obtained theta power (~ 3–7 Hz) effects and channel-level theta–BOLD correlations reflect motion in the scanner. These findings highlight an important caveat that needs to be addressed by future EEG-fMRI studies.</abstract>
    <parentTitle language="eng">NeuroImage</parentTitle>
    <identifier type="pmid">27012498</identifier>
    <identifier type="doi">10.1016/j.neuroimage.2016.03.031</identifier>
    <enrichment key="opus.import.date">2022-08-05T07:16:41+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>Marie-Christin Fellner</author>
    <author>G. Volberg</author>
    <author>K. J. Mullinger</author>
    <author>Mark W. Greenlee</author>
    <author>Simon Hanslmayr</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
</export-example>
