title: Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data.
abstract: The simultaneous acquisition and subsequent analysis of EEG and fMRI data is challenging owing to increased noise levels in the EEG data. A common method to integrate data from these two modalities is to use aspects of the EEG data, such as the amplitudes of event-related potentials (ERP) or oscillatory EEG activity, to predict fluctuations in the fMRI data. However, this relies on the acquisition of high quality datasets to ensure that only the correlates of neuronal activity are being studied. In this study, we investigate the effects of head-motion-related artefacts in the EEG signal on the predicted T2*-weighted signal variation. We apply our analyses to two independent datasets: 1) four participants were asked to move their feet in the scanner to generate small head movements, and 2) four participants performed an episodic memory task. We created T2*-weighted signal predictors from indicators of abrupt head motion using derivatives of the realignment parameters, from visually detected artefacts in the EEG as well as from three EEG frequency bands (theta, alpha and beta). In both datasets, we found little correlation between the T2*-weighted signal and EEG predictors that were not convolved with the canonical haemodynamic response function (cHRF). However, all convolved EEG predictors strongly correlated with the T2*-weighted signal variation in various regions including the bilateral superior temporal cortex, supplementary motor area, medial parietal cortex and cerebellum. The finding that movement onset spikes in the EEG predict T2*-weighted signal intensity only when the time course of movements is convolved with the cHRF, suggests that the correlated signal might reflect a BOLD response to neural activity associated with head movement. Furthermore, the observation that broad-spectral EEG spikes tend to occur at the same time as abrupt head movements, together with the finding that abrupt movements and EEG spikes show similar correlations with the T2*-weighted signal, indicates that the EEG spikes are produced by abrupt movement and that continuous regressors of EEG oscillations contain motion-related noise even after stringent correction of the EEG data. If not properly removed, these artefacts complicate the use of EEG data as a predictor of T2*-weighted signal variation.
authors.affiliation: Division of Psychiatry, School of Community Health Sciences, University of Nottingham, UK. marije.jansen@doctors.org.uk
authors.forename: Marije
authors.initials: M
authors.lastname: Jansen
authors.forename: Thomas P
authors.initials: TP
authors.lastname: White
authors.forename: Karen J
authors.initials: KJ
authors.lastname: Mullinger
authors.forename: Elizabeth B
authors.initials: EB
authors.lastname: Liddle
authors.forename: Penny A
authors.initials: PA
authors.lastname: Gowland
authors.forename: Susan T
authors.initials: ST
authors.lastname: Francis
authors.forename: Richard
authors.initials: R
authors.lastname: Bowtell
authors.forename: Peter F
authors.initials: PF
authors.lastname: Liddle
mesh_terms: D000328:Adult; D016477:Artifacts; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D004569:Electroencephalography; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009038:Motion; D009474:Neurons / Q000502:physiology; D012815:Signal Processing, Computer-Assisted