Abstract
The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.
Highlights
Electroencephalography (EEG) is by far the most commonly used technique to study brain function
Comparison Simultaneous EEG-functional magnetic resonance imaging (fMRI) measurements of event-related activity are typically used for assessing the performance of a given correction method, based on metrics computed from the event-related potentials (ERPs) such as: the inter-trial variability (Vanderperren et al, 2010), the signal-to-noise ratio (SNR) (Debener et al, 2007), and the difference between the ERPs extracted from the inside-MR EEG datasets and those that are obtained from the pulse artifact (PA)-free outside-MR EEG data (Mantini et al, 2007a)
We overviewed the several challenges associated with each step of the data analysis pipeline in EEG-informed fMRI, and provided a comprehensive description and discussion of the plethora of methods available to address each of those challenges
Summary
Electroencephalography (EEG) is by far the most commonly used technique to study brain function. (Bottom) The zoomed red box shows the high-amplitude electrical potentials generated by the time-varying gradients applied during the acquisition of four image slices using 2D multi-slice EPI; due to their clear periodicity and precise timing, these artifacts can be accurately corrected using channel-specific average template subtraction techniques. Prospective motion correction (PMC) of the EEG signal can be performed based on the head translation and rotation parameters estimated along the three main axes These estimates are typically used to improve MR image quality, by updating the specifications of the RF pulses and MR gradients during the image acquisition in real-time (recent reviews on these approaches can be found in Maclaren et al, 2013; Zaitsev et al, 2016). 2014 Maziero et al, 2016 used to model, and subsequently regress out, the motion-induced voltages on the concurrently acquired EEG
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