EEG-Informed fMRI: A Review of Data Analysis Methods

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.

Keywords: data quality; neurovascular coupling; simultaneous EEG-fMRI.

FIGURE 1

Main processing pipeline steps in EEG-informed fMRI analysis. Data quality is typically addressed first, taking into account modality-specific artifacts, as well as those that are induced by one technique on the other. The EEG phenomenon of interest is then identified and appropriate features are extracted, from which a BOLD signal predictor is derived for the localization of EEG-related BOLD-fMRI changes.

FIGURE 2

Illustration of the gradient artifact (GA) generated by a 2D multi-slice EPI sequence. (Top) 5 s traces of raw EEG data from 10 channels. At approximately 15 s, the fMRI acquisition starts, completely obscuring any neuronal activity being recorded. (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.

FIGURE 3

Illustration of the AAS technique to correct for the pulse artifact (PA). The EEG traces are shown for three channels, before and after correction, as well as for the ECG channel, over a time period of 10 s including 11 artifact occurrences. The segmented windows for each artifact occurrence (blue and red boxes) were averaged to compute the artifact template (red trace); this was then subtracted from the corresponding artifact occurrence (red box), yielding the artifact-corrected signal (green trace).

FIGURE 4

Illustration of the presence of physiological-related fluctuations in the BOLD signal. (Top) Structural brain image highlighting the brainstem (red dashed circle), a brain structure located close to major arteries and CSF-filled spaces, thus particularly susceptible to physiological fluctuations. (Middle) Average physiological noise-related BOLD time-course and respective power spectrum computed from a region near the brainstem. (Bottom) Average BOLD time-course across GM; in contrast with the brainstem, the GM time-course presents a clear spectrum, with most of its power located at frequencies below 0.1 Hz.