EEG-Informed fMRI: A Review of Data Analysis Methods

• Published in: Frontiers in human neuroscience Vol. 12; p. 29
• Main Authors: Abreu, Rodolfo, Leal, Alberto, Figueiredo, Patrícia
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 06.02.2018; Frontiers Media S.A
• Subjects: Brain; Brain mapping; Contamination; Data processing; data quality; EEG; Electroencephalography; Functional magnetic resonance imaging; Integration; Neuroimaging; Neuroscience; neurovascular coupling; NMR; Nuclear magnetic resonance; Reviews; Signal processing; simultaneous EEG-fMRI; Portugal; neurovascular coupling; data quality; simultaneous EEG-fMRI
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2018.00029

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.