EEG-Informed fMRI: A Review of Data Analysis Methods

Frontiers in Human Neuroscience
Rodolfo AbreuPatrícia Figueiredo

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 EE...Continue Reading

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Citations

Jul 30, 2019·Frontiers in Neuroscience·Muhammad E H ChowdhuryRichard Bowtell
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Feb 12, 2020·Network Neuroscience·Sepideh Sadaghiani, Jonathan Wirsich
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Methods Mentioned

BETA
PCA
feature extraction

Software Mentioned

RETROKCOR
RETROICOR
PROJIC

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