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EEG-Informed fMRI: A Review of Data Analysis Methods

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 an...

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Autores principales: Abreu, Rodolfo, Leal, Alberto, Figueiredo, Patrícia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5808233/
https://www.ncbi.nlm.nih.gov/pubmed/29467634
http://dx.doi.org/10.3389/fnhum.2018.00029
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author Abreu, Rodolfo
Leal, Alberto
Figueiredo, Patrícia
author_facet Abreu, Rodolfo
Leal, Alberto
Figueiredo, Patrícia
author_sort Abreu, Rodolfo
collection PubMed
description 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.
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spelling pubmed-58082332018-02-21 EEG-Informed fMRI: A Review of Data Analysis Methods Abreu, Rodolfo Leal, Alberto Figueiredo, Patrícia Front Hum Neurosci Neuroscience 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. Frontiers Media S.A. 2018-02-06 /pmc/articles/PMC5808233/ /pubmed/29467634 http://dx.doi.org/10.3389/fnhum.2018.00029 Text en Copyright © 2018 Abreu, Leal and Figueiredo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Abreu, Rodolfo
Leal, Alberto
Figueiredo, Patrícia
EEG-Informed fMRI: A Review of Data Analysis Methods
title EEG-Informed fMRI: A Review of Data Analysis Methods
title_full EEG-Informed fMRI: A Review of Data Analysis Methods
title_fullStr EEG-Informed fMRI: A Review of Data Analysis Methods
title_full_unstemmed EEG-Informed fMRI: A Review of Data Analysis Methods
title_short EEG-Informed fMRI: A Review of Data Analysis Methods
title_sort eeg-informed fmri: a review of data analysis methods
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5808233/
https://www.ncbi.nlm.nih.gov/pubmed/29467634
http://dx.doi.org/10.3389/fnhum.2018.00029
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