Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

Abstract

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.

Preservation of EEG spectral power features during simultaneous EEG-fMRI

“…None of the seven BCG removal methods tested here allowed to preserve the EEG alpha fluctuations to the same extent, and no statistical associations with the BOLD signal were observed in the EEG-informed fMRI analysis. These results provide compelling evidence that BCG artifact residuals and/or EEG signal losses related to the artifact removal procedure severely impair data quality and mask the functional association between EEG alpha power and occipito-parietal BOLD signal, hampering our interpretations from multimodal EEG-fMRI integrative analyses (Goldman et al, 2002;Scrivener, 2021;Warbrick, 2022).…”

The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoencephalography-functional magnetic resonance imaging study

“…Multimodal data-fusion based approaches have proven particularly useful for combining genetic mapping with other measures in the study of brain disorders ( Purcell et al, 2009 ; Pearlson et al, 2015 ) as well as for evaluating the variability of brain anatomy and function in healthy subjects ( Hardoon et al, 2009 ; Le Floch et al, 2012 ; Renvall et al, 2012b ; Salmela et al, 2016 ) and predicting the subjects’ age ( Engemann et al, 2020 ). So far, fusion of different neuroimaging data-types has been applied for identifying (in individual brain regions), e.g., the neural underpinnings of the BOLD response ( Scheeringa et al, 2011 ; Kujala et al, 2014 ), also at the laminar level ( Scheeringa et al, 2016 ; Warbrick, 2022 ), the effects of anatomical properties on functional data ( Sepulcre et al, 2009 ; Schwarzkopf et al, 2012 ), or the effects of GABAergic inhibition on fMRI and MEG responses ( Muthukumaraswamy et al, 2009 ; Kujala et al, 2015 ). While it has been proposed that by combining the temporally/spectrally and spatially sensitive measures of neural engagement provided by MEG and fMRI one could obtain a spatiotemporally accurate picture of brain activity ( Dale et al, 2000 ), such data fusion has rarely been applied.…”

The Noninvasive Evaluation for Minimally Invasive Pediatric Epilepsy Surgery (MIPES): A Multimodal Exploration of the Localization-Based Hypothesis

“…Although the relatively low temporal resolution of fMRI hinders it from independently localizing IEDs, simultaneous EEG (fMRI-EEG) has been used effectively. 92 IEDs on scalp EEG that are adjacent to a region of maximal hemodynamic activation on simultaneous fMRI may be more likely to represent the SOZ than those in remote locations and their removal has been associated with favorable postsurgical outcome. 93 94 EEG equipment may generate artifacts in the MRI signal and vice versa, but techniques exist to minimize these effects.…”

“…93 94 EEG equipment may generate artifacts in the MRI signal and vice versa, but techniques exist to minimize these effects. 92 Like MEG, the odds of capturing ictal data are low due to relatively short study duration.…”