Exploring the feasibility of simultaneous electroencephalography/functional magnetic resonance imaging at 7 T

Segmenting electroencephalography wires reduces radiofrequency shielding artifacts in simultaneous electroencephalography and functional magnetic resonance imaging at 7 T

Purpose

Simultaneous scalp electroencephalography and functional magnetic resonance imaging (EEG‐fMRI) enable noninvasive assessment of brain function with high spatial and temporal resolution. However, at ultra‐high field, the data quality of both modalities is degraded by mutual interactions. Here, we thoroughly investigated the radiofrequency (RF) shielding artifact of a state‐of‐the‐art EEG‐fMRI setup, at 7 T, and design a practical solution to limit this issue.

Methods

Electromagnetic field simulations and MR measurements assessed the shielding effect of the EEG setup, more specifically the EEG wiring. The effectiveness of segmenting the wiring with resistors to reduce the transmit field disruption was evaluated on a wire‐only EEG model and a simulation model of the EEG cap.

Results

The EEG wiring was found to exert a dominant effect on the disruption of the transmit field, whose intensity varied periodically as a function of the wire length. Breaking the electrical continuity of the EEG wires into segments shorter than one quarter RF wavelength in air (25 cm at 7 T) reduced significantly the RF shielding artifacts. Simulations of the EEG cap with segmented wires indicated similar improvements for a moderate increase of the power deposition.

Conclusion

We demonstrated that segmenting the EEG wiring into shorter lengths using commercially available nonmagnetic resistors is effective at reducing RF shielding artifacts in simultaneous EEG‐fMRI. This prevents the formation of RF‐induced standing waves, without substantial specific absorption rate (SAR) penalties, and thereby enables benefiting from the functional sensitivity boosts achievable at ultra‐high field.

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

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.

Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors

Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.

When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.

Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.

Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage

Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics. It is useful for understanding neuronal activity during many different event types, including spontaneous epileptic discharges, the activity of sleep stages, and activity evoked by external stimuli and decision-making tasks. However, EEG recorded during fMRI is subject to imaging, pulse, environment and motion artifact, causing noise many times greater than the neuronal signals of interest. Therefore, artifact removal methods are essential to ensure that artifacts are accurately removed, and EEG of interest is retained. This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published since 2016. The aim of the first review is to distill the literature into clear guidelines for use of simultaneous EEG-fMRI artifact reduction methods, and the aim of the second review is to determine the prevalence of artifact reduction method use in contemporary studies. We find that there are many published artifact reduction techniques available, including hardware, model based, and data-driven methods, but there are few studies published that adequately compare these methods. In contrast, recent EEG-fMRI studies show overwhelming use of just one or two artifact reduction methods based on literature published 15–20 years ago, with newer methods rarely gaining use outside the group that developed them. Surprisingly, almost 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized. We recommend minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies and suggest that more needs to be done to make new artifact reduction techniques more accessible for the researchers and clinicians using simultaneous EEG-fMRI.

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r ≈ 0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI

Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks' dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques.

Adapted cabling of an EEG cap improves simultaneous measurement of EEG and fMRI at 7T

BACKGROUND

The combination of EEG and ultra-high-field (7 T and above) fMRI holds the promise to relate electrophysiology and hemodynamics with greater signal to noise level and at higher spatial resolutions than conventional field strengths. Technical and safety restrictions have so far resulted in compromises in terms of MRI coil selection, resulting in reduced, signal quality, spatial coverage and resolution in EEG-fMRI studies at 7 T.

NEW METHOD

We adapted a 64-channel MRI-compatible EEG cap so that it could be used with a closed 32-channel MRI head coil thus avoiding several of these compromises. We compare functional and anatomical as well as the EEG quality recorded with this adapted setup with those recorded with a setup that uses an open-ended 8-channel head-coil.

RESULTS

Our set-up with the adapted EEG cap inside the closed 32 channel coil resulted in the recording of good quality EEG and (f)MRI data. Both functional and anatomical MRI images show no major effects of the adapted EEG cap on MR signal quality. We demonstrate the ability to compute ERPs and changes in alpha and gamma oscillations from the recorded EEG data.

COMPARISON WITH EXISTING METHODS

Compared to MRI recordings with an 8-channel open-ended head-coil, the loss in signal quality of the MRI images related to the adapted EEG cap is considerably reduced.

CONCLUSIONS

The adaptation of the EEG cap permits the simultaneous recording of good quality whole brain (f)MRI data using a 32 channel receiver coil, while maintaining the quality of the EEG data.

Safety Considerations of 7-T MRI in Clinical Practice

Although 7-T MRI has recently received approval for use in clinical patient care, there are distinct safety issues associated with this relatively high magnetic field. Forces on metallic implants and radiofrequency power deposition and heating are safety considerations at 7 T. Patient bioeffects such as vertigo, dizziness, false feelings of motion, nausea, nystagmus, magnetophosphenes, and electrogustatory effects are more common and potentially more pronounced at 7 T than at lower field strengths. Herein the authors review safety issues associated with 7-T MRI. The rationale for safety concerns at this field strength are discussed as well as potential approaches to mitigate risk to patients and health care professionals.

Investigating the variability of cardiac pulse artifacts across heartbeats in simultaneous EEG-fMRI recordings: A 7T study

Electroencephalography (EEG) recordings performed in magnetic resonance imaging (MRI) scanners are affected by complex artifacts caused by heart function, often termed pulse artifacts (PAs). PAs can strongly compromise EEG data quality, and remain an open problem for EEG-fMRI. This study investigated the properties and mechanisms of PA variability across heartbeats, which has remained largely unaddressed to date, and evaluated its impact on PA correction approaches. Simultaneous EEG-fMRI was performed at 7T on healthy participants at rest or under visual stimulation, with concurrent recordings of breathing and cardiac activity. PA variability was found to contribute to EEG variance with more than 500 μV² at 7T, which extrapolates to 92 μV² at 3T. Clustering analyses revealed that PA variability not only is linked to variations in head position/orientation, as previously hypothesized, but also, and more importantly, to the respiratory cycle and to heart rate fluctuations. The latter mechanisms are associated to short-timescale variability (even across consecutive heartbeats), and their importance varied across EEG channels. In light of this PA variability, three PA correction techniques were compared: average artifact subtraction (AAS), optimal basis sets (OBS), and an approach based on K-means clustering. All methods allowed the recovery of visual evoked potentials from the EEG data; nonetheless, OBS and K-means tended to outperform AAS, likely due to the inability of the latter in modeling short-timescale variability. Altogether, these results offer novel insights into the dynamics and underlying mechanisms of the pulse artifact, with important consequences for its correction, relevant to most EEG-fMRI applications.

Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI

Simultaneous EEG‐fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post‐processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post‐processing using a multichannel, recursive least squares (M‐RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M‐RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.

Magnetic Resonance Imaging technology-bridging the gap between noninvasive human imaging and optical microscopy

Technological advances in Magnetic Resonance Imaging (MRI) have provided substantial gains in the sensitivity and specificity of functional neuroimaging. Mounting evidence demonstrates that the hemodynamic changes utilized in functional MRI can be far more spatially and thus neuronally specific than previously believed. This has motivated a push toward novel, high-resolution MR imaging strategies that can match this biological resolution limit while recording from the entire human brain. Although sensitivity increases are a necessary component, new MR encoding technologies are required to convert improved sensitivity into higher resolution. These new sampling strategies improve image acquisition efficiency and enable increased image encoding in the time-frame needed to follow hemodynamic changes associated with brain activation.

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 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.

Microstructural and functional correlates of glutamate concentration in the posterior cingulate cortex

Glutamate is the major excitatory neurotransmitter in the human brain and has a central role in both intrinsic and stimulus-induced activity. We conducted a study in a cohort of healthy, male volunteers in which glutamate levels were measured in the posterior cingulate cortex (PCC) using 1H magnetic resonance spectroscopy at 3T. The advantages of simultaneous electroencephalography and magnetic resonance imaging (EEG-MRI) were exploited and the subjects were measured in the same session and under the same physiological conditions. Diffusion tensor imaging (DTI), functional MRI (fMRI) and EEG were measured in order to investigate the functional and microstructural correlates of glutamate. The concentration of glutamate (institute units) was calculated and those values were tested for correlation with the metrics of resting state fMRI, DTI, and EEG electrical sources. Our results showed that the concentration of glutamate in the PCC had a significant negative correlation with the tissue mean diffusivity in the same area. The analysis of resting state networks did not show any relationship between the concentration of glutamate and the intrinsic activity of the resting state networks. The concentration of glutamate showed a positive correlation with the electrical generators of α-1 frequency and a negative correlation with the generators of α-2 and β-1 electrical generators. © 2017 Wiley Periodicals, Inc.

Length matters: Improved high field EEG-fMRI recordings using shorter EEG cables

BACKGROUND

The use of concurrent EEG-fMRI recordings has increased in recent years, allowing new avenues of medical and cognitive neuroscience research; however, currently used setups present problems with data quality and reproducibility.

NEW METHOD

We propose a compact experimental setup for concurrent EEG-fMRI at 4T and compare it to a more standard reference setup. The compact setup uses short EEG cables connecting to the amplifiers, which are placed right at the back of the head RF coil on a form-fitting extension force-locked to the patient MR bed. We compare the two setups in terms of sensitivity to MR-room environmental noise, interferences between measuring devices (EEG or fMRI), and sensitivity to functional responses in a visual stimulation paradigm.

RESULTS

The compact setup reduces the system sensitivity to both external noise and MR-induced artefacts by at least 60%, with negligible EEG noise induced from the mechanical vibrations of the cryogenic cooling compression pump.

COMPARISON WITH EXISTING METHODS

The compact setup improved EEG data quality and the overall performance of MR-artifact correction techniques. Both setups were similar in terms of the fMRI data, with higher reproducibility for cable placement within the scanner in the compact setup.

CONCLUSIONS

This improved compact setup may be relevant to MR laboratories interested in reducing the sensitivity of their EEG-fMRI experimental setup to external noise sources, setting up an EEG-fMRI workplace for the first time, or for creating a more reproducible configuration of equipment and cables. Implications for safety and ergonomics are discussed.

Time-frequency analysis of resting state and evoked EEG data recorded at higher magnetic fields up to 9.4 T

BACKGROUND

Combining both high temporal and spatial resolution by means of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is of relevance to neuroscientists. This combination, however, leads to a distortion of the EEG signal by the so-called cardio-ballistic artefacts. The aim of the present study was developing an approach to restore meaningful physiological EEG data from recordings at different magnetic fields.

NEW METHODS

The distortions introduced by the magnetic field were corrected using a combination of concepts from independent component analysis (ICA) and mutual information (MI). Thus, the components were classified as either related to the cardio-ballistic artefacts or to the signals of interest. EEG data from two experimental paradigms recorded at different magnetic field strengths up to 9.4 T were analyzed: (i) spontaneous activity using an eyes-open/eyes-closed alternation, and (ii) responses to auditory stimuli, i.e. auditory evoked potentials.

RESULTS

Even at ultra-high magnetic fields up to 9.4 T the proposed artefact rejection approach restored the physiological time-frequency information contained in the signal of interest and the data were suitable for subsequent analyses.

COMPARISON WITH EXISTING METHODS

Blind source separation (BSS) has been used to retrieve information from EEG data recorded inside the MR scanner in previous studies. After applying the presented method on EEG data recorded at 4 T, 7 T, and 9.4 T, we could retrieve more information than from data cleaned with the BSS method.

CONCLUSIONS

The present work demonstrates that EEG data recorded at ultra-high magnetic fields can be used for studying neuroscientific research question related to oscillatory activity.

Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion

The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7 T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.

Simultaneous EEG-fMRI: evaluating the effect of the cabling configuration on the gradient artefact

EEG recordings made in combined EEG-fMRI studies are corrupted by gradient artefacts (GAs) resulting from the interaction of the EEG system with the time-varying magnetic field gradients used in MRI. The dominant contribution to the GA arises from interaction with the leads of the EEG cap and the human head, but artefacts are also produced in the cables used to connect the EEG cap to the amplifier. The aim of this study is to measure the effects of the connecting cable configuration on the characteristics of the GA. We measured the GA produced on two different cable configurations (a ribbon cable and a cable consisting of wires that are twisted together to form a cylindrical bundle) by gradient pulses applied on three orthogonal axes and also characterized the effect of each cable configuration on the GA generated by a multi-slice echo planar imaging sequence, as employed in typical EEG-fMRI studies. The results demonstrate that the cabling that connects the EEG cap to the amplifier can make a significant contribution to the GA recorded during EEG-fMRI studies. In particular, we demonstrate that the GA generated by a ribbon cable is larger than that produced using a twisted cable arrangement and that changes in the GA resulting from variation in the cable position are also greater for the ribbon cable.

Safety in simultaneous EEG-fMRI at 3 T: temperature measurements

BACKGROUND

In simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), safety of the EEG equipment is ensured by the manufacturer only for localizers and fMRI sequences. To conduct a clinically feasible simultaneous EEG-fMRI study, other sequences, e.g. anatomical and B0-correction sequences, have to be acquired in the same imaging session.

PURPOSE

To measure the temperature increase of the electrodes in different size EEG caps in a phantom and volunteers during magnetic resonance imaging (MRI) sequences typically used in clinical studies.

MATERIAL AND METHODS

A phantom with EEG caps of size 52, 56, and 60 was imaged using several sequences in two 3 T MRI scanners to determine the maximum and average temperature increases in the electrodes. Additionally, three volunteer studies were performed for the EEG caps of sizes 56 and 60. The sequences were gradient echo based echo planar imaging sequence, T2-weighted turbo spin echo (T2-TSE), spin echo multiecho for B0-correction, diffusion tensor imaging and T1-weighted 3D sequences.

RESULTS

In phantom studies the maximum temperature increase was 4.1℃ with a mean of 1.2 ± 1.1℃. In volunteer studies, the maximum temperature measured was 35.6℃ and the maximum temperature rise was 2.1℃ with a mean of 0.9 ± 0.7℃. Both were observed with a T2-TSE sequence.

CONCLUSION

The temperature of the electrodes did not exceed the limits set by the IEC 60601-1 standard (43℃) or manufacturer (45℃), thus indicating a safe EEG-fMRI protocol in this respect.

Simultaneous EEG-fMRI at ultra-high field: artifact prevention and safety assessment

The simultaneous recording of scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can provide unique insights into the dynamics of human brain function, and the increased functional sensitivity offered by ultra-high field fMRI opens exciting perspectives for the future of this multimodal approach. However, simultaneous recordings are susceptible to various types of artifacts, many of which scale with magnetic field strength and can seriously compromise both EEG and fMRI data quality in recordings above 3T. The aim of the present study was to implement and characterize an optimized setup for simultaneous EEG-fMRI in humans at 7 T. The effects of EEG cable length and geometry for signal transmission between the cap and amplifiers were assessed in a phantom model, with specific attention to noise contributions from the MR scanner coldheads. Cable shortening (down to 12 cm from cap to amplifiers) and bundling effectively reduced environment noise by up to 84% in average power and 91% in inter-channel power variability. Subject safety was assessed and confirmed via numerical simulations of RF power distribution and temperature measurements on a phantom model, building on the limited existing literature at ultra-high field. MRI data degradation effects due to the EEG system were characterized via B0 and B1(+) field mapping on a human volunteer, demonstrating important, although not prohibitive, B1 disruption effects. With the optimized setup, simultaneous EEG-fMRI acquisitions were performed on 5 healthy volunteers undergoing two visual paradigms: an eyes-open/eyes-closed task, and a visual evoked potential (VEP) paradigm using reversing-checkerboard stimulation. EEG data exhibited clear occipital alpha modulation and average VEPs, respectively, with concomitant BOLD signal changes. On a single-trial level, alpha power variations could be observed with relative confidence on all trials; VEP detection was more limited, although statistically significant responses could be detected in more than 50% of trials for every subject. Overall, we conclude that the proposed setup is well suited for simultaneous EEG-fMRI at 7 T.

Characterisation and reduction of the EEG artefact caused by the helium cooling pump in the MR environment: validation in epilepsy patient data

The EEG acquired simultaneously with fMRI is distorted by a number of artefacts related to the presence of strong magnetic fields, which must be reduced in order to allow for a useful interpretation and quantification of the EEG data. For the two most prominent artefacts, associated with magnetic field gradient switching and the heart beat, reduction methods have been developed and applied successfully. However, a number of artefacts related to the MR-environment can be found to distort the EEG data acquired even without ongoing fMRI acquisition. In this paper, we investigate the most prominent of those artefacts, caused by the Helium cooling pump, and propose a method for its reduction and respective validation in data collected from epilepsy patients. Since the Helium cooling pump artefact was found to be repetitive, an average template subtraction method was developed for its reduction with appropriate adjustments for minimizing the degradation of the physiological part of the signal. The new methodology was validated in a group of 15 EEG-fMRI datasets collected from six consecutive epilepsy patients, where it successfully reduced the amplitude of the artefact spectral peaks by 95 ± 2 % while the background spectral amplitude within those peaks was reduced by only -5 ± 4 %. Although the Helium cooling pump should ideally be switched off during simultaneous EEG-fMRI acquisitions, we have shown here that in cases where this is not possible the associated artefact can be effectively reduced in post processing.

Recursive approach of EEG-segment-based principal component analysis substantially reduces cryogenic pump artifacts in simultaneous EEG-fMRI data

Electroencephalography (EEG) data simultaneously acquired with functional magnetic resonance imaging (fMRI) data are preprocessed to remove gradient artifacts (GAs) and ballistocardiographic artifacts (BCAs). Nonetheless, these data, especially in the gamma frequency range, can be contaminated by residual artifacts produced by mechanical vibrations in the MRI system, in particular the cryogenic pump that compresses and transports the helium that chills the magnet (the helium-pump). However, few options are available for the removal of helium-pump artifacts. In this study, we propose a recursive approach of EEG-segment-based principal component analysis (rsPCA) that enables the removal of these helium-pump artifacts. Using the rsPCA method, feature vectors representing helium-pump artifacts were successfully extracted as eigenvectors, and the reconstructed signals of the feature vectors were subsequently removed. A test using simultaneous EEG-fMRI data acquired from left-hand (LH) and right-hand (RH) clenching tasks performed by volunteers found that the proposed rsPCA method substantially reduced helium-pump artifacts in the EEG data and significantly enhanced task-related gamma band activity levels (p=0.0038 and 0.0363 for LH and RH tasks, respectively) in EEG data that have had GAs and BCAs removed. The spatial patterns of the fMRI data were estimated using a hemodynamic response function (HRF) modeled from the estimated gamma band activity in a general linear model (GLM) framework. Active voxel clusters were identified in the post-/pre-central gyri of motor area, only from the rsPCA method (uncorrected p<0.001 for both LH/RH tasks). In addition, the superior temporal pole areas were consistently observed (uncorrected p<0.001 for the LH task and uncorrected p<0.05 for the RH task) in the spatial patterns of the HRF model for gamma band activity when the task paradigm and movement were also included in the GLM.

Methods for pulse artefact reduction: experiences with EEG data recorded at 9.4 T static magnetic field

BACKGROUND

The feasibility of recording electroencephalography (EEG) at ultra-high static magnetic fields up to 9.4 T was recently demonstrated and is expected to be incorporated into functional magnetic resonance imaging (fMRI) studies at 9.4 T. Correction of the pulse artefact (PA) is a significant challenge since its amplitude is proportional to the strength of the magnetic field in which EEG is recorded.

NEW METHOD

We conducted a study in which different PA correction methods were applied to EEG data recorded inside a 9.4 T scanner in order to retrieve visual P100 and auditory P300 evoked potentials. We explored different PA reduction methods, including the optimal basis set (OBS) method as well as objective and subjective component rejection using independent component analysis (ICA).

RESULTS

ICA followed by objective rejection of components is optimal for retrieving visual P100 and auditory P300 from EEG data recorded inside the scanner.

COMPARISON WITH EXISTING METHODS

Previous studies suggest that OBS or OBS followed by ICA are optimal for retrieving evoked potentials at 3T. In our EEG data recorded at 9.4 T OBS performed alone was not fully optimal for the identification of evoked potentials. OBS followed by ICA was partially effective.

CONCLUSIONS

In this study ICA has been shown to be an important tool for correcting the PA in EEG data recorded at 9.4 T, particularly when automated rejection of components is performed.

Simultaneous EEG-fMRI acquisition at low, high and ultra-high magnetic fields up to 9.4 T: perspectives and challenges

In this perspectives article we highlight the advantages of simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). As MRI moves towards using ultra-high magnetic fields in the quest for increased signal-to-noise, the question arises whether combined EEG-fMRI measurements are feasible at magnetic fields of 7 T and higher. We describe the challenges of MRI-EEG at 1.5, 3, 7 and 9.4 T and review the proposed solutions. In an outlook, we discuss further developments such as simultaneous trimodal imaging using MR, positron emission tomography (PET) and EEG under the same physiological conditions in the same subject.

EEG-fMRI integration for the study of human brain function

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have proved to be extremely valuable tools for the non-invasive study of human brain function. Moreover, due to a notable degree of complementarity between the two modalities, the combination of EEG and fMRI data has been actively sought in the last two decades. Although initially focused on epilepsy, EEG-fMRI applications were rapidly extended to the study of healthy brain function, yielding new insights into its underlying mechanisms and pathways. Nevertheless, EEG and fMRI have markedly different spatial and temporal resolutions, and probe neuronal activity through distinct biophysical processes, many aspects of which are still poorly understood. The remarkable conceptual and methodological challenges associated with EEG-fMRI integration have motivated the development of a wide range of analysis approaches over the years, each relying on more or less restrictive assumptions, and aiming to shed further light on the mechanisms of brain function along with those of the EEG-fMRI coupling itself. Here, we present a review of the most relevant EEG-fMRI integration approaches yet proposed for the study of brain function, supported by a general overview of our current understanding of the biophysical mechanisms coupling the signals obtained from the two modalities.

Reduction of EEG artefacts induced by vibration in the MR-environment

The EEG acquired simultaneously with functional magnetic resonance imaging (fMRI) is distorted by a number of artefacts related to the presence of strong magnetic fields. In order to allow for a useful interpretation of the EEG data, it is necessary to reduce these artefacts. For the two most prominent artefacts, associated with magnetic field gradient switching and the heart beat, reduction methods have been developed and applied successfully. Due to their repetitive nature, such artefacts can be reduced by subtraction of the respective template retrieved by averaging across cycles. In this paper, we investigate additional artefacts related to the MR environment and propose a method for the reduction of the vibration artefact caused by the cryo-cooler compression pumps system. Data were collected from the EEG cap placed on an MR head phantom, in order to characterise the MR environment related artefacts. Since the vibration artefact was found to be repetitive, a template subtraction method was developed for its reduction, and this was then adjusted to meet the specific requirements of patient data. The developed methodology successfully reduced the vibration artefact by about 90% in five EEG-fMRI datasets collected from two epilepsy patients.

EEG acquisition in ultra-high static magnetic fields up to 9.4 T

The simultaneous acquisition of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data has gained momentum in recent years due to the synergistic effects of the two modalities with regard to temporal and spatial resolution. Currently, only EEG-data recorded in fields of up to 7 T have been reported. We investigated the feasibility of recording EEG inside a 9.4 T static magnetic field, specifically to determine whether meaningful EEG information could be recovered from the data after removal of the cardiac-related artefact. EEG-data were recorded reliably and reproducibly at 9.4 T and the cardiac-related artefact increased in amplitude with increasing B0, as expected. Furthermore, we were able to correct for the cardiac-related artefact and identify auditory event related responses at 9.4 T in 75% of subjects using independent component analysis (ICA). Also by means of ICA we detected event related spectral perturbations (ERSP) in subjects at 9.4 T in response to opening/closing the eyes comparable with the response at 0 T. Overall our results suggest that it is possible to record meaningful EEG data at ultra-high magnetic fields. The simultaneous EEG-fMRI approach at ultra-high-fields opens up the horizon for investigating brain dynamics at a superb spatial resolution and a temporal resolution in the millisecond domain.

On the feasibility of concurrent human TMS-EEG-fMRI measurements

Simultaneously combining the complementary assets of EEG, functional MRI (fMRI), and transcranial magnetic stimulation (TMS) within one experimental session provides synergetic results, offering insights into brain function that go beyond the scope of each method when used in isolation. The steady increase of concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI studies further underlines the added value of such multimodal imaging approaches. Whereas concurrent EEG-fMRI enables monitoring of brain-wide network dynamics with high temporal and spatial resolution, the combination with TMS provides insights in causal interactions within these networks. Thus the simultaneous use of all three methods would allow studying fast, spatially accurate, and distributed causal interactions in the perturbed system and its functional relevance for intact behavior. Concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI experiments are already technically challenging, and the three-way combination of TMS-EEG-fMRI might yield additional difficulties in terms of hardware strain or signal quality. The present study explored the feasibility of concurrent TMS-EEG-fMRI studies by performing safety and quality assurance tests based on phantom and human data combining existing commercially available hardware. Results revealed that combined TMS-EEG-fMRI measurements were technically feasible, safe in terms of induced temperature changes, allowed functional MRI acquisition with comparable image quality as during concurrent EEG-fMRI or TMS-fMRI, and provided artifact-free EEG before and from 300 ms after TMS pulse application. Based on these empirical findings, we discuss the conceptual benefits of this novel complementary approach to investigate the working human brain and list a number of precautions and caveats to be heeded when setting up such multimodal imaging facilities with current hardware.

Measuring cerebral hemodynamics with a modified magnetoencephalography system

Magnetoencephalography (MEG) systems are designed to noninvasively measure magnetic fields produced by neural electrical currents. This project examines the possibility of measuring hemodynamics with an MEG system that has been modified with dc electromagnets to measure magnetic susceptibility while maintaining the capability of measuring neural dynamics. A forward model is presented that simulates the interaction of an applied magnetic field with changes in magnetic susceptibility in the brain associated with hemodynamics. Model predictions are compared with an experiment where deionized water was pumped into an inverted flask under the MEG sensor array of superconducting quantum interference device (SQUID) gradiometers (R(2) = 0.98, p < 0.001). The forward model was used to simulate the SQUID readouts from hemodynamics in the scalp and brain induced by performing the Valsalva maneuver. Experimental human subject recordings (N = 10) were made from the prefrontal region during Valsalva using concurrent measurement with the modified MEG system and near-infrared spectroscopy (NIRS). The NIRS deoxyhemoglobin signal was found to correlate significantly with the SQUID readouts (R(2) = 0.84, p < 0.01). SQUID noise was found to increase with the applied field, which will need to be mitigated in future work. These results demonstrate the potential and technical challenges of measuring cerebral hemodynamics with a modified MEG system.

Combining EEG and fMRI

The combination of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) forms a powerful tool for the investigation of brain function, but concurrent implementation of EEG and fMRI poses many technical challenges. Here, the motivation for combining EEG and fMRI is explored and methods underlying the combination are described. After a brief introduction to the two different techniques, the advantages and disadvantages of different methods of data recording are detailed, followed by a description of the artefacts encountered when performing EEG and fMRI measurements simultaneously, and the methods which have been developed to eliminate these artefacts. Important safety considerations and potential pitfalls associated with simultaneous recording are also described. The ways in which EEG and fMRI data analysis can be integrated are then described along with examples of key work which illustrate the power of combined EEG/fMRI measurements. The chapter concludes with a brief discussion of future directions for combined EEG/fMRI research.

Reproducibility of interictal EEG-fMRI results in patients with epilepsy

PURPOSE

Combined electroencephalography (EEG) and functional MRI (EEG-fMRI) can be useful in the evaluation of epilepsy patients. The reproducibility of EEG-fMRI findings needs to be established to consider it as a clinically valuable method. We addressed the intrasubject reproducibility of EEG-fMRI and the possible superiority of higher magnetic field strength in patients who were scanned twice.

METHODS

Fifteen patients were studied: Seven had one 1.5T and one 3T scan and eight had two 3T EEG-fMRI studies. Equal numbers of events of the same interictal epileptic discharge (IED) were included, and IED-related blood oxygenation level dependent (BOLD) results were compared.

KEY FINDINGS

In 1.5T-3T comparisons, five patients had BOLD responses in both studies, but in four there was a better response (higher maximum t-score and larger cluster) in 3T studies. One patient had a BOLD response in the 3T study only. The remaining patient had no BOLD response in either study. In 3T-3T comparisons, results were reproducible in five of eight patients, and one patient had no response in both studies. The two remaining patients had previous extensive surgery and extremely frequent IEDs. Some of the reproduced patterns in other patients, however, differed in terms of maximum t-score and cluster size.

SIGNIFICANCE

EEG-fMRI appears to provide reasonable reproducibility, although repeated studies may show differences. The absence of BOLD response seems to be reproducible as well. EEG-fMRI results tend to benefit from higher field scanners (3T over 1.5T). Further studies are needed to determine if reproducibility depends on specific clinical, electrographic, or anatomic findings.

How wrong can we be? The effect of inaccurate mark-up of EEG/fMRI studies in epilepsy

OBJECTIVE

The aim of this investigation was to determine the effect of inaccurate or inconsistent marking up of events in the EEG on statistical analysis of EEG/fMRI studies of patients with epilepsy.

METHODS

EEGs obtained during EEG/fMRI studies conducted on 10 patients with epilepsy and six normal control subjects were reviewed. All clear epileptiform events were marked up in the patient EEGs, as were all small movement-related artefacts in the patient and control subject EEGs. We then considered the effect on the numbers of voxels above threshold in the resulting Statistical Parametric Mapping (SPM) analysis if events were omitted, mislabelled, or if event times were inconsistently marked up.

RESULTS

Omitting true epileptiform events resulted in a decrease in the number of voxels that survive statistical threshold. Mixing epileptiform and non-epileptiform events in the SPM analysis generally (but not always) decreased the number of voxels that survived threshold. Inconsistent event mark-up had little effect if the inconsistency was small (<200 ms), but had more effect if it was large (>500 ms).

CONCLUSION

It is important to accurately mark-up EEGs acquired during EEG/fMRI studies in order to get the best results from subsequent analyses.

SIGNIFICANCE

Our study reveals the consequences of inaccurate review of the EEG in EEG/fMRI studies and suggests guidelines for the review of EEG in these investigations which, if followed, should result in studies of acceptable quality.