Effects of simultaneous EEG recording on MRI data quality at 1.5, 3 and 7 tesla

NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI

The simultaneous acquisition of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) allows the complementary study of the brain's electrophysiology and hemodynamics with high temporal and spatial resolution. One application with great potential is neurofeedback training of targeted brain activity, based on the real-time analysis of the EEG and/or fMRI signals. This depends on the ability to reduce in real time the severe artifacts affecting the EEG signal acquired with fMRI, mainly the gradient and pulse artifacts. A few methods have been proposed for this purpose, but they are either slow, hardware-dependent, publicly unavailable, or proprietary software. Here, we present a fully open-source and publicly available tool for real-time EEG artifact reduction in simultaneous EEG-fMRI recordings that is fast and applicable to any hardware. Our tool is integrated in the Python toolbox NeuXus for real-time EEG processing and adapts to a real-time scenario well-established artifact average subtraction methods combined with a long short-term memory network for R peak detection. We benchmarked NeuXus on three different datasets, in terms of artifact power reduction and background signal preservation in resting state, alpha-band power reactivity to eyes closure, and event-related desynchronization during motor imagery. We showed that NeuXus performed at least as well as the only available real-time tool for conventional hardware setups (BrainVision's RecView) and a well-established offline tool (EEGLAB's FMRIB plugin). We also demonstrated NeuXus' real-time ability by reporting execution times under 250 ms. In conclusion, we present and validate the first fully open-source and hardware-independent solution for real-time artifact reduction in simultaneous EEG-fMRI studies.

Comparison of Resting-State EEG Network Analyses With and Without Parallel MRI in Genetic Generalized Epilepsy

Genetic generalized epilepsy (GGE) is conceptualized as a brain disorder involving distributed bilateral networks. To study these networks, simultaneous EEG-fMRI measurements can be used. However, inside-MRI EEG suffers from strong MR-related artifacts; it is not established whether EEG-based metrics in EEG-fMRI resting-state measurements are suitable for the analysis of group differences at source-level. We evaluated the impact of the inside-MR measurement condition on statistical group comparisons of EEG on source-level power and functional connectivity in patients with GGE versus healthy controls. We studied the cross-modal spatial relation of statistical group differences in seed-based FC derived from EEG and parallel fMRI. We found a significant increase in power and a frequency-specific change in functional connectivity for the inside MR-scanner compared to the outside MR-scanner condition. For power, we found reduced group difference between GGE and controls both in terms of statistical significance as well as effect size. Group differences for ImCoh remained similar both in terms of statistical significance as well as effect size. We found increased seed-based FC for GGE patients from the thalamus to the precuneus cortex region in fMRI, and in the theta band of simultaneous EEG. Our findings suggest that the analysis of EEG functional connectivity based on ImCoh is suitable for MR-EEG, and that relative group difference in a comparison of patients with GGE against controls are preserved. Spatial correspondence of seed-based FC group differences between the two modalities was found for the thalamus.

Preservation of EEG spectral power features during simultaneous EEG-fMRI

Introduction

Electroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches.

Method

Electroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods.

Results

The BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis.

Discussion

Current software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.

Brain activity during shadowing of audiovisual cocktail party speech, contributions of auditory-motor integration and selective attention

Selective listening to cocktail-party speech involves a network of auditory and inferior frontal cortical regions. However, cognitive and motor cortical regions are differentially activated depending on whether the task emphasizes semantic or phonological aspects of speech. Here we tested whether processing of cocktail-party speech differs when participants perform a shadowing (immediate speech repetition) task compared to an attentive listening task in the presence of irrelevant speech. Participants viewed audiovisual dialogues with concurrent distracting speech during functional imaging. Participants either attentively listened to the dialogue, overtly repeated (i.e., shadowed) attended speech, or performed visual or speech motor control tasks where they did not attend to speech and responses were not related to the speech input. Dialogues were presented with good or poor auditory and visual quality. As a novel result, we show that attentive processing of speech activated the same network of sensory and frontal regions during listening and shadowing. However, in the superior temporal gyrus (STG), peak activations during shadowing were posterior to those during listening, suggesting that an anterior-posterior distinction is present for motor vs. perceptual processing of speech already at the level of the auditory cortex. We also found that activations along the dorsal auditory processing stream were specifically associated with the shadowing task. These activations are likely to be due to complex interactions between perceptual, attention dependent speech processing and motor speech generation that matches the heard speech. Our results suggest that interactions between perceptual and motor processing of speech relies on a distributed network of temporal and motor regions rather than any specific anatomical landmark as suggested by some previous studies.

Evaluation and comparison of most prevalent artifact reduction methods for EEG acquired simultaneously with fMRI

Multimodal neuroimaging using EEG and fMRI provides deeper insights into brain function by improving the spatial and temporal resolution of the acquired data. However, simultaneous EEG-fMRI inevitably compromises the quality of the EEG and fMRI signals due to the high degree of interaction between the two systems. Fluctuations in the magnetic flux flowing through the participant and the EEG system, whether due to movement within the magnetic field of the scanner or to changes in magnetic field strength, induce electrical potentials in the EEG recordings that mask the much weaker electrical activity of the neuronal populations. A number of different methods have been proposed to reduce MR artifacts. We present an overview of the most commonly used methods and an evaluation of the methods using three sets of diverse EEG data. We limited the evaluation to open-access and easy-to-use methods and a reference signal regression method using a set of six carbon-wire loops (CWL), which allowed evaluation of their added value. The evaluation was performed by comparing EEG signals recorded outside the MRI scanner with artifact-corrected EEG signals recorded simultaneously with fMRI. To quantify and evaluate the quality of artifact reduction methods in terms of the spectral content of the signal, we analyzed changes in oscillatory activity during a resting-state and a finger tapping motor task. The quality of artifact reduction in the time domain was assessed using data collected during a visual stimulation task. In the study we utilized hierarchical Bayesian probabilistic modeling for statistical inference and observed significant differences between the evaluated methods in the success of artifact reduction and associated signal quality in both the frequency and time domains. In particular, the CWL system proved superior to the other methods evaluated in improving spectral contrast in the alpha and beta bands and in recovering visual evoked responses. Based on the results of the evaluation study, we proposed guidelines for selecting the optimal method for MR artifact reduction.

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.

The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study

Objective.In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after an NF training protocol targeting localized motor areas.Approach.We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the dynamic causal modeling and parametric empirical Bayes frameworks for the estimation of effective connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas.Main results.Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task.Significance.To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.

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.

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.

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.

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.

Exploring the advantages of multiband fMRI with simultaneous EEG to investigate coupling between gamma frequency neural activity and the BOLD response in humans

We established an optimal combination of EEG recording during sparse multiband (MB) fMRI that preserves high-resolution, whole-brain fMRI coverage while enabling broad-band EEG recordings which are uncorrupted by MRI gradient artefacts (GAs). We first determined the safety of simultaneous EEG recording during MB fMRI. Application of MB factor = 4 produced <1°C peak heating of electrode/hardware during 20 min of GE-EPI data acquisition. However, higher SAR sequences require specific safety testing, with greater heating observed using PCASL with MB factor = 4. Heating was greatest in the electrocardiogram channel, likely due to it possessing longest lead length. We investigated the effect of MB factor on the temporal signal-to-noise ratio for a range of GE-EPI sequences (varying MB factor and temporal interval between slice acquisitions). We found that, for our experimental purpose, the optimal acquisition was achieved with MB factor = 3, 3mm isotropic voxels, and 33 slices providing whole head coverage. This sequence afforded a 2.25 s duration quiet period (without GAs) in every 3 s TR. Using this sequence, we demonstrated the ability to record gamma frequency (55-80 Hz) EEG oscillations, in response to right index finger abduction, that are usually obscured by GAs during continuous fMRI data acquisition. In this novel application of EEG-MB fMRI to a motor task, we observed a positive correlation between gamma and BOLD responses in bilateral motor regions. These findings support and extend previous work regarding coupling between neural and hemodynamic measures of brain activity in humans and showcase the utility of EEG-MB fMRI for future investigations.

Pitfalls in Organizational Neuroscience: A Critical Review and Suggestions for Future Research

The potential of neuroscience to be a viable framework for studying human behavior in organizations depends on scholars’ ability to evaluate, design, analyze, and accurately interpret neuroscientific research. Prior to the publishing of this special issue, relatively little guidance has been available in the management literature for scholars seeking to integrate neuroscience and organization science in a balanced, informative and methodologically rigorous manner. In response to this need, we address design logic and inferential issues involved in evaluating and conducting neuroscience research capable of informing organizational science. Specifically, neuroscience methods of functional magnetic resonance imaging, electroencephalography, lesion studies, transcranial magnetic stimulation, and transcranial direct current stimulation are reviewed, with attention to how these methods might be combined to achieve convergent evidence. We then discuss strengths and limitations of various designs, highlighting the issue of reverse inference as precarious yet necessary for organizational neuroscience. We offer solutions for addressing limitations related to reverse inference, and propose features that allow stronger inferences to be made. The article concludes with a review of selected empirical work in organizational neuroscience in light of these critical design features.

Application of polymer sensitive MRI sequence to localization of EEG electrodes

BACKGROUND

The growing popularity of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) opens up the possibility of imaging EEG electrodes while the subject is in the scanner. Such information could be useful for improving the fusion of EEG-fMRI datasets.

NEW METHOD

Here, we report for the first time how an ultra-short echo time (UTE) MR sequence can image the materials of an MR-compatible EEG cap, finding that electrodes and some parts of the wiring are visible in a high resolution UTE. Using these images, we developed a segmentation procedure to obtain electrode coordinates based on voxel intensity from the raw UTE, using hand labeled coordinates as the starting point.

RESULTS

We were able to visualize and segment 95% of EEG electrodes using a short (3.5min) UTE sequence. We provide scripts and template images so this approach can now be easily implemented to obtain precise, subject-specific EEG electrode positions while adding minimal acquisition time to the simultaneous EEG-fMRI protocol.

COMPARISON WITH EXISTING METHOD(S)

T1 gel artifacts are not robust enough to localize all electrodes across subjects, the polymers composing Brainvision cap electrodes are not visible on a T1, and adding T1 visible materials to the EEG cap is not always possible. We therefore consider our method superior to existing methods for obtaining electrode positions in the scanner, as it is hardware free and should work on a wide range of materials (caps).

CONCLUSIONS

EEG electrode positions are obtained with high precision and no additional hardware.

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.

NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity

In Diffusion Weighted MR Imaging (DWI), the signal is affected by the biophysical properties of neuronal cells and their relative placement, as well as extra-cellular tissue compartments. Typically, microstructural indices, such as fractional anisotropy (FA) and mean diffusivity (MD), are based on a tensor model that cannot disentangle the influence of these parameters. Recently, Neurite Orientation Dispersion and Density Imaging (NODDI) has exploited multi-shell acquisition protocols to model the diffusion signal as the contribution of three tissue compartments. NODDI microstructural indices, such as intra-cellular volume fraction (ICVF) and orientation dispersion index (ODI) are directly related to neuronal density and orientation dispersion, respectively. One way of examining the neurophysiological role of these microstructural indices across neuronal fibres is to look into how they relate to brain function. Here we exploit a statistical framework based on sparse Canonical Correlation Analysis (sCCA) and randomised Lasso to identify structural connections that are highly correlated with resting-state functional connectivity measured with simultaneous EEG-fMRI. Our results reveal distinct structural fingerprints for each microstructural index that also reflect their inter-relationships.

Presurgical brain mapping in epilepsy using simultaneous EEG and functional MRI at ultra-high field: feasibility and first results

OBJECTIVES

The aim of this study was to demonstrate that eloquent cortex and epileptic-related hemodynamic changes can be safely and reliably detected using simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) recordings at ultra-high field (UHF) for clinical evaluation of patients with epilepsy.

MATERIALS AND METHODS

Simultaneous EEG-fMRI was acquired at 7 T using an optimized setup in nine patients with lesional epilepsy. According to the localization of the lesion, mapping of eloquent cortex (language and motor) was also performed in two patients.

RESULTS

Despite strong artifacts, efficient correction of intra-MRI EEG could be achieved with optimized artifact removal algorithms, allowing robust identification of interictal epileptiform discharges. Noise-sensitive topography-related analyses and electrical source localization were also performed successfully. Localization of epilepsy-related hemodynamic changes compatible with the lesion were detected in three patients and concordant with findings obtained at 3 T. Local loss of signal in specific regions, essentially due to B 1 inhomogeneities were found to depend on the geometric arrangement of EEG leads over the cap.

CONCLUSION

These results demonstrate that presurgical mapping of epileptic networks and eloquent cortex is both safe and feasible at UHF, with the benefits of greater spatial resolution and higher blood-oxygenation-level-dependent sensitivity compared with the more traditional field strength of 3 T.

Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality

PURPOSE

To develop a 256-channel dense-array electroencephalography (dEEG) sensor net (the Ink-Net) using high-resistance polymer thick film (PTF) technology to improve safety and data quality during simultaneous dEEG/MRI.

METHODS

Heating safety was assessed with temperature measurements in an anthropomorphic head phantom during a 30-min, induced-heating scan at 7T. MRI quality assessment used B1 field mapping and functional MRI (fMRI) retinotopic scans in three humans at 3T. Performance of the 256-channel PTF Ink-Net was compared with a 256-channel MR-conditional copper-wired electroencephalography (EEG) net and to scans with no sensor net. A visual evoked potential paradigm assessed EEG quality within and outside the 3T scanner.

RESULTS

Phantom temperature measurements revealed nonsignificant heating (ISO 10974) in the presence of either EEG net. In human B1 field and fMRI scans, the Ink-Net showed greatly reduced cross-modal artifact and less signal degradation than the copper-wired net, and comparable quality to MRI without sensor net. Cross-modal ballistocardiogram artifact in the EEG was comparable for both nets.

CONCLUSION

High-resistance PTF technology can be effectively implemented in a 256-channel dEEG sensor net for MR conditional use at 7T and with significantly improved structural and fMRI data quality as assessed at 3T. Magn Reson Med 77:895-903, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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.

Modeling Neurovascular Coupling from Clustered Parameter Sets for Multimodal EEG-NIRS

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters (P < 0.05) for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data.

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG-fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological-haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG-fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG-fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG-fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations.

MRI with and without a high-density EEG cap--what makes the difference?

Besides the benefit of combining electroencephalography (EEG) and magnetic resonance imaging (MRI), much effort has been spent to develop algorithms aimed at successfully cleaning the EEG data from MRI-related gradient and ballistocardiological artifacts. However, there are also studies showing a negative influence of the EEG on MRI data quality. Therefore, in the present study, we focused for the first time on the influence of the EEG on morphometric measurements of T1-weighted MRI data (voxel- and surfaced-based morphometry). Here, we demonstrate a strong influence of the EEG on cortical thickness, surface area, and volume as well as subcortical volumes due to local EEG-related inhomogeneities of the static magnetic (B0) and the gradient field (B1). In a second step, we analyzed the signal-to-noise ratios for both the anatomical and the functional data when recorded simultaneously with EEG and MRI and compared them to the ratios of the MRI data without simultaneous EEG measurements. These analyses revealed consistently lower signal-to-noise ratios for anatomical as well as functional MRI data during simultaneous EEG registration. In contrast, further analyses of T2*-weighted images provided reliable results independent of whether including the individuals' T1-weighted image with or without the EEG cap in the fMRI preprocessing stream. Based on our findings, we strongly recommend against using the structural images obtained during simultaneous EEG-MRI recordings for further anatomical data analysis.

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.

Psychophysiology-informed (multimodal) imaging

Electroencephalography (EEG) and magnetic resonance imaging are two popular methodologies for brain research. While EEG has a high temporal resolution, yet a low spatial resolution, MRI has the complete opposite, a high spatial resolution, yet a low temporal resolution. Obviously therefore, researchers have been searching for ways combining the two methodologies, for more than two decades. However, there are many issues that have to be solved before the methodologies can be successfully and, more importantly reliably, combined. Here, we give an overview of these issues, and present strategies that have been used over the past two decades to overcome them. We start with a general description of EEG and (f)MRI methodology, then present the difficulties involved in combining both methodologies, and lastly present and discuss the most popular strategies that have been used over the past two decades to solve these problems. We conclude that in spite of the many issues, the two methodologies can be combined successfully, provided that the correct procedures are followed.

Data quality in fMRI and simultaneous EEG-fMRI

OBJECT

To evaluate functional magnetic resonance imaging (fMRI) and simultaneous electroencephalography (EEG)-fMRI data quality in an organization using several magnetic resonance imaging (MRI) systems.

MATERIALS AND METHODS

Functional magnetic resonance imaging measurements were carried out twice with a uniform gel phantom on five different MRI systems with field strengths of 1.5 and 3.0 T. Several image quality parameters were measured with automatic analysis software. For simultaneous EEG-fMRI, data quality was evaluated on 3.0 T systems, and the phantom results were compared to data on human volunteers.

RESULTS

The fMRI quality parameters measured with different MRI systems were on an acceptable level. The presence of the EEG equipment caused superficial artifacts on the phantom image. The typical artifact depth was 15 mm, and no artifacts were observed in the brain area in the images of volunteers. Average signal-to-noise ratio (SNR) reduction in the phantom measurements was 15 %, a reduction of SNR similar to that observed in the human data. We also detected minor changes in the noise of the EEG signal during the phantom measurement.

CONCLUSION

The phantom proved valuable in the successful evaluation of the data quality of fMRI and EEG-fMRI. The results fell within acceptable limits. This study demonstrated a repeatable method to measure and follow up on the data quality of simultaneous EEG-fMRI.

Reference layer artefact subtraction (RLAS): a novel method of minimizing EEG artefacts during simultaneous fMRI

Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after average artefact subtraction. Any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, as well as reducing the residual artefacts that can easily swamp signals from brain activity measured using current methods. Since these problems currently limit the utility of simultaneous EEG-fMRI, new approaches for reducing the magnitude and variability of the artefacts are required. One such approach is the use of an EEG cap that incorporates electrodes embedded in a reference layer that has similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads by time-varying field gradients, cardiac pulsation and subject movement are similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Taking the difference of the voltages in the reference and scalp channels will therefore reduce the artefacts, without affecting sensitivity to neuronal signals. Here, we test this approach by using a simple experimental realisation of the reference layer to investigate the artefacts induced on the leads attached to the reference layer and scalp and to evaluate the degree of artefact attenuation that can be achieved via reference layer artefact subtraction (RLAS). Through a series of experiments on phantoms and human subjects, we show that RLAS significantly reduces the gradient (GA), pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms AAS when motion is present in the removal of the GA and PA, while the combination of AAS and RLAS always produces higher artefact attenuation than AAS. Additionally, we demonstrate that RLAS greatly attenuates the unpredictable and highly variable MAs that are very hard to remove using post-processing methods.

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.

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.

Methods for simultaneous EEG-fMRI: an introductory review

The simultaneous recording and analysis of electroencephalography (EEG) and fMRI data in human systems, cognitive and clinical neurosciences is rapidly evolving and has received substantial attention. The significance of multimodal brain imaging is documented by a steadily increasing number of laboratories now using simultaneous EEG-fMRI aiming to achieve both high temporal and spatial resolution of human brain function. Due to recent developments in technical and algorithmic instrumentation, the rate-limiting step in multimodal studies has shifted from data acquisition to analytic aspects. Here, we introduce and compare different methods for data integration and identify the benefits that come with each approach, guiding the reader toward an understanding and informed selection of the integration approach most suitable for addressing a particular research question.

Simultaneous intracranial EEG-fMRI in humans: protocol considerations and data quality

We have recently performed simultaneous intracranial EEG and fMRI recordings (icEEG-fMRI) in patients with epilepsy. In this technical note, we examine limited thermometric data for potential electrode heating during our protocol and found that heating was ≤0.1 °C in-vitro at least 10 fold less than in-vivo limits. We quantify EEG quality, which can be degraded by MRI scanner-induced artefacts, and fMRI image (gradient echo echo-planar imaging: GE-EPI) signal quality around the electrodes, which can be degraded by electrode interactions with B1 (radiofrequency) and B0 (static) magnetic fields. We recorded EEG outside and within the MRI scanner with and without scanning. EEG quality was largely preserved during scanning and in particular heartbeat-related artefacts were small compared to epileptic events. To assess the GE-EPI signal reduction around the electrodes, we compared image signal intensity along paths into the brain normal to its surface originating from the individual platinum-iridium electrode contacts. GE-EPI images were obtained at 1.5 T with an echo time (TE) of 40 ms and repetition time (TR) of 3000 ms and a slice thickness of 2.5 mm. We found that GE-EPI signal intensity reduction was confined to a 10 mm radius and that it was reduced on average by less than 50% at 5mm from the electrode contacts. The GE-EPI image signal reduction also varied with electrode orientation relative to the MRI scanner axes; in particular, cortical grid contacts with a normal along the scanner's main magnetic field (B(0)) axis have higher artefact levels relative to those with a normal perpendicular to the z-axis. This suggests that the artefacts were predominantly susceptibility-related rather than due to B1 interactions. This information can be used to guide interpretation of results of icEEG-fMRI experiments proximal to the electrodes, and to optimise artefact reduction strategies.

Influence of dense-array EEG cap on fMRI signal

Dense-array (>64 channel) electroencephalography (EEG) systems are increasingly being used in simultaneous EEG-functional magnetic resonance imaging (fMRI) studies. However, with increasing channel count, dense-array EEG caps can induce more severe signal dropout in the MRI images than conventional systems due to the radiofrequency shielding effect of the denser wire bundle. This study investigates the influence of a 256-channel EEG cap on MRI image quality and detection sensitivity of blood oxygen level dependent fMRI signal. A theoretical model is first established to describe the impact of the EEG cap on anatomic signal, noise, signal-to-noise ratio, and contrast-to-noise ratio of blood oxygen level dependent signal. Seven subjects were scanned to measure and compare the T(2)-weighted image quality and fMRI detection sensitivity with and without the EEG cap using an auditory/visual/sensorimotor task. The results show that the dense-array EEG cap can substantially reduce the anatomic signal in the brain areas (visual cortex) near the conducting wires (average percent decrease ≈ 38%). However, the image signal-to-noise ratio with and without the EEG cap was comparable (percent decrease < 8%, not statistically significant), and there was no statistically significant difference in the extent of blood oxygen level dependent activation. This suggests that the ability to detect fMRI signal is nearly unaffected by dense-array EEG caps in simultaneous EEG-fMRI experiments.

A semi-automatic method to determine electrode positions and labels from gel artifacts in EEG/fMRI-studies

The analysis of simultaneous EEG and fMRI data is generally based on the extraction of regressors of interest from the EEG, which are correlated to the fMRI data in a general linear model setting. In more advanced approaches, the spatial information of EEG is also exploited by assuming underlying dipole models. In this study, we present a semi automatic and efficient method to determine electrode positions from electrode gel artifacts, facilitating the integration of EEG and fMRI in future EEG/fMRI data models. In order to visualize all electrode artifacts simultaneously in a single view, a surface rendering of the structural MRI is made using a skin triangular mesh model as reference surface, which is expanded to a "pancake view". Then the electrodes are determined with a simple mouse click for each electrode. Using the geometry of the skin surface and its transformation to the pancake view, the 3D coordinates of the electrodes are reconstructed in the MRI coordinate frame. The electrode labels are attached to the electrode positions by fitting a template grid of the electrode cap in which the labels are known. The correspondence problem between template and sample electrodes is solved by minimizing a cost function over rotations, shifts and scalings of the template grid. The crucial step here is to use the solution of the so-called "Hungarian algorithm" as a cost function, which makes it possible to identify the electrode artifacts in arbitrary order. The template electrode grid has to be constructed only once for each cap configuration. In our implementation of this method, the whole procedure can be performed within 15 min including import of MRI, surface reconstruction and transformation, electrode identification and fitting to template. The method is robust in the sense that an electrode template created for one subject can be used without identification errors for another subject for whom the same EEG cap was used. Furthermore, the method appears to be robust against spurious or missing artifacts. We therefore consider the proposed method as a useful and reliable tool within the larger toolbox required for the analysis of co-registered EEG/fMRI data.

Coupling electrophysiological and hemodynamic responses to errors

Performance errors are associated with distinct electrophysiological and hemodynamic signatures: a fronto-central error-related negativity (ERN) is seen in the event-related potentials and a network of activations including medio-frontal, parietal, and insular cortex is revealed by functional magnetic resonance imaging. We used simultaneous electroencephalography and functional magnetic resonance imaging (fMRI) to characterize the relationship between the electrophysiological and hemodynamic responses to errors. Participants performed a modified Flanker task. When analyzed independently, we found the ERN and hemodynamic activations in dorsal anterior cingulate cortex, superior frontal gyrus, precentral gyrus, inferior frontal gyrus, and inferior parietal lobule. fMRI-informed dipole modeling and joint independent component analysis (ICA) were used to couple electrophysiological and hemodynamic data. Both techniques revealed a temporal evolution of the areas found in the fMRI analysis, with the right hemisphere activations peaking before the left hemisphere. However, joint ICA added information, revealing a number of cortical and subcortical areas that had not been shown with parametric mapping. This technique also uncovered how these areas evolve over time. All together, these analyses provide a more detailed picture of the spatiotemporal dynamics of the processing of performance errors.

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.

Simultaneous measurements of kinematics and fMRI: compatibility assessment and case report on recovery evaluation of one stroke patient

BACKGROUND

Correlating the features of the actual executed movement with the associated cortical activations can enhance the reliability of the functional Magnetic Resonance Imaging (fMRI) data interpretation. This is crucial for longitudinal evaluation of motor recovery in neurological patients and for investigating detailed mutual interactions between activation maps and movement parameters.Therefore, we have explored a new set-up combining fMRI with an optoelectronic motion capture system, which provides a multi-parameter quantification of the performed motor task.

METHODS

The cameras of the motion system were mounted inside the MR room and passive markers were placed on the subject skin, without any risk or encumbrance. The versatile set-up allows 3-dimensional multi-segment acquisitions including recording of possible mirror movements, and it guarantees a high inter-sessions repeatability.We demonstrated the integrated set-up reliability through compatibility tests. Then, an fMRI block-design protocol combined with kinematic recordings was tested on a healthy volunteer performing finger tapping and ankle dorsal- plantar-flexion. A preliminary assessment of clinical applicability and perspectives was carried out by pre- and post rehabilitation acquisitions on a hemiparetic patient performing ankle dorsal- plantar-flexion. For all sessions, the proposed method integrating kinematic data into the model design was compared with the standard analysis.

RESULTS

Phantom acquisitions demonstrated the not-compromised image quality. Healthy subject sessions showed the protocols feasibility and the model reliability with the kinematic regressor. The patient results showed that brain activation maps were more consistent when the images analysis included in the regression model, besides the stimuli, the kinematic regressor quantifying the actual executed movement (movement timing and amplitude), proving a significant model improvement. Moreover, concerning motor recovery evaluation, after one rehabilitation month, a greater cortical area was activated during exercise, in contrast to the usual focalization associated with functional recovery. Indeed, the availability of kinematics data allows to correlate this wider area with a higher frequency and a larger amplitude of movement.

CONCLUSIONS

The kinematic acquisitions resulted to be reliable and versatile to enrich the fMRI images information and therefore the evaluation of motor recovery in neurological patients where large differences between required and performed motion can be expected.

Computational electromagnetic analysis in a human head model with EEG electrodes and leads exposed to RF-field sources at 915 MHz and 1748 MHz

An electromagnetic analysis of a human head with EEG electrodes and leads exposed to RF-field sources was performed by means of Finite-Difference Time-Domain simulations on a 1-mm(3) MRI-based human head model. RF-field source models included a half-wave dipole, a patch antenna, and a realistic CAD-based mobile phone at 915 MHz and 1748 MHz. EEG electrodes/leads models included two configurations of EEG leads, both a standard 10-20 montage with 19 electrodes and a 32-electrode cap, and metallic and high resistive leads. Whole-head and peak 10-g average SAR showed less than 20% changes with and without leads. Peak 1-g and 10-g average SARs were below the ICNIRP and IEEE guideline limits. Conversely, a comprehensive volumetric assessment of changes in the RF field with and without metallic EEG leads showed an increase of two orders of magnitude in single-voxel power absorption in the epidermis and a 40-fold increase in the brain during exposure to the 915 MHz mobile phone. Results varied with the geometry and conductivity of EEG electrodes/leads. This enhancement confirms the validity of the question whether any observed effects in studies involving EEG recordings during RF-field exposure are directly related to the RF fields generated by the source or indirectly to the RF-field-induced currents due to the presence of conductive EEG leads.

Physiological recordings: basic concepts and implementation during functional magnetic resonance imaging

Combining human functional neuroimaging with other forms of psychophysiological measurement, including autonomic monitoring, provides an empirical basis for understanding brain-body interactions. This approach can be applied to characterize unwanted physiological noise, examine the neural control and representation of bodily processes relevant to health and morbidity, and index covert expression of affective and cognitive processes to enhance the interpretation of task-evoked regional brain activity. In recent years, human neuroimaging has been dominated by functional magnetic resonance imaging (fMRI) studies. The spatiotemporal information of fMRI regarding central neural activity is valuably complemented by parallel physiological monitoring, yet such studies still remain in the minority. This review article highlights fMRI studies that employed cardiac, vascular, respiratory, electrodermal, gastrointestinal and pupillary psychophysiological indices to address specific questions regarding interaction between brain and bodily state in the context of experience, cognition, emotion and behaviour. Physiological monitoring within the fMRI environment presents specific technical issues, most importantly related to safety. Mechanical and electrical hazards may present dangers to scanned subjects, operator and/or equipment. Furthermore, physiological monitoring may interfere with the quality of neuroimaging data, or itself be compromised by artefacts induced by the operation of the scanner. We review the sources of these potential problems and the current approaches and advice to enable the combination of fMRI and physiological monitoring in a safe and effective manner.

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.

Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship.

Realignment parameter-informed artefact correction for simultaneous EEG-fMRI recordings

In this work we introduce a new algorithm to correct the imaging artefacts in the EEG signal measured during fMRI acquisition. The correction techniques proposed so far cannot optimally represent transitions, i.e. when abrupt changes of the artefact properties due to head movements occur. The algorithm developed here takes the head movement parameters from the fMRI signal into account to calculate adequate EEG artefact templates and subsequently correct the distorted EEG data. The data reported in this work demonstrate that the realignment parameter-informed algorithm outperforms the commonly used moving average algorithm if head movements occur. The superiority is reflected by comparing the residual variance after artefact correction with either method. The residual variance is lower around head-movements that exceed head deflections of about 1 mm when applying the realignment parameter-informed algorithm. Additionally, the signal to noise ratio of a surrogate event-related potential (ERP) increased by 10-40% for head displacements larger than 1 mm. The algorithm developed here is particularly suited for studies where head movements of the subject cannot be prevented as in studies with patients, children, or during sleep. Furthermore, the enhanced signal to noise ratio of a single trial ERP indicates the power of the presented algorithm for single trial ERP-fMRI studies in which EEG signal quality is a critical factor.

The utility of conductive plastic electrodes in prolonged ICU EEG monitoring

We investigated the feasibility and utilization of conductive plastic electrodes (CPEs) in patients undergoing continuous video-electroencephalographic (EEG) monitoring in the intensive care unit (ICU), and assessed the quality of brain magnetic resonance imaging (MRI) and computed tomography (CT) images obtained during this period. A total of 54 patients were monitored. Seizures were recorded in 16 patients. Twenty-five patients had neuroimaging performed with electrodes in place; 15 MRI and 23 CT scans were performed. All patients had excellent quality anatomical images without clinically significant artifacts, and without any signs or symptoms that raised safety concerns. Recording quality of the EEG was indistinguishable to that achieved with standard gold electrodes. The use of CPEs allowed for uninterrupted EEG recording of patients who required urgent neuroimaging, and decreased the amount of time spent by the technologists required to remove and reattach leads.

Simultaneous recording of EEG and BOLD responses: A historical perspective

Electromagnetic fields as measured with electroencephalogram (EEG) are a direct consequence of neuronal activity and feature the same timescale as the underlying cognitive processes, while hemodynamic signals as measured with functional magnetic resonance imaging (fMRI) are related to the energy consumption of neuronal populations. It is obvious that a combination of both techniques is a very attractive aim in neuroscience, in order to achieve both high temporal and spatial resolution for the non-invasive study of cognitive brain function. During the last decade a number of research groups have taken up this challenge. Here, we review the development of the combined EEG-fMRI approach. We summarize the main data integration approaches developed to achieve such a combination, discuss the current state-of-the-art in this field and outline challenges for the future success of this promising approach.