Restoration of MR-induced artifacts in simultaneously recorded MR/EEG data

Broadly applicable methods for the detection of artefacts in electroencephalography acquired simultaneously with hemodynamic recordings

Electroencephalography (EEG) data, acquired simultaneously with magnetic resonance imaging (MRI), must be corrected for artefacts related to MR gradient switches (GS) and the cardioballistic (CB) effect. Canonical approaches require additional signal acquisition for artefact detection (e.g., MR volume onsets, ECG), without which the EEG data would be rendered uncleanable from these artefacts.•We present two broadly applicable methods for artefact detection based on peak detection combined with temporal constraints with respect to periodicity directly from the EEG data itself; no additional signals are required. We validated the performance of our methods versus the two canonical approaches for detection of GS/CB artefact, respectively, on 26 healthy human EEG-functional MRI resting-state datasets. Utilising various performance metrics, we found our methods to perform as well as - and sometimes better than - the canonical standard approaches. With as little as one EEG channel recording, our methods can be applied to detect GS/CB artefacts in EEG data acquired simultaneously with MRI in the absence of MR volume onsets and/or an ECG recording. The detected artefact onsets can then be fed into the standard artefact correction software.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

OBJECTIVE

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools.

APPROACH

The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain.

MAIN RESULTS

This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways.

SIGNIFICANCE

These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Multimodal investigation of epileptic networks: The case of insular cortex epilepsy

The insula is a deep cortical structure sharing extensive synaptic connections with a variety of brain regions, including several frontal, temporal, and parietal structures. The identification of the insular connectivity network is obviously valuable for understanding a number of cognitive processes, but also for understanding epilepsy since insular seizures involve a number of remote brain regions. Ultimately, knowledge of the structure and causal relationships within the epileptic networks associated with insular cortex epilepsy can offer deeper insights into this relatively neglected type of epilepsy enabling the refining of the clinical approach in managing patients affected by it. In the present chapter, we first review the multimodal noninvasive tests performed during the presurgical evaluation of epileptic patients with drug refractory focal epilepsy, with particular emphasis on their value for the detection of insular cortex epilepsy. Second, we review the emerging multimodal investigation techniques in the field of epilepsy, that aim to (1) enhance the detection of insular cortex epilepsy and (2) unveil the architecture and causal relationships within epileptic networks. We summarize the results of these approaches with emphasis on the specific case of insular cortex epilepsy.

An improved artifacts removal method for high dimensional EEG

BACKGROUND

Multiple noncephalic electrical sources superpose with brain signals in the recorded EEG. Blind source separation (BSS) methods such as independent component analysis (ICA) have been shown to separate noncephalic artifacts as unique components. However, robust and objective identification of artifact components remains a challenge in practice. In addition, with high dimensional data, ICA requires a large number of observations for stable solutions. Moreover, using signals from long recordings to provide the large observation set might violate the stationarity assumption of ICA due to signal changes over time.

NEW METHOD

Instead of decomposing all channels simultaneously, subsets of channels are randomly selected and decomposed with ICA. With reduced dimensionality of the subsets, much less amount of data is required to derive stable components. To characterize each independent component, an artifact relevance index (ARI) is calculated by template matching each component with a model of the artifact. Automatic artifact identification is then implemented based on the statistical distribution of ARI of the numerous components generated.

RESULTS

The proposed permutation resampling for identification matching (PRIM) method effectively removed eye blink artifacts from both simulated and real EEG.

COMPARISON WITH EXISTING METHOD

The average topomap correlation coefficient between the cleaned EEG and the ground truth is 0.89±0.01 for PRIM, compared with 0.64±0.05 for conventional ICA based method. The average relative root-mean-square error is 0.40±0.01 for PRIM, compared with 0.66±0.10 for conventional method.

CONCLUSIONS

The proposed method overcame limitations of conventional ICA based method and succeeded in removing eye blink artifacts automatically.

Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals

To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient (ρ), mean of coherence function (C), and rate of absolute error (RAE). All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with ρ>0.85, C>0.8, and RAE<0.5. These values were significantly better than the performance of LMS adaptive filter (ρ<0.85, C<0.6, and RAE>1).

Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphasis is directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac timing markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable with the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use.

Adaptive noise cancellation to suppress electrocardiography artifacts during real-time interventional MRI

PURPOSE

To develop a system for artifact suppression in electrocardiogram (ECG) recordings obtained during interventional real-time magnetic resonance imaging (MRI).

MATERIALS AND METHODS

We characterized ECG artifacts due to radiofrequency pulses and gradient switching during MRI in terms of frequency content. A combination of analog filters and digital least mean squares adaptive filters were used to filter the ECG during in vivo experiments and the results were compared with those obtained with simple low-pass filtering. The system performance was evaluated in terms of artifact suppression and ability to identify arrhythmias during real-time MRI.

RESULTS

Analog filters were able to suppress artifacts from high-frequency radiofrequency pulses and gradient switching. The remaining pulse artifacts caused by intermittent preparation sequences or spoiler gradients required adaptive filtering because their bandwidth overlapped with that of the ECG. Using analog and adaptive filtering, a mean improvement of 38 dB (n = 11, peak QRS signal to pulse artifact noise) was achieved. This filtering system was successful in removing pulse artifacts that obscured arrhythmias such as premature ventricular complexes and complete atrioventricular block.

CONCLUSION

We have developed an online ECG monitoring system employing digital adaptive filters that enables the identification of cardiac arrhythmias during real-time MRI-guided interventions.

Adaptive removal of gradients-induced artefacts on ECG in MRI: a performance analysis of RLS filtering

One of the main vital signs used in patient monitoring during Magnetic Resonance Imaging (MRI) is Electro-Cardio-Gram (ECG). Unfortunately, magnetic fields gradients induce artefacts which severely affect ECG quality. Adaptive Noise Cancelling (ANC) is one of the preferred techniques for artefact removal. ANC involves the adaptive estimation of the impulse response of the system constituted by the MRI equipment, the patient and the ECG recording device. Least Mean Square (LMS) adaptive filtering has been traditionally employed because of its simplicity: anyway, it requires the choice of a step-size parameter, whose proper value for the specific application must be estimated case by case: an improper choice could yield slow convergence and unsatisfactory behaviour. Recursive Least Square (RLS) algorithm has, potentially, faster convergence while not requiring any parameter. As far as the authors' knowledge, there is no systematic analysis of performances of RLS in this scenario. In this study we evaluated the performance of RLS for adaptive removal of artefacts induced by magnetic field gradients on ECG in MRI, in terms of efficacy of suppression. Tests have been made on real signals, acquired via an expressly developed system. A comparison with LMS was made on the basis of opportune performance indices. Results indicate that RLS is superior to LMS in several respects.

Simultaneously recorded EEG-fMRI: removal of gradient artifacts by subtraction of head movement related average artifact waveforms

Electroencephalograms (EEGs) recorded simultaneously with functional magnetic resonance imaging (fMRI) are corrupted by large repetitive artifacts generated by the switched MR gradients. Several methods have been proposed to remove these distortions by subtraction of averaged artifact templates from the ongoing EEG. Here, we present a modification of this approach which accounts for head movements to improve the extracted template. Using the fMRI analysis package statistical parametric mapping (SPM; FIL London) the head displacement is determined at each half fMRI-volume. The basic idea is to apply a moving average algorithm for template extraction but to include only epochs that were obtained at the same head position as the artefact to be removed. This approach was derived from phantom EEG measurements demonstrating substantial variations of the artefact waveform in response to movements of the phantom in the MRI magnet. To further reduce the residual noise, we applied a resampling algorithm which aligns the EEG samples in a strict adaptive manner to the fMRI timing. Finally, we propose a new algorithm to suppress residual artifacts such as those occasionally observed in case of brief strong movements, which are not reflected by the movement indicator because of the limited temporal resolution of the fMRI sequence. On the basis of EEG recordings of six subjects these measures combined reduce the residual artefact activity quantified in terms of the spectral power at the gradient repetition rate and its harmonics by roughly 20 to 50% (depending on the amount of movement) predominantly in frequencies beyond 30 Hz.

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.

Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG

Noninvasive functional neuroimaging, as an important tool for basic neuroscience research and clinical diagnosis, continues to face the need of improving the spatial and temporal resolution. While existing neuroimaging modalities might approach their limits in imaging capability mostly due to fundamental as well as technical reasons, it becomes increasingly attractive to integrate multiple complementary modalities in an attempt to significantly enhance the spatiotemporal resolution that cannot be achieved by any modality individually. Electrophysiological and hemodynamic/metabolic signals reflect distinct but closely coupled aspects of the underlying neural activity. Combining fMRI and EEG/MEG data allows us to study brain function from different perspectives. In this review, we start with an overview of the physiological origins of EEG/MEG and fMRI, as well as their fundamental biophysics and imaging principles, we proceed with a review of the major advances in the understanding and modeling of neurovascular coupling and in the methodologies for the fMRI-EEG/MEG simultaneous recording. Finally, we summarize important remaining issues and perspectives concerning multimodal functional neuroimaging, including brain connectivity imaging.

EEG monitoring during functional MRI in animal models

Despite its excellent temporal resolution, electroencephalogram (EEG) has poor spatial resolution to study the participation of different brain areas in epileptic discharges, and the propagation of seizures to subcortical areas is not revealed. Furthermore, EEG provides no information about metabolic changes that occur in the brain before and during the epileptic discharges. Thus, monitoring variations in blood flow and oxygenation in response to epileptic discharges can provide additional complementary information. Functional magnetic resonance imaging (fMRI) technology can be used to study the hemodynamic changes associated with interictal epileptiform discharges or epileptic seizures (i.e., before, during or after them) in experimental animal models and may noninvasively monitor these changes over time. Blood oxygenation level-dependent fMRI has superior spatial resolution compared with other functional imaging modalities and utilizes changes in local magnetic field properties to measure the amount of deoxyhemoglobin in each brain areas as an indicator of brain activity. Simultaneous recording of EEG and fMRI is required to achieve this objective. This article describes methods of acquiring and monitoring EEG during fMRI studies in experimental animals. Particular attention will be paid to methods used to eliminate artifacts induced in the acquired magnetic resonance images by EEG equipment and MR-related artifacts in EEG recordings.

Artifact removal in co-registered EEG/fMRI by selective average subtraction

OBJECTIVE

Co-registration of EEG (electroencephalogram) and fMRI (functional magnetic resonance imaging) remains a challenge due to the large artifacts induced on the EEG by the MR (magnetic resonance) sequence magnetic fields. Thus, we present an algorithm, based on the average-subtraction method, which is able to correct EEG data for gradient and pulse artifacts.

METHODS

MR sequence timing parameters are estimated from the EEG data and both slice and volume artifact templates are subtracted from the data. A clustering algorithm is proposed to account for the variability of the pulse artifact.

RESULTS

The algorithm is able to keep the spontaneous EEG as well as visual evoked potentials (VEPs), while removing gradient and pulse artifacts with only a subtraction of selectively averaged data. In the frequency domain, the artifact frequencies are strongly attenuated. Estimated MR sequence time parameters showed that the correction is extremely sensitive to the slice time value. Pulse artifact clustering showed that most of the variability is due to the time jitter of the pulse artifact markers.

CONCLUSIONS

Selective subtraction of averages in combination with proper time alignment is enough to remove most of the MR-induced artifacts.

SIGNIFICANCE

Clean EEG can be obtained from raw signals that are corrupted by MR-induced artifacts during simultaneous EEG-fMRI scanning without using dedicated hardware to synchronize EEG and fMRI clocks.

A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI

In electroencephalographic (EEG) measurements performed during functional Magnetic Resonance Imaging (fMRI), imaging and cardiac artefacts strongly contaminate the EEG signal. Several algorithms have been proposed to suppress these artefacts and most of them have shown important improvements with respect to uncorrected signals. However, the relative performances of these algorithms have not been properly assessed. In particular, it is not known to what extent such algorithms deteriorate the EEG signal of interest. In this study, we propose to cross-validate different methods proposed for artefact correction, using a forward model to generate EEG and MR-related artefacts. The methods are assessed under various experimental conditions (described in terms of EEG sampling rate, artefacts amplitude, frequency band of interest, etc.). Using experimental data, we also tested the performance of the correction methods for alpha rhythm imaging and for epileptic spike reconstruction. Results show that most of the methods allow the observation of the modulation of alpha rhythms and the identification of spikes, despite subtle differences between algorithms. They also show that over-filtering the data may degrade the EEG. Our results indicate that the optimal artefact removal technique should be chosen according to whether one is interested in fast (>10 Hz) vs. slow (<10 Hz) oscillations or in evoked vs. ongoing activity.

Current status of functional MRI on small animals: application to physiology, pathophysiology, and cognition

This review aims to make the reader aware of the potential of functional MRI (fMRI) in brain activation studies in small animal models. As small animals generally require anaesthesia for immobilization during MRI protocols, this is believed to be a serious limitation to the type of question that can be addressed with fMRI. We intend to introduce a fresh view with an in-depth overview of the surprising number of fMRI applications in a wide range of important research domains in neuroscience. These include the pathophysiology of brain functioning, the basic science of activity, and functional connectivity of different sensory circuits, including sensory brain mapping, the challenges when studying the hypothalamus as the major control centre in the central nervous system, and the limbic system as neural substrate for emotions and reward. Finally the contribution of small animal fMRI research to cognitive neuroscience is outlined. This review avoids focusing exclusively on traditional small laboratory animals such as rodents, but rather aims to broaden the scope by introducing alternative lissencephalic animal models such as songbirds and fish, as these are not yet well recognized as neuroimaging study subjects. These models are well established in many other neuroscience disciplines, and this review will show that their investigation with in vivo imaging tools will open new doors to cognitive neuroscience and the study of the autonomous nervous system in experimental animals.

Evaluating gradient artifact correction of EEG data acquired simultaneously with fMRI

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has become a widely used application in spite of EEG perturbations due to electromagnetic interference in the MR environment. The most prominent and disturbing artifacts in the EEG are caused by the alternating magnetic fields (gradients) of the MR scanner. Different methods for gradient artifact correction have been developed. Here we propose an approach for the systematic evaluation and comparison of these gradient artifact correction methods. Exemplarily, we evaluate different algorithms all based on artifact template subtraction--the currently most established means of gradient artifact removal. We introduce indices for the degree of gradient artifact reduction and physiological signal preservation. The combination of both indices was used as a measure for the overall performance of gradient artifact removal and was shown to be useful in identifying problems during artifact removal. We demonstrate that the evaluation as proposed here allows to reveal frequency-band specific performance differences among the algorithms. This emphasizes the importance of carefully selecting the artifact correction method appropriate for the respective case.

Optimisation of a post-processing method to remove the pulse artifact from EEG data recorded during fMRI: An application to P300 recordings during e-fMRI

In functional cerebral studies, it has been established that co-registered electroencephalography (EEG) measurements and functional magnetic resonance imaging (fMRI) were complementary. However, EEG data recorded inside an MRI scanner are heavily distorted, mainly by the most prominent artifact, the cardiac pulse artifact (PA). We describe an original algorithm which yields a high-quality PA filter and demonstrates how this tool can be used to improve the quality of P300 ERP measurements during event-related fMRI (e-fMRI) experiments. EEG data were acquired in interleaved mode during e-fMRI while six healthy volunteers performed a visual odd-ball task, involving Distractors, Target and Novel stimuli, to elicit P300 components. The PA was corrected with the original algorithm. The temporal variations in the PA were evidenced using a principal component analysis (PCA), on each EEG channel. The procedure yielded several PA templates, which were regressed from the EEG data. The PA removal procedure was optimised, and then implemented to improve the measured P300 components. Regressing the most adequate PA template resulted in a high-quality reduction in spectral power at frequencies associated with the cardiac PA. More reliable P300 component measurements were obtained, evidencing higher amplitudes for Novels (9.76-11.20 microV) than for to Targets (6.3-9.09 microV) in centro-parietal and prefrontal areas. The improvement of the processing of EEG data acquired simultaneously with fMRI data provides a new tool and casts perspectives to study the functional organisation of the brain.

Accurate Real-Time Feedback of Surface EMG During fMRI

Real-time acquisition of EMG during functional MRI (fMRI) provides a novel method of controlling motor experiments in the scanner using feedback of EMG. Because of the redundancy in the human muscle system, this is not possible from recordings of joint torque and kinematics alone, because these provide no information about individual muscle activation. This is particularly critical during brain imaging because brain activations are not only related to joint torques and kinematics but are also related to individual muscle activation. However, EMG collected during imaging is corrupted by large artifacts induced by the varying magnetic fields and radio frequency (RF) pulses in the scanner. Methods proposed in literature for artifact removal are complex, computationally expensive, and difficult to implement for real-time noise removal. We describe an acquisition system and algorithm that enables real-time acquisition for the first time. The algorithm removes particular frequencies from the EMG spectrum in which the noise is concentrated. Although this decreases the power content of the EMG, this method provides excellent estimates of EMG with good resolution. Comparisons show that the cleaned EMG obtained with the algorithm is, like actual EMG, very well correlated with joint torque and can thus be used for real-time visual feedback during functional studies.

Improving automatic analysis of the electrocardiogram acquired during magnetic resonance imaging using magnetic field gradient artefact suppression

The electrocardiogram (ECG) used for patient monitoring during magnetic resonance imaging (MRI) unfortunately suffers from severe artefacts. These artefacts are due to the special environment of the MRI. Modeling helped in finding solutions for the suppression of these artefacts superimposed on the ECG signal. After we validated the linear and time invariant model for the magnetic field gradient artefact generation, we applied offline and online filters for their suppression. Wiener filtering (offline) helped in generating reference annotations of the ECG beats. In online filtering, the least-mean-square filter suppressed the magnetic field gradient artefacts before the acquired ECG signal was input to the arrhythmia algorithm. Comparing the results of two runs (one run using online filtering and one run without) to our reference annotations, we found an eminent improvement in the arrhythmia module's performance, enabling reliable patient monitoring and MRI synchronization based on the ECG signal.

On the effect of resistive EEG electrodes and leads during 7 T MRI: simulation and temperature measurement studies

The purpose of the study was to assess the effects of electrodes and leads on electromagnetic field and specific absorption rate (SAR) distributions during simultaneous electroencephalography (EEG) and 7-T MRI. Two different approaches were evaluated and compared to the case without electrodes: (a) the use of different EEG lead resistivity and (b) the use of a radiofrequency (RF) resistor on the lead near the EEG electrode. These configurations are commonly used in research and clinical settings. Electromagnetic field and SAR distributions generated by the transmit RF coil were evaluated using finite difference time domain simulations on an anatomically accurate head model. The spatiotemporal changes of temperature were estimated with the heat equation. Temperature changes during turbo spin echo sequences were also measured using a custom-made phantom: the conductive head mannequin anthropomorphic (CHEMA). The results of this study showed that the SAR and temperature distributions in CHEMA (a) increased when using low resistive leads, with respect to the no-electrode case; (b) were affected by the resistivity of the EEG leads, with carbon fiber leads performing better than standard copper leads; and (c) were not affected by the use of an RF resistor between the EEG electrode and the lead.

Artifact reduction for simultaneous EEG/fMRI recording: Adaptive FIR reduction of imaging artifacts

OBJECTIVE

We present a new method of effectively removing imaging artifacts of electroencephalography (EEG) and extensively conserving the time-frequency features of EEG signals during simultaneous functional magnetic resonance imaging (fMRI) scanning under conventional conditions.

METHODS

Under the conventional conditions of a 5000 Hz EEG sampling rate, but in the absence of the MRI slice-timing signals, the imaging artifact during each slice scanning is theoretically inferred to be a linear combination of the average artifact waveform and its derivatives, deduced by band-limited Taylor's expansion. Technically, the imaging artifact reduction algorithm is equivalent to an adaptive finite impulse response (FIR) filter.

RESULTS

The capability of this novel method removing the imaging artifacts of EEG recording during fMRI scanning has been demonstrated by a phantom experiment. Moreover, the effectiveness of this method in conserving the time-frequency features of EEG activity has been evaluated by both visually evoked experiments and alpha waves.

CONCLUSIONS

The adaptive FIR method is an effective method of removing the imaging artifacts under conventional conditions, and also conserving the time-frequency EEG signals.

SIGNIFICANCE

The proposed adaptive FIR method, removing the imaging artifacts, combined with the wavelet-based non-linear noise reduction (WNNR) method [Wan X, Iwata K, Riera J, Ozaki T, Kitamura M, Kawashima R. Artifact reduction for EEG/fMRI recording: Nonlinear reduction of ballistocardiogram artifacts. Clin Neurophysiol 2006;117:668-80], reducing the ballistocardiogram artifacts (BAs), makes it feasible to obtain accurate EEG signals from the simultaneous EEG recordings during fMRI scanning.

Simultaneous EEG–fMRI

Acquisition of electroencephalogram (EEG) during functional magnetic resonance imaging (fMRI) provides an additional monitoring tool for the analysis of brain state fluctuations. The exploration of brain responses following inputs or in the context of state changes is crucial for a better understanding of the basic principles governing large-scale neuronal dynamics. State-of-the-art techniques allow EEG activity-from DC (direct current) up to high frequencies in the gamma range-to be acquired simultaneously with fMRI data. In the interleaved mode, spiking activities can also be assessed during concurrent fMRI. The utilization of fMRI evidence to better constrain solutions of the inverse problem of source localization of EEG activity is an exciting possibility. Nonetheless, this approach should be applied cautiously since the degree of overlap between underlying neuronal activity sources is variable and, for the most part, unknown. The ultimate goal is to make joint inferences about the activity, dynamics, and functions by exploiting complementary information from multimodal data sets.

Physiological monitoring of small animals during magnetic resonance imaging

Maintaining a stable physiologic state is essential when studying animal models of epilepsy with simultaneous electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI) or EEG and magnetic resonance spectroscopy (MRS). To achieve and maintain such stability in rats in the MRI environment, a minimally invasive but comprehensive system was developed to monitor body temperature, heart rate, blood pressure, blood oxygen saturation and end-tidal CO2 (ETCO2) of expired gas. All physiologic parameters were successfully monitored in Sprague-Dawley rats without interfering with EEG recordings during simultaneous fMRI and MRS studies. Body temperature, heart rate, blood pressure, blood oxygen saturation, and ETCO2, were maintained between 36.5 and 37.5 degrees C, 250-450 beats/min, 136+/-17 mmHg, >90%, and 20-35 mmHg, respectively for 6-8 h under inhalational anesthesia. This set-up could be extended to study in vivo applications in other laboratory animals with only minor modifications.

Real-time display of artifact-free electroencephalography during functional magnetic resonance imaging and magnetic resonance spectroscopy in an animal model of epilepsy

Simultaneous recording of electroencephalogram (EEG) and functional MRI (fMRI) or MR spectroscopy (MRS) can provide further insight into our understanding of the underlying mechanisms of neurologic disorders. Current technology for simultaneous EEG and MRI recording is limited by extensive postacquisition processing of the data. Real-time display of artifact-free EEG recording during fMRI/MRS studies is essential in studies that involve epilepsy to ensure that they address specific EEG features such as epileptic spikes or seizures. By optimizing the EEG recording equipment to maximize the common mode rejection ratio of its amplifiers, a unique EEG system was designed and tested that allowed real-time display of the artifact-free EEG during fMRI/MRS in an animal model of epilepsy. Spike recordings were optimized by suppression of the background EEG activity using fast-acting and easily controlled inhalational anesthesia. Artifact suppression efficiency of 70-100% was achieved following direct subtraction of referentially recorded filtered EEG tracings from active electrodes, which were located in close proximity to each other (over homologous occipital cortices) and a reference electrode. Two independent postacquisition processing tools, independent component analysis and direct subtraction of unfiltered digital EEG data in MATLAB, were used to verify the accuracy of real-time EEG display.

P300 recordings during event-related fMRI: a feasibility study

Analysis of combined event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) can provide a high temporal and high spatial resolution to study functional cerebral processes. However, EEG data recorded inside an MR scanner is heavily distorted by artifacts. It is important in cognitive studies to ensure that recorded data reflect the same brain activity, and this is achieved through interleaved electroencephalographic (EEG) and fMRI measurements. Here, we demonstrate the feasibility of recording P300 ERPs during fMRI using a three-stimulus visual oddball task and involving a small number of trials for each stimulus. Ten EEG channels were acquired interleaved with fMRI images in five healthy subjects. The stimuli, including rare targets "X," frequent repetitive distractors "O," and rare distractors referred to as novels, were randomly presented every 2 +/- 1 s. The post hoc filter presented here was designed and applied to EEG data to remove the cardiac pulse artifact. Interleaved EEG/fMRI acquisition evidenced two P300 ERPs evoked at Fz, Cz, and Pz by targets and novels. Novel-related ERPs were of higher amplitude than their target-related counterparts. The fMRI maps acquired concurrently showed stronger BOLD response for target condition. We have shown that interleaved acquisition allows to obtain reliable P300 data and fMRI results, likely to shed light on the anatomical location of brain regions involved in cognitive ERPs relevant to many disorders affecting CNS functions. These noninvasive multimodal neuroimaging techniques can be used to explore and better understand processes underlying the functional brain organization.

Electroencephalography-correlated functional MR imaging studies of epileptic activity

EEG-fMRI is capable of providing novel localizing information in a substantial proportion of patients with frequent epileptiform discharges, even at 1.5T, suggesting a potential clinical role. Increasing availability of equipment capable of providing good-quality intra-MR imaging EEG with relative ease should ensure a more widespread application of the technique in neurology and neuroscience. Early findings in epilepsy have raised a number of interesting issues related to the localization of the activations and the time course of the event-related response that require further investigation. These investigations will benefit from substantial increases in sensitivity resulting from recent and forthcoming technical developments. Validation of the findings will require comparison with invasive and postsurgical findings and further correlations with other measures of brain activity,which should also lead to an improved understanding of the underlying phenomena. To facilitate comparison of findings, improved reporting standards are needed-namely, illustration of activation maps using the glass-brain-more consistent threshold selection,and the listing of all activation clusters, including their volumes, maximum (or minimum) z scores, and peak signal change.

Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection

fMRI and EEG are complimentary methods for the analysis of brain activity since each method has its strength where the other one has limits: The spatial resolution is thus in the range of millimeters with fMRI and the time resolution is in the range of milliseconds with EEG. For a comprehensive understanding of brain activity in target detection, nine healthy subjects (age 24.2 +/- 2.9) were investigated with simultaneous EEG (27 electrodes) and fMRI using an auditory oddball paradigm. As a first step, event-related potentials, measured inside the scanner, have been compared with the potentials recorded in a directly preceding session in front of the scanner. Attenuated amplitudes were found inside the scanner for the earlier N1/P2 component but not for the late P300 component. Second, an independent analysis of the localizations of the fMRI activations and the current source density as revealed by low resolution electromagnetic tomography (LORETA) has been done. Concordant activations were found in most regions, including the temporoparietal junction (TPJ), the supplementary motor area (SMA)/anterior cingulate cortex (ACC), the insula, and the middle frontal gyrus, with a mean Euclidean distance of 16.0 +/- 6.6 mm between the BOLD centers of gravity and the LORETA-maxima. Finally, a time-course analysis based on the current source density maxima was done. It revealed different time-course patterns in the left and right hemisphere with earlier activations in frontal and parietal regions in the right hemisphere. The results suggest that the combination of EEG and fMRI permits an improved understanding of the spatiotemporal dynamics of brain activity.

Improved ballistocardiac artifact removal from the electroencephalogram recorded in fMRI

The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance image (fMRI) is a promising tool that is capable of providing high spatiotemporal brain mapping, with each modality supplying complementary information. One of the major barriers to obtain high-quality simultaneous EEG/fMRI data is that pulsatile activity due to the heartbeat induces significant artifacts in the EEG. The purpose of this study was to develop a novel algorithm for removing heartbeat artifact, thus overcoming problems associated with previous methods. Our method consists of a mean artifact wave form subtraction, the selective removal of wavelet coefficients, and a recursive least-square adaptive filtering. The recursive least-square adaptive filtering operates without dedicated sensor for the reference signal, and only when the mean subtraction and wavelet-based noise removal is not satisfactory. The performance of our system has been assessed using simulated data based on experimental data of various spectral characteristics, and actual experimental data of alpha-wave-dominant normal EEG and epileptic EEG.

Metallic electrodes and leads in simultaneous EEG-MRI: Specific absorption rate (SAR) simulation studies

The purpose of this study was to investigate the changes in specific absorption rate (SAR) in human-head tissues while using nonmagnetic metallic electroencephalography (EEG) electrodes and leads during magnetic resonance imaging (MRI). A realistic, high resolution (1 mm(3)) head model from individual MRI data was adopted to describe accurately thin tissues, such as bone marrow and skin. The RF power dissipated in the human head was evaluated using the FDTD algorithm. Both surface and bird cage coils were used. The following numbers of EEG electrodes/leads were considered: 16, 31, 62, and 124. Simulations were performed at 128 and 300 MHz. The difference in SAR between the electrodes/leads and no-electrodes conditions was greater with the bird cage coil than with the surface coil. The peak 1 g averaged SAR values were highest at 124 electrodes, increasing to as much as two orders of magnitude (x172.3) at 300 MHz compared to the original value. At 300 MHz, there was a fourfold (x3.6) increase of SAR averaged over the bone marrow, and a sevenfold (x7.4) increase in the skin. At 128 MHz, there was a fivefold (x5.6) increase of whole head SAR. Head models were obtained from two different subjects, with an inter-subject whole head SAR variability of 3%. .

EEG quality during simultaneous functional MRI of interictal epileptiform discharges

This article concerns the evaluation of the quality of interictal epileptiform EEG discharges recorded throughout simultaneous echo planar imaging (EPI). BOLD (blood oxygen level dependent) functional MRI (fMRI) images were acquired continuously on a patient with intractable epilepsy. EEG was sampled simultaneously, during and after imaging, with removal of pulse and imaging artifacts by subtraction of channel-specific running averages. Contiguous EEG epochs recorded with and without fMRI (fMRI+ve vs. fMRI-ve) were next randomized and presented to two blinded observers. Epileptiform discharges were identified retrospectively, and comparison was made in terms of the number of identified events, their amplitude, and spatiotemporal distribution. A spectral analysis was also performed on the EEG. In the randomized comparison of EEG segments, 80 (fMRI+ve) vs. 69 (fMRI-ve) discharges were noted with good interobserver agreement (69%). There were no significant differences in amplitude or spatio-temporal distribution. Comparison of the events detected and measured by two expert observers demonstrated that the Interictal Epileptiform Discharge (IED) characteristics were indistinguishable with and without scanning. We review briefly the existing literature on EEG recording quality for combined EEG/fMRI.

Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy

We used simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) and EEG-near infrared spectroscopy (NIRS) to investigate whether changes of the posterior EEG alpha rhythm are correlated with changes in local cerebral blood oxygenation. Cross-correlation analysis of slowly fluctuating, spontaneous rhythms in the EEG and the fMRI signal revealed an inverse relationship between alpha activity and the fMRI-blood oxygen level dependent signal in the occipital cortex. The NIRS-EEG measurements demonstrated a positive cross-correlation in occipital cortex between alpha activity and concentration changes of deoxygenated hemoglobin, which peaked at a relative shift of about 8 s. Our data suggest that alpha activity in the occipital cortex is associated with metabolic deactivation. Mapping of spontaneously synchronizing distributed neuronal networks is thus shown to be feasible.

Simultaneous electroencephalographic recording and functional magnetic resonance imaging during pentylenetetrazol-induced seizures in rat

Truly simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) were registered in curarized rats injected with convulsive doses of pentylenetetrazol (PTZ, 65 mg/kg, sc). Rigorous control of physiological parameters like body temperature and ventilation with control of blood gasses helped to avoid potential interference between systemic parameters, and central PTZ-induced blood oxygenation level-dependent (BOLD) changes. Simultaneous EEG/fMRI recordings demonstrated progressive epileptiform EEG discharges with concomitant BOLD changes, the latter gradually affecting most of the fore- and midbrain. Approximately 15 min after PTZ injection, the first BOLD contrast changes mainly occurred in neocortex, and coincided with the first minor EEG alterations. Most regions that displayed BOLD changes were regions with reportedly high GABA(A) receptor densities. Full-blown epileptiform discharges occurred on the EEG tracing, approximately 30 min after PTZ injection, and coincided with bilateral positive and/or negative BOLD contrast changes in cortical and subcortical regions. Behavioral observations demonstrated the first of several generalized clonic or clonic-tonic seizure episodes to occur also around this time. Approximately 90 min after injection, the electrographic paroxysms gradually decreased in amplitude and duration, whereas the BOLD signal changes still extended with alternating positive and negative traces, and spread to subcortical regions like caudate-putamen and globus pallidus.

Quality of EEG in simultaneous EEG-fMRI for epilepsy

It is now possible to record the EEG continuously during fMRI studies. This is a very promising methodology that combines knowledge about neuronal activity and its metabolic response. The EEG recorded inside the fMRI scanner is, however,heavily contaminated by artifacts caused by the high intensity magnetic field and rapidly changing field gradients. Methods have been reported in the literature to reduce or eliminate these artifacts, in particular the ballistocardiogram and the artifact caused by currents induced by rapidly changing magnetic gradients. Nevertheless, recording the EEG simultaneously with fMRI remains an extremely delicate operation. In addition the use of artifact removal methods has only been reported by the laboratories in which they were developed. We report here the practical procedures we developed to reduce artifacts in a series of 10 epileptic patients, in the context of the visualization of epileptic spikes. We illustrate the effectiveness of methods designed to remove the scanning artifact and present new methods for removing the ballistocardiographic artifact. We present and evaluate techniques to obtain an EEG of good quality when performing simultaneous EEG and fMRI studies.

A data post-processing protocol for dynamic MRI data to discriminate brain activity from global physiological effects

All fMRI techniques measure stimulus induced focal metabolic and physiological changes in activated brain areas. During the entire fMRI experiment it is necessary to maintain the general physiological condition of the subject as stable as possible. This is not always an easy task. The typical block design in standard fMRI experiments minimizes most of the problems related with these general physiological changes. However in some fMRI experiments, like pharmacological MRI, the experimental setup makes the use of a blocked design impossible. Therefore signal correction algorithms have been developed to correct for these physiological signal instabilities. These algorithms often require elaborate calculation efforts and the data interpretation is often very difficult if no prior knowledge on the nature of the changes exists. In this work we present an algorithm, which has the advantage of being low in calculation effort and the resulting data after correction are easy to interpret. It makes use of a datafit between the general physiological and focal activation related signal changes to eliminate the generalized effects. This algorithm has been tested on simulated and experimentally obtained signal traces suffering both from substantial general signal changes overwhelming the smaller focal activation induced signal changes.

Comparing BOLD fMRI signal changes in the awake and anesthetized rat during electrical forepaw stimulation

The difference between awake curarized and alpha-chloralose anesthetized animals was studied with respect to the BOLD signal response in an fMRI experiment. By studying the activation of the cortex upon electrical forepaw stimulation in the same rat, but following consecutively applied curarization and alpha-chloralose anesthesia protocols, it was possible to compare quantitatively the effect of both immobilization protocols on the fMRI data. The largest BOLD signal change as a result of forepaw stimulation was found in the awake condition, however the activated areas are less specific than those in the anesthetized state leaving it more difficult to interpret.

Electroencephalography during functional echo-planar imaging: detection of epileptic spikes using post-processing methods

EEG has been used to trigger functional MRI of patients with focal epilepsy, but EEG can be obscured by artifacts during MR data acquisition, and no continuous correlation of EEG and MRI has been possible without limiting the image time. Artifacts caused by an MRI sequence were investigated in five healthy subjects, and an EEG of five patients with epileptic discharges was recorded during echo-planar imaging. All interfering frequencies in the EEG were discrete and defined by loop structures in the MRI sequence. In post-processing of the EEG interfering frequencies were automatically detected by comparing the frequency spectra of the EEG recorded before and during imaging. After elimination of interfering frequencies by filters in the time domain or by Fourier transform, reliable spike detection in the EEG recorded during MR data acquisition became feasible, without loss of EEG quality.

Reduction of ECG and gradient related artifacts in simultaneously recorded human EEG/MRI data

Nowadays, electroencephalography signals can be acquired from a patient lying in a magnetic resonance imaging system. It is even possible to acquire EEG signals during an MR imaging sequence. However, such EEG signals are severely distorted by artifacts originating from various effects (e.g., MR gradients, ECG). In this paper, a simple method is presented to reduce such artifacts. Thereby, special attention is focused on artifacts related to the patient's electrocardiogram. The method is shown to be effective, adaptive, and automatic.

Special designed RF-antenna with integrated non-invasive carbon electrodes for simultaneous magnetic resonance imaging and electroencephalography acquisition at 7T

The construction of a high quality MR RF-antenna with incorporated EEG electrodes for simultaneous MRI and EEG acquisition is presented. The antenna comprises an active decoupled surface coil for receiving the MR signal and a whole body coil for transmitting the excitation RF pulses. The surface coil offers a high signal-to-noise ratio required for fMRI application and the whole body coil has a good B(1) excitation profile, which enables the application of homogeneous RF pulses. Non-invasive carbon electrodes are used in order to minimise the magnetic susceptibility artefacts that occur upon application of conductive materials. This dedicated set-up is compared to a standard set-up being a linear birdcage coil and commercially available Ag/AgCl electrodes. As the acquired EEG signals are heavily disturbed by the gradient switching, intelligent filtering is applied to obtain a clean EEG signal.

A method for removing imaging artifact from continuous EEG recorded during functional MRI

Combined EEG/fMRI recording has been used to localize the generators of EEG events and to identify subject state in cognitive studies and is of increasing interest. However, the large EEG artifacts induced during fMRI have precluded simultaneous EEG and fMRI recording, restricting study design. Removing this artifact is difficult, as it normally exceeds EEG significantly and contains components in the EEG frequency range. We have developed a recording system and an artifact reduction method that reduce this artifact effectively. The recording system has large dynamic range to capture both low-amplitude EEG and large imaging artifact without distortion (resolution 2 microV, range 33.3 mV), 5-kHz sampling, and low-pass filtering prior to the main gain stage. Imaging artifact is reduced by subtracting an averaged artifact waveform, followed by adaptive noise cancellation to reduce any residual artifact. This method was validated in recordings from five subjects using periodic and continuous fMRI sequences. Spectral analysis revealed differences of only 10 to 18% between EEG recorded in the scanner without fMRI and the corrected EEG. Ninety-nine percent of spike waves (median 74 microV) added to the recordings were identified in the corrected EEG compared to 12% in the uncorrected EEG. The median noise after artifact reduction was 8 microV. All these measures indicate that most of the artifact was removed, with minimal EEG distortion. Using this recording system and artifact reduction method, we have demonstrated that simultaneous EEG/fMRI studies are for the first time possible, extending the scope of EEG/fMRI studies considerably.