Removal of FMRI environment artifacts from EEG data using optimal basis sets

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.

Sound level dependence of auditory evoked potentials: Simultaneous EEG recording and low-noise fMRI

The simultaneous recording of EEG and fMRI offers the advantage of combining precise spatial information about neuronal processing obtained by fMRI data with the high temporal resolution of EEG data. One problem for the analysis of auditory processing, however, is the noisy environment during fMRI measurements, especially when EPI sequences are employed. While EEG studies outside an MRI scanner repeatedly demonstrated a clear sound level-dependent increase of N1 amplitude, this finding was less obvious in simultaneous recordings inside a scanner. Based on the assumption that this inconsistency might be due to the confounding effect of the rather loud EPI noise, we employed a low-noise fMRI protocol. This method was previously used to reveal level-dependent fMRI activation in auditory cortex areas. We combined this method with simultaneous EEG recordings to investigate the effect of different sound intensities on the auditory evoked potentials. Eight participants without hearing deficits took part in our experiment. Frequency modulated tones (FM) were presented monaurally with two sound intensities (60 and 80 dB HL). The task of the participants was to categorize the FM-direction (rising vs. falling). Our results inside the scanner replicate the sound level dependence of AEPs from previous EEG studies outside the scanner. The data analysis revealed a significant shortening of N1 latency and an increase in the N1-P2 peak-to-peak amplitude for the higher sound intensity. On a descriptive level, the 80 dB HL stimulation yielded more activated voxels in fMRI and stronger activations. This effect was pronounced over the right hemisphere. Our results suggest that low-noise sequences might be advantageous for the examination of auditory processing in simultaneous EEG and fMRI 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.

Spatial filters and automated spike detection based on brain topographies improve sensitivity of EEG–fMRI studies in focal epilepsy

The ballistocardiogram (BCG) represents one of the most prominent sources of artifacts that contaminate the electroencephalogram (EEG) during functional MRI. The BCG artifacts may affect the detection of interictal epileptiform discharges (IED) in patients with epilepsy, reducing the sensitivity of the combined EEG-fMRI method. In this study we improved the BCG artifact correction using a multiple source correction (MSC) approach. On the one hand, a source analysis of the IEDs was applied to the EEG data obtained outside the MRI scanner to prevent the distortion of EEG signals of interest during the correction of BCG artifacts. On the other hand, the topographies of the BCG artifacts were defined based on the EEG recorded inside the scanner. The topographies of the BCG artifacts were then added to the surrogate model of IED sources and a combined source model was applied to the data obtained inside the scanner. The artifact signal was then subtracted without considerable distortion of the IED topography. The MSC approach was compared with the traditional averaged artifact subtraction (AAS) method. Both methods reduced the spectral power of BCG-related harmonics and enabled better detection of IEDs. Compared with the conventional AAS method, the MSC approach increased the sensitivity of IED detection because the IED signal was less attenuated when subtracting the BCG artifacts. The proposed MSC method is particularly useful in situations in which the BCG artifact is spatially correlated and time-locked with the EEG signal produced by the focal brain activity of interest.

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.

Heart beats brain: The problem of detecting alpha waves by neuronal current imaging in joint EEG–MRI experiments

It has been suggested recently that the influence of the neuro-magnetic field should make electrical brain activity directly detectable by MRI. To test this hypothesis, we performed combined EEG-MRI experiments which aim to localize the neuronal current sources of alpha waves (8-12 Hz), one of the most prominent EEG phenomena in humans. A detailed analysis of cross-spectral coherence between simultaneously recorded EEG and MRI time series revealed no sign of alpha waves. Instead the EEG-MRI approach was found to be hampered by artefacts due to cardiac pulsation, which extend into the frequency band of alpha waves. Separate brain displacement mapping experiments confirmed that not only the EEG but also the MRI signal is confounded by harmonics of the cardiac frequency even at 10 Hz and beyond. This well-known ballistocardiogram artefact cannot be avoided or eliminated entirely by available signal processing techniques. Therefore we must conclude that current EEG-MRI methodology based on correlation analysis lacks not only the sensitivity but also the specificity required for the reliable detection of alpha waves.

Properties of the ballistocardiogram artefact as revealed by EEG recordings at 1.5, 3 and 7 T static magnetic field strength

Electroencephalogram (EEG) data recorded simultaneously with functional magnetic resonance imaging (fMRI) suffer from severe artefacts. The ballistocardiogram (BCG) artefact in particular is as yet poorly understood and different BCG removal strategies have been proposed. In the present study, EEG data were recorded from four participants in three different MRI scanners with field strengths of 1.5, 3 and 7 T, with the aim of investigating the impact of the static magnetic field strength on the BCG artefact and independent component analysis (ICA). The results confirm that the amplitude of the BCG artefact is a function of the static magnetic field strength. Moreover, the spatial variability of the BCG artefact substantially increased at higher magnetic field strengths. A comparison of ICA before and after channel-wise BCG correction revealed that typical independent components could be more easily identified when ICA was applied after channel-wise BCG correction. Further analysis of EEG and electrocardiogram recordings points towards the contribution of at least two different processes to the origin of the BCG, which are blood movement or axial head rotation on the one hand and electrode movement at lateral sites of the head on the other. This is summarized in a preliminary BCG model that may help to explain recent inconsistencies regarding the usefulness of ICA for BCG removal. It may also guide the future development of more advanced BCG removal procedures.

Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation

An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings

Recording the electroencephalogram (EEG) during functional magnetic resonance imaging (fMRI) permits the identification of haemodynamic changes associated with EEG events. However, subject motion within the MR scanner can cause unpredictable and frustrating artefacts on the EEG that may appear focally, bilaterally or unilaterally and can sometimes be confused for epileptiform activity. Motion may arise from a number of sources: small involuntary cardiac-related body movements (ballistocardiogram); acoustic vibrations due to the scanner machinery; and voluntary subject movements. Here we describe a new real-time technique for removing ballistocardiogram (BCG) and movement artefact from EEG recordings in the MR scanner using a novel method for recording subject motion. We record the current induced in a number of wire loops, attached to a cap worn by the subject, due to motion in the static magnetic field of the scanner (Faraday's Law). This is the same process that leads to the motion artefacts on the EEG, and hence these signals are ideally suited to filtering these artefacts from the EEG. Our filter uses a linear adaptive technique based upon the Recursive Least Squares (RLS) algorithm. We demonstrate in both simulations and real EEG recordings from epilepsy patients that our filter significantly reduces the artefact power whilst preserving the underlying EEG signal.

BOLD functional MRI in disease and pharmacological studies: room for improvement?

In the past decade the use of blood oxygen level-dependent (BOLD) fMRI to investigate the effect of diseases and pharmacological agents on brain activity has increased greatly. BOLD fMRI does not measure neural activity directly, but relies on a cascade of physiological events linking neural activity to the generation of MRI signal. However, most of the disease and pharmacological studies performed so far have interpreted changes in BOLD fMRI as "brain activation," ignoring the potential confounds that can arise through drug- or disease-induced modulation of events downstream of the neural activity. This issue is especially serious in diseases (like multiple sclerosis, brain tumours and stroke) and drugs (like anaesthetics or those with a vascular action) that are known to influence these physiological events. Here we provide evidence that, to extract meaningful information on brain activity in patient and pharmacological BOLD fMRI studies, it is important to identify, characterise and possibly correct these influences that potentially confound the results. We suggest a series of experimental measures to improve the interpretability of BOLD fMRI studies. We have ranked these according to their potential information and current practical feasibility. First-line, necessary improvements consist of (1) the inclusion of one or more control tasks, and (2) the recording of physiological parameters during scanning and subsequent correction of possible between-group differences. Second-line, highly recommended important aim to make the results of a patient or drug BOLD study more interpretable and include the assessment of (1) baseline brain perfusion, (2) vascular reactivity, (3) the inclusion of stimulus-related perfusion fMRI and (4) the recording of electrophysiological responses to the stimulus of interest. Finally, third-line, desirable improvements consist of the inclusion of (1) simultaneous EEG-fMRI, (2) cerebral blood volume and (3) rate of metabolic oxygen consumption measurements and, when relevant, (4) animal studies investigating signalling between neural cells and blood vessels.

Moving GLM ballistocardiogram artifact reduction for EEG acquired simultaneously with fMRI

OBJECTIVE

Simultaneous acquisition of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) enables studies of brain activity at both high temporal and high spatial resolution. However, EEG acquired in a magnetic field is contaminated by ballistocardiogram (BKG) artifact. The most commonly used method of BKG artifact reduction, averaged artifact subtraction (AAS), was not designed to account for overlapping BKG waveforms generated by adjacent beats. We describe a new method based on a moving general linear model (mGLM) that accounts for overlapping BKG waveforms.

METHODS

Simultaneous EEG-fMRI at 3 Tesla was performed in nine normal human subjects (8-11 runs/subject, 5.52 min/run). Gradient switching artifact was effectively reduced using commercially supplied procedures. Cardiac beats were detected using a novel correlation detector algorithm applied to the EKG trace. BKG artifact was reduced using both mGLM and AAS.

RESULTS

mGLM recovered BKG waveforms outlasting the median inter-beat interval. mGLM more effectively than AAS removed variance in the EEG attributable to BKG artifact.

CONCLUSIONS

mGLM offers advantages over AAS especially in the presence of variable heart rate.

SIGNIFICANCE

The BKG artifact reduction procedure described herein improves the technique of simultaneous EEG-fMRI. Potential applications include basic investigations of the relationship between scalp potentials and functional imaging signals as well as clinical localization of epileptic foci.

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.

The hemodynamic response of the alpha rhythm: an EEG/fMRI study

EEG was recorded during fMRI scanning of 16 normal controls in resting condition with eyes closed. Time variations of the occipital alpha band amplitudes were correlated to the fMRI signal variations to obtain insight into the hemodynamic correlates of the EEG alpha activity. Contrary to earlier studies, no a priori assumptions were made on the expected shape of the alpha band response function (ARF). The ARF of different brain regions and subjects were explored and compared. It was found that: (1) the ARF of the thalamus is mainly positive. (2) The ARFs at the occipital and left and right parietal points are similar in amplitude and timing. (3) The peak time of the thalamus is a few seconds earlier than that of occipital and parietal cortex. (4) No systematic BOLD activity was found preceding the alpha band activity, although in the two subjects with the strongest alpha band power such correlation was present. (5) There is a strong and immediate positive correlation at the eyeball, and a strong negative correlation at the back of the eye. Furthermore, it was found that in one subject the cortical ARF was positive, contrary to the other subjects. Finally, a cluster analysis of the observed ARF, in combination with a Modulated Sine Model (MSM) fit to the estimated ARF, revealed that within the cortex the ARF peak time shows a spatial pattern that may be interpreted as a traveling wave. The spatial pattern of alpha band response function represents the combined effect of local differences in electrical alpha band activity and local differences in the hemodynamic response function (HRF) onto these electrical activities. To disentangle the contributions of both factors, more advanced integration of EEG inverse modeling and hemodynamic response modeling is required in future studies.

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.

The brain and behavior: limitations in the legal use of functional magnetic resonance imaging

Brain imaging is one of the most remarkable technological advances towards understanding the relationship of behavior to brain anatomy and physiology. Brain images provide insight to understanding behavior. Additionally, the images themselves carry great impact, particularly when used to show differences in either the anatomy or the biological functioning of two different brains. For these reasons, brain images have increasingly been used in both criminal and civil trials.

After describing some general features of brain imaging, we will focus on functional magnetic imaging (fMRI), as many believe this technology has the most potential for advancing our understanding of how parts of the brain function, including perhaps linking specific functions with cognition and behavior. Brain imaging as a field is vast and therefore our discussion will be limited. First, we will assess the advantages and limitations of fMRI, including research efforts towards standardizing equipment thereby assuring reliability and reproducibility.

Improved quality of auditory event-related potentials recorded simultaneously with 3-T fMRI: Removal of the ballistocardiogram artefact

EEG signals recorded simultaneously with fMRI are massively compromised by severe artefacts, among them the cardiac cycle-related ballistocardiogram (BCG) artefact. Different methods have been proposed to remove the BCG artefact focusing on channel-wise template subtraction procedures or spatial filtering approaches such as independent component analysis (ICA). Here we systematically compared the performance of the optimal basis set (OBS), a channel-wise correction approach, with ICA and a recently proposed combination of both (OBS-ICA). The three different procedures were applied to 60-channel EEG data from 12 subjects recorded during fMRI acquisition in a 3-T scanner. In addition to examination of the residual BCG artefact, the signal-to-noise ratio (SNR) and the topography of the resulting auditory evoked potential component N1 were compared. Whereas all three approaches led to a significant artefact reduction, the ICA procedure resulted in a significantly reduced N1 SNR and amplitude when compared to BCG-uncorrected data, indicating a rather poor performance. In contrast to ICA, OBS and OBS-ICA corrected data substantially improved the SNR of the N1. The quality of the auditory evoked potential N1 topography was investigated by means of equivalent current dipole modelling. On a descriptive level, all three correction procedures led to a reduced localization error when compared to BCG-uncorrected data. This improvement was significant for OBS-ICA. We conclude that OBS and OBS-ICA can efficiently remove BCG artefacts and substantially improve the quality of EEG signals recorded inside the scanner, a prerequisite for the successful integration of simultaneously recorded EEG and fMRI.

Single-trial EEG–fMRI reveals the dynamics of cognitive function

Two major non-invasive techniques in cognitive neuroscience, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. Recent hardware and software developments have made it feasible to acquire EEG and fMRI data simultaneously. We emphasize the potential of simultaneous EEG and fMRI recordings to pursue new strategies in cognitive neuroimaging. Specifically, we propose that, by exploiting the combined spatiotemporal resolution of the methods, the integration of EEG and fMRI recordings on a single-trial level enables the rich temporal dynamics of information processing to be characterized within spatially well-defined neural networks.

Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis

The simultaneous recording of EEG and fMRI is a promising method for combining the electrophysiological and hemodynamic information on cerebral dynamics. However, EEG recordings performed in the MRI scanner are contaminated by imaging, ballistocardiographic (BCG) and ocular artifacts. A number of processing techniques for the cancellation of fMRI environment disturbances exist: the most popular is averaged artifact subtraction (AAS), which performs well for the imaging artifact, but has some limitations in removing the BCG artifact, due to the variability in cardiac wave duration and shape; furthermore, no processing method to attenuate ocular artifact is currently used in EEG/fMRI, and contaminated epochs are simply rejected before signal analysis. In this work, we present a comprehensive method based on independent component analysis (ICA) for simultaneously removing BCG and ocular artifacts from the EEG recordings, as well as residual MRI contamination left by AAS. The ICA method has been tested on event-related potentials (ERPs) obtained from a visual oddball paradigm: it is very effective in attenuating artifacts in order to reconstruct clear brain signals from EEG acquired in the MRI scanner. It performs significantly better than the AAS method in removing the BCG artifact. Furthermore, since ocular artifacts can be completely suppressed, a larger number of trials is available for analysis. A comparison of ERPs inside the magnetic environment with those obtained out of the MRI scanner confirms that no systematic bias in the ERP waveform is produced by the ICA method.

Applications of fMRI in translational medicine and clinical practice

Functional MRI (fMRI) has had a major impact in cognitive neuroscience. fMRI now has a small but growing role in clinical neuroimaging, with initial applications to neurosurgical planning. Current clinical research has emphasized novel concepts for clinicians, such as the role of plasticity in recovery and the maintenance of brain functions in a broad range of diseases. There is a wider potential for clinical fMRI in applications ranging from presymptomatic diagnosis, through drug development and individualization of therapies, to understanding functional brain disorders. Realization of this potential will require changes in the way clinical neuroimaging services are planned and delivered.

Ballistocardiogram artifact removal from EEG signals using adaptive filtering of EOG signals

We estimated ballistocardiogram (BCG) components in EEG signals recorded inside an MRI magnet using the electro-oculogram (EOG) signals recorded simultaneously with the EEG signals. Since the EOG signals are measured near the EEG measuring points, it is thought that the BCG components in the EOG signals resemble the BCG components in the EEG signals. To estimate the BCG components in the EEG signals, we applied the Kalman filter to the EOG and EEG signals recorded inside a 3.0 T MRI magnet. After removing the estimated BCG components from the EEG signals, we extracted the visual-evoked potentials (VEPs) from the BCG-removed EEG signals. To validate the efficacy of Kalman filtering in the BCG artifact removal, we have compared three types of VEPs of eight healthy subjects: one extracted from the raw EEG signals measured outside the magnet and the others extracted from the BCG-removed EEG signals measured inside the magnet. The BCG artifacts have been removed with Kalman filtering as well as with the conventional BCG template subtraction method for the sake of comparison. No significant difference in waveforms, latencies and amplitudes has been found between the two types of VEPs extracted from the two kinds of BCG-removed EEG signals.

Synchronization facilitates removal of MRI artefacts from concurrent EEG recordings and increases usable bandwidth

Investigating human brain function non-invasively by simultaneous EEG and fMRI measurements is gaining in popularity as more and better solutions to the inherent technical challenges emerge. We demonstrate the use of a commercially available frequency divider and phase-locking device for the purpose of synchronizing an MRI acquisition with a simultaneous recording of the EEG. Synchronization hugely improves the effectiveness of MRI artefact removal from the EEG signal by the common mean template subtraction method. It complements or substitutes post-processing techniques like filtering, thereby increasing the usable bandwidth of the EEG signal to about 150 Hz. This is important for covering the full range of human Gamma band activity. Similarly, synchronization eliminates the necessity for over-sampling of the EEG signal.

Automated single-trial measurement of amplitude and latency of laser-evoked potentials (LEPs) using multiple linear regression

OBJECTIVE

Laser stimulation of Adelta-fibre nociceptors in the skin evokes nociceptive-specific brain responses (laser-evoked potentials, LEPs). The largest vertex complex (N2-P2) is widely used to assess nociceptive pathways in physiological and clinical studies. The aim of this study was to develop an automated method to measure amplitudes and latencies of the N2 and P2 peaks on a single-trial basis.

METHODS

LEPs were recorded after Nd:YAP laser stimulation of the left hand dorsum in 7 normal volunteers. For each subject, a basis set of 4 regressors (the N2 and P2 waveforms and their respective temporal derivatives) was derived from the time-averaged data and regressed against every single-trial LEP response. This provided a separate quantitative estimate of amplitude and latency for the N2 and P2 components of each trial.

RESULTS

All estimates of LEP parameters correlated significantly with the corresponding measurements performed by a human expert (N2 amplitude: R2=0.70; P2 amplitude: R2=0.70; N2 latency: R2=0.81; P2 latency: R2=0.59. All P<0.0001). Furthermore, regression analysis was able to extract an LEP response from a subset of the trials that had been classified by the human expert as without response.

CONCLUSIONS

This method provides a simple, fast and unbiased measurement of different components of single-trial LEP responses.

SIGNIFICANCE

This method is particularly desirable in several experimental conditions (e.g. drug studies, correlations with experimental variables, simultaneous EEG/fMRI and low signal-to-noise ratio data) and in clinical practice. The described multiple linear regression approach can be easily implemented for measuring evoked potentials in other sensory modalities.

Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring

Goal-directed behavior requires the continuous monitoring and dynamic adjustment of ongoing actions. Here, we report a direct coupling between the event-related electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and behavioral measures of performance monitoring in humans. By applying independent component analysis to EEG signals recorded simultaneously with fMRI, we found the single-trial error-related negativity of the EEG to be systematically related to behavior in the subsequent trial, thereby reflecting immediate behavioral adjustments of a cognitive performance monitoring system. Moreover, this trial-by-trial EEG measure of performance monitoring predicted the fMRI activity in the rostral cingulate zone, a brain region thought to play a key role in processing of response errors. We conclude that investigations of the dynamic coupling between EEG and fMRI provide a powerful approach for the study of higher order brain functions.

Simultaneous EEG and fMRI in the macaque monkey at 4.7 Tesla

Simultaneous electroencephalography (EEG)/functional magnetic resonance imaging (fMRI) acquisition can identify the brain networks involved in generating specific EEG patterns. Yet, the combination of these methodologies is hampered by strong artifacts that arise due to electromagnetic interference during magnetic resonance (MR) image acquisition. Here, we report corrections of the gradient-induced artifact in phantom measurements and in experiments with an awake behaving macaque monkey during fMRI acquisition at a magnetic field strength of 4.7 T. Ninety-one percent of the amplitude of a 10 microV, 10 Hz phantom signal could successfully be recovered without phase distortions. Using this method, we were able to extract the monkey EEG from scalp recordings obtained during MR image acquisition. Visual evoked potentials could also be reliably identified. In conclusion, simultaneous EEG/fMRI acquisition is feasible in the macaque monkey preparation at 4.7 T and holds promise for investigating the neural processes that give rise to particular EEG patterns.

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.

Simultaneous recording of laser-evoked brain potentials and continuous, high-field functional magnetic resonance imaging in humans

Simultaneous recording of event-related electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) responses has the potential to provide information on how the human brain reacts to an external stimulus with unique spatial and temporal resolution. However, in most studies combining the two techniques, the acquisition of functional MR images has been interleaved with the recording of evoked potentials. In this study we investigated the feasibility of recording pain-related evoked potentials during continuous and simultaneous collection of blood oxygen level-dependent (BOLD) functional MR images at 3 T. Brain potentials were elicited by selective stimulation of cutaneous Adelta and C nociceptors using brief radiant laser pulses (laser-evoked potentials, LEPs). MR-induced artifacts on EEG data were removed using a novel algorithm. Latencies, amplitudes, and scalp distribution of LEPs recorded during fMRI were not significantly different from those recorded in a control session outside of the MR scanner using the same equipment and experimental design. Stability tests confirmed that MR-image quality was not impaired by the evoked potential recording, beyond signal loss related to magnetic susceptibility differences local to the electrodes. fMRI results were consistent with our previous studies of brain activity in response to nociceptive stimulation. These results demonstrate the feasibility of recording reliable pain-related LEPs and fMRI responses simultaneously. Because LEPs collected during fMRI and those collected in a control session show remarkable similarity, for many experimental designs the integration of LEP and fMRI data collected in separate, single-modality acquisitions may be appropriate. Truly simultaneous recording of LEPs and fMRI is still desirable in specific experimental conditions, such as single-trial, learning, and pharmacological studies.