Simultaneous recording of EEG and BOLD responses: A historical perspective

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International Journal of Psychophysiology 67 (2008) 161 – 168 www.elsevier.com/locate/ijpsycho

Simultaneous recording of EEG and BOLD responses: A historical perspective Christoph S. Herrmann a,⁎, Stefan Debener b a b

Department of Biological Psychology, Otto-von-Guericke-University of Magdeburg, P.O. Box 4120, 39016 Magdeburg, Germany Medical Research Council, Institute of Hearing Research, Royal South Hants, Hospital, Southampton, SO14 OYG, United Kingdom Received 22 May 2007; accepted 20 June 2007 Available online 10 July 2007

Abstract 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. © 2007 Published by Elsevier B.V. Keywords: Electroencephalogram; Functional magnetic resonance imaging; Blood oxygen level dependent

1. Introduction Measurable correlates of cognitive events in the human brain include electromagnetic fields that can be recorded with electroencephalography (EEG) and magnetoencephalography (MEG), as well as hemodynamic responses measured by functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). EEG/MEG reflects synchronized electrical activity of neurons, and thus feature the same timescale as the underlying neuro-cognitive process, while fMRI is linked to the energy consumption of the neuronal populations and records a signal on a timescale of several seconds. However, a wealth of studies has shown that cognitive processes modulate hemodynamic responses as measured with the fMRI blood oxygen level dependent (BOLD) contrast (e.g., Raichle, 2001). Unlike fMRI, MEG and EEG are not brain imaging methods. The inverse problem i.e. inferring sources

⁎ Corresponding author. Tel.: +49 391 6718477. E-mail address: [email protected] (C.S. Herrmann). 0167-8760/$ - see front matter © 2007 Published by Elsevier B.V. doi:10.1016/j.ijpsycho.2007.06.006

inside the brain from signals recorded outside the brain, exists in both EEG and MEG. Accordingly, a combination of MEG and/or EEG with fMRI has been proposed to achieve both high temporal and spatial resolution of brain function. Combining EEG and fMRI promises to integrate the good temporal resolution of EEG with the good spatial resolution of fMRI (for recent reviews, see Debener et al., 2006; Hopfinger et al., 2005; Menon and Crottaz-Herbette, 2005). However, it is likely that some EEG/MEG correlates of cognitive processing may not result in measurable changes of the BOLD signal, whereas other patterns of neuro-cognitive activity may be detectable with fMRI but not EEG (Liu et al., 1998; Nunez and Silberstein, 2000; Schulz et al., 2004; Im et al., 2005). Accordingly, a main goal of simultaneous EEG– fMRI is to shed light on the foundations of the two measures and their interrelations. Along theses lines, attempts have been made to determine how neuronal activity is coupled to the hemodynamic response (Logothetis et al., 2001; Lauritzen and Gold, 2003; Logothetis, 2003). Indeed, one of the main virtues of the combined EEG–fMRI approach may soon turn out to be a better understanding of which aspects of each signal are coupled, and which are not. This view is illustrated

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in Fig. 1, showing that a direct coupling between EEG and fMRI signals may account for only a fraction of the variance of each signal. 2. The development of EEG–fMRI integration Several approaches have been published aiming to integrate EEG and fMRI information. These can be grouped into at least three classes which at the same time may be regarded as representing the temporal evolution of this endeavour: 1. Separate EEG–fMRI recordings and analyses, and subsequent combination of results 2. Simultaneous EEG–fMRI recordings and analysis of highamplitude EEG signals (e.g. alpha oscillations or epileptic spikes) 3. Simultaneous EEG–fMRI recordings and analysis of eventrelated potentials and single trials. 2.1. Separate EEG–fMRI recordings and analyses, and subsequent combination of results EEG and fMRI setups are both complicated technical environments which need sophisticated hard- and software as well as skilled personnel for operation. Thus, even slight disturbances such as a nearby electromechanical device in EEG or metal parts in the MRI room may result in severe artifacts and corrupted signal quality. Therefore, the recording of EEG signals inside the MRI certainly compromises the EEG signal quality (e.g., Warbrick and Bagshaw, 2008-this issue) and can also affect the quality of MR images (e.g., Mullinger et al., 2008-this issue). In order to avoid these obstacles, a number of early studies have recorded EEG and BOLD responses from the

Fig. 1. A direct coupling between EEG and fMRI, if present, is thought to represent only a fraction of the variance of each measure. Some fraction of the correlation between EEG and FMRI may refer to event-related coupling (shaded area), while other correlations of ongoing EEG and BOLD activity may not be affected by experimental manipulation. Adapted from Debener et al. (2006).

same subjects in two separate EEG and fMRI recording sessions. Among the first approaches to combine electrophysiology with hemodynamic responses were separate recordings of eventrelated potentials (ERPs) and positron emission tomography (PET) for the investigation of the location and timing of the earliest effects of visual attention (Heinze et al., 1994). In this seminal work, the difference between ERPs from attended and unattended conditions revealed the temporal evolution of visual attention at a millisecond time scale. The spatial localization of the attention effect was inferred from a PET subtraction image of the same two experimental conditions. By showing that a dipole localization of the ERP component revealed the same brain area as the PET imaging data, the authors suggested that the combination of both measures yielded joint temporal and spatial information about the process of interest, that is, visual attention. A number of studies have subsequently employed similar approaches using fMRI instead of PET (e.g. Menon et al., 1997; Linden et al., 1999; Martinez et al., 1999; Opitz et al., 1999; Bledowski et al., 2004, 2006; Crottaz-Herbette and Menon, 2006; Wibral et al., 2008-this issue). The method of fMRIconstrained EEG source modelling was derived from such correlations of EEG and fMRI sources. Another approach combines separately recorded EEG and fMRI data during a parametric task manipulation (e.g., Horovitz et al., 2004; Schicke et al., 2006). Such parametric studies are very useful, as they require less a-priori assumptions than do colocalizations. To date, using separate recording protocols is the only way to integrate MEG recordings with fMRI BOLD signals, since the recording of electromagnetic signals in the femtotesla (fT) range requires superconducting quantum interference devices (SQUIDs) which are incompatible with the MRI environment. Accordingly, unlike EEG, MEG data cannot be recorded inside an MRI. However, MEG may provide superior localization quality compared to EEG data, mostly due to the fact that magnetic fields are neither attenuated by the human skull nor smeared by volume conduction, which has led to the combining of MEG and fMRI (e.g. George et al., 1995; Liu et al., 1998; Ahlfors et al., 1999; Woldorff et al., 1999; Dale et al., 2000; Fujimaki et al., 2002; Moradi et al., 2003; Kircher et al., 2004; Del Gratta et al., 2002). For a review of the MEG–fMRI combination approach see Dale and Halgren (2001). It is noteworthy that this approach has meanwhile gained clinical importance. Grummich et al. (2006) for instance were able to demonstrate that the combination of MEG and MRI enhances the reliability of source localization during pre-surgical planning. The combination of EEG, MEG and fMRI may result in even further advancement (Babiloni et al., 2004). While most studies combining EEG or MEG with fMRI implicitly assume that both measures pick up more or less similar neural activity, some authors have questioned this fundamental assumption. In a number of studies it has been shown that not all activity visible in MEG also results in BOLD responses, and vice versa (e.g. Liu et al., 1998; Schulz et al., 2004; Im et al., 2005). This has resulted in the notion of so-called fMRI-blind EEG/MEG sources and EEG/MEG-blind fMRI sources (Ritter and Villringer,

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2006), and interesting hypotheses have been put forward regarding which activities might be specific to EEG and not visible to fMRI (Fell et al., 2004). One of the criticisms of separate recording protocols results from the fact that it seems impossible to control whether a subject performs in the same manner in both experiments (Debener et al., 2006). For separate recordings is seems necessary to test for order of session effects. In numerous psychophysiological studies these tests have revealed significant differences depending on whether subjects performed an experiment for the first time or repeated a known experimental paradigm in a second session (e.g., Debener et al., 2002). This is easily conceivable for paradigms explicitly investigating learning and memory processes. One should note that the most basic perceptual and cognitive operations may also show adaptation over time, such that temporal aspects of sessions or order of trials should be taken into account. A further criticism is that even minor changes of an experimental setup can result in significant changes of subjects' behavioural and physiological responses. For example, changing a subject from a seated upright position, as common in EEG recordings, into a supine position, as necessary for most fMRI scanners, may influence physiological responses and overt behaviour. Another important aspect is the additional noise of MR scanning, which makes recordings inside and outside the scanner much less comparable. EEG data are usually recorded in a sound-attenuated chamber in order to control for environmental noise. It has been demonstrated that the noise of an MR scanner's echo planar imaging (EPI) sequences results in significant changes of subjects' auditory MEG responses during an auditory experiment (Herrmann et al., 2000), and similar findings have been obtained for ERPs (Novitski et al., 2001, 2003). Along these lines, a direct comparison of ERPs recorded inside and outside the MR scanner typically reveals some differences. While some of these differences may be largely attributed to residual EEG artifacts compromising the quality of EEG recordings inside a scanner, it is also possible that they emerge at least in part due to differences in the recording environments that have an effect on cognitive brain states. Nevertheless, effects of cognitive parameters can usually be replicated inside the scanner. The enhanced P3 amplitude to rare target tones (Mulert et al., 2004) as well as to rare nogo-trials (Bregadze and Lavric, 2006) for instance is visible inside and outside the scanner. Sammer et al. (2005) also investigated the influence of a 1.5 T magnetic field on steady-state evoked potentials, lateralized readiness potentials and cognitive theta oscillations. While they reported significant differences between the EEG recordings inside and outside the scanner for all three measures, the pattern of results was similar inside and outside, i.e. the same experimental condition that yielded maximal amplitudes outside the scanner also did so inside. 2.2. Simultaneous EEG–fMRI recordings and analysis of highamplitude EEG signals In order to avoid the above-mentioned disadvantages of separate recording protocols, it is desirable to record EEG data

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simultaneously with the BOLD response inside the MR scanner. The first approaches in this direction addressed EEG phenomena with a relatively high amplitude visible to the naked eye, such as alpha oscillations, epileptic spikes, or steady-state visual evoked potentials (SSVEPs) at low MR field strengths (e.g. 1.5 T). Ives et al. (1993) were among the first to report ongoing alpha oscillations from inside scanner EEG recordings. Such oscillations can have amplitudes of approximately 50 to 100 μV and were simultaneously recorded at 1.5 T magnetic field strength more than ten years ago (Huang-Hellinger et al., 1995). Subsequently, a number of groups reported temporal correlations between the EEG alpha amplitude and the BOLD signal and, with few exceptions, consistently found negative correlations with the BOLD signal in occipital cortex and positive correlations with thalamic activity (Feige et al., 2005; Goldman et al., 2002; Goncalves et al., 2006; Laufs et al., 2003, 2006; Moosmann et al., 2003). These studies clearly mark a substantial advancement in the field, as for the first time they reported a temporal correlation, and thus coupling, between non-invasively recorded EEG and BOLD signals. Investigating the ongoing background EEG of epileptic patients for paroxysmal activity (spikes, sharp waves, spikewave complexes, etc.) is an important clinical application of EEG. Epileptiform activity is routinely identified visually for diagnostic purposes, and amplitudes of paroxysmal activity often exceed those of ongoing alpha oscillations. A number of studies have used epileptic spikes as a trigger for subsequent fMRI recordings, simply employing the relative timing of epileptic spikes to reveal hemodynamic activation. This procedure has been termed EEG-triggered fMRI analysis (Warach et al., 1996) and has revealed insights into the metabolic effects of epileptic activity. Accordingly, several groups have used simultaneous recordings and similar approaches to localize foci of epileptic discharges in the human brain (e.g. Krakow et al., 1999, 2001; Patel et al., 1999; Lazeyras et al., 2000; Lemieux et al., 2001; Benar et al., 2002; Salek-Haddadi et al., 2002; for review, see Salek-Haddadi et al., 2003; Gotman et al., 2004a,b). 2.3. Simultaneous EEG–fMRI recordings and analysis of eventrelated potentials and single trials While high-amplitude EEG phenomena were simultaneously recorded with BOLD responses as early as 1993 (Ives et al., 1993), event-related potentials (ERPs) were only much later revealed in simultaneous recordings, possibly due to their low amplitude. Over the past few years, significant improvements in hard- and software development have been achieved, making it now possible to uncover ERPs and single-trial event-related EEG amplitudes from recordings inside an MRI scanner (see below). The first study reporting a visual evoked potential in a 3 T MR scanner was carried out by Bonmassar et al. (1999). These authors recorded P1 and N1 ERPs and BOLD activity in response to a checkerboard stimulation which was inverted at a rate of 4 Hz. A similar study extended the simultaneous recording of P1 and N1 by showing that the sources of the ERP components fitted the brain regions identified by functional

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BOLD activity (Kruggel et al., 2000). In addition, Kruggel and co-workers demonstrated that the recorded ERPs inside the scanner were highly comparable during periods of MR image acquisition and non-acquisition. The above-mentioned studies recorded so-called exogenous ERP components which are mainly modulated by the parameters of external stimuli. Cognitive brain research, however, is also interested in modulations arising from endogenous, mental operations such as attention, memory, and language. So-called endogenous ERP modulations were first recorded inside an MR scanner in response to illusory figures revealing an amplitude modulation of the visual N170 by Gestalt perception in parallel to enhanced extrastriate BOLD activity (Kruggel et al., 2001). A very promising approach of integrating EEG and fMRI is to apply parametric variations of a stimulus and to correlate the influence on ERPs with those on the BOLD signal (Horovitz et al., 2002). Liebenthal et al. (2003) have done this in an auditory mismatch paradigm, where the amplitude variation of the mismatch negativity in response to small or large deviants was used to identify brain regions which displayed the same experimental manipulations as the BOLD signal. Moreover, the P3 represents a prominent ERP component reflecting mainly attentional resources during target processing for which simultaneous recording revealed an enhancement for targets concurrent with increased BOLD activity in the temporoparietal junction, frontal areas and the insulae (Mulert et al., 2004). Otzenberger et al. (2005) further extended these findings by showing that different P3 components such as the target-P3 and novelty-P3 could be discriminated in a simultaneous recording, a finding that has been recently replicated (Strobel et al., 2006). While most of the above studies analysed responses to visual stimuli, Sammer et al. (2005) and Debener et al. (2005) were able to record response-locked ERPs such as the lateralized readiness potential (LRP) and the error-related negativity, and Mulert et al. (2005) and Debener et al. (2007) reported reliable recording of the auditory N1 in spite of the noisy MR environment. Further improvements in auditory research include using sparse sampling MRI acquisition protocols (Hall et al., 1999) and/or silent fMRI recording protocols (Thaerig et al., 2008-this issue). Most of the studies mentioned above refer to the classical ERP, that is, the averaged phase-locked fraction of the eventrelated EEG signal. However, the validity of the underlying additive ERP model has been questioned, and event-related EEG signals that are not tightly phase-locked to a time-locking event are lost by time-domain averaging (e.g., Makeig et al., 2004). For this and other reasons, it is therefore desirable to retain event-related EEG information on a single-trial level. In the context of simultaneous EEG–fMRI, this has been successfully demonstrated by several authors (Debener et al., 2005; Eichele et al., 2005; Hinterberger et al., 2005; Nagai et al., 2004). In order to improve the poor signal-to-noise ratio, Eichele et al. and Debener et al. have applied independent component analysis (ICA). ICA unmixes multi-channel EEG recordings into a number of maximally independent components, thereby improving the signal-to-noise ratio and making it possible to directly relate event-related EEG to event-related

fMRI (see Debener et al., 2006, for review). It has been proposed that the non-phase-locked portion of the EEG signal, that is, trial-by-trial amplitude and latency fluctuations, further contributes to the correlation between event-related EEG and the BOLD signal (Debener et al., 2006). Further consideration of the event-related brain dynamics model (Makeig et al., 2004) and the development of optimised ICA decomposition tools may provide further advances (Moosmann et al., 2008-this issue; Eichele et al., 2008-in press). 3. Hardware and software developments The development from separate to simultaneous recordings was paralleled by a hardware and software development which made progress in this field possible. In the early days of simultaneous EEG–fMRI recordings, standard EEG amplifiers were used and placed outside the MRI room. Thus, long wires connecting the electrode cap or pre-amplifier through the MR room to the main amplifier were required (e.g., HuangHellinger et al., 1995). Since the movement of a conductor in a magnetic field induces an electric current, this potentially leads to technical artifacts when those wires are moved or even when they vibrate due to the activity of the Helium pump. More recent amplifier systems such as the system shown in Fig. 2 use short leads running from the electrode cap towards the amplifier which sits inside or nearby the magnet (e.g. pre-amplifier inside MR magnet in Ives et al., 1993). Analog amplitude values are then digitized and transferred via a fiber-optic cable which is not susceptible to magnetic fields. Of course, these amplifiers have to be free of ferro-magnetic materials, since they are placed inside a strong magnetic field. It is important that the artifact caused by the magnetic transients during scanning and picked up by the EEG, the socalled gradient artifact, does not drive the EEG amplifier into saturation. This gradient switching causes a varying magnetic field strength and induces electric currents even in stationary

Fig. 2. A modern EEG amplifier system, consisting of the amplifier unit and a power supply unit, can be placed inside the MRI scanner room. This enables usage of short wires connecting the amplifier with the recording electrodes. Without compromising signal quality, the amplified and digitized EEG data are transmitted via fiber-optic cables to a recording computer placed outside the scanner room.

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conductors (e.g., electrode leads). With standard EEG amplifiers, the induced currents result in large amplitudes and can cause amplifier saturation. More recent EEG systems designed for recordings inside an MRI scanner include specifically designed hardware low-pass filters. In combination with a large dynamic range, these systems can cope with the gradient artifact and thereby allow its subsequent removal by means of statistical procedures. A current focus of research is the development of EEG electrodes and cables that are optimised for recordings inside a scanner (Vasios et al., 2006), which may result in an optimised data quality for both fMRI and EEG signals. As an alternative, normal EEG electrodes, fitted with in-line current limiting resistors, have been be successfully used, but may affect the MRI image quality (see Mullinger et al., 2008-this issue). 3.1. Reduction of EEG artifacts specific to simultaneous acquisition protocols A great deal of work has been devoted to the development of artifact removal techniques. The progress in this field has been of major importance to the successful integration of EEG and fMRI and will be briefly summarized below. EEG recordings obtained inside the MRI environment suffer from at least two artifacts. The gradient artifact (GA), as mentioned above, is caused by the MRI gradient switching and radio frequency pulses. Therefore, this artifact is limited to the time required to acquire these images. For many purposes, the GA can be avoided by recording EEG and fMRI data interleaved with each other (e.g., Eichele et al., 2005). However, for clinical purposes and for the implementation of more flexible experimental designs it is clearly desirable to recover the EEG under periods distorted by the GA (e.g., Gotman et al., 2004a,b). Since the seminal publication of Allen et al. (1998), this goal has been within reach. In fact, most recent EEG/fMRI publications are based on simultaneous recording protocols. 3.2. The gradient artifact The GA is a technical, or exogenous, artifact. Under (the rather unrealistic) condition where head movement is absent, the GA amplitude and morphology is essentially invariant over time. This characteristic has led to the implementation of the average artifact template (AAT) procedure (Allen et al., 2000). Here, the average across several artifact trials is used to form a GA template which in turn is subtracted out from every single GA occurrence. For the AAT procedure to be successful, several assumptions need to be taken into consideration. Firstly, as already outlined, it is essential that the EEG amplifier captures the large amplitudes introduced by the GA. In effect, this ensures that the sum of the GA and the underlying EEG signal is being recorded. Secondly, the AAT requires accurate temporal information about the onset of each single GA. This goal can be achieved by recording the data with a high sampling rate of 5 kHz (Allen et al., 2000) and storing it together with MRtriggered information about the onset of each artifact occurrence. Because GA amplitudes can substantially change from

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one sampling point to another (N 100 μV), accurate trigger timing in the microsecond range is crucial. One feasible solution to achieve this precision is the synchronization of the two computer clocks responsible for EEG recording and MRI slice timing. However, in cases where data are recorded with a lower sampling rate, and/or accurate trigger timing is not obtained, reasonable GA reduction can be obtained by interpolation of the recorded EEG data and alignment of the GA template before subtraction (Niazy et al., 2005). It is important to note that the AAT procedure does not completely remove the GA. Allen et al. (2000) suggested removing residual GA activity by means of an adaptive filter approach. It is also feasible to low-pass filter the EEG data before further processing. Another important consideration is that even minor head movements during the course of the EEG recording can affect the performance of the AAT approach. In order to take such movements into account, the AAT can be implemented with a local moving average template. The averaging of about 30 neighbouring artifact occurrences may be considered sufficient to create an appropriate template with the main EEG activity being averaged out. While alternative approaches for GA reduction in the frequency domain have been proposed (e.g., Hoffmann et al., 2000), they are much less popular in comparison with the AAT, probably because the GA spectral content substantially overlaps with the EEG spectrum (Bénar et al., 2003). 3.3. The ballistocardiogram (artifact) A second major source of artifact becomes apparent in the period between the recording of MRI images, or after GA removal. The ballistocardiogram (BCG) is related to the cardiac cycle and scales in amplitude proportionally with the magnetic field strength (Tenforde et al., 1983; Debener et al., 2008-this issue). It is commonly agreed that the BCG is related to the pulsatile movement of the head and/or the pulsatile movement of blood and EEG electrodes. It is, therefore, also referred to as the pulse artifact. Blood, EEG electrodes and EEG leads are conductive, and the movement of conductive material in a high static magnetic field creates a current that is picked up by the EEG. In contrast to the GA, the BCG shows substantial variability within and between subjects even without the influence of gross head movement, which may be related to adaptive autonomic properties of the cardiac system. Therefore, temporal fluctuations of autonomic nervous system properties such as heart beat rate, blood pressure, or cardiac output may impact the BCG. Accordingly, its appropriate removal is usually considered the main obstacle that needs to be solved to obtain a reasonable EEG signal quality (Debener et al., 2008-this issue). Due to its close temporal correlation with the pulsatile movements of the heart, an electrocardiogram (ECG) needs to be recorded in order to accurately correct the BCG. In a first step, the R-peak needs to be detected in every single cardiac cycle. Then, a template is computed as an average over a fixed number (e.g. 30) of cardiac cycles. Finally, this template is then subtracted using a procedure similar to that of GA removal. Niazy et al. (2005) have recently proposed an improved method

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for correcting the BCG by using multiple templates determined via a principle component analysis. This approach seems to outperform single template approaches, and the resulting data quality can be even further improved by subsequent application of ICA (Debener et al., 2007). 3.4. Other artifacts A third source of artifact has been reported in many early EEG/ fMRI studies and refers to mechanically induced artifacts that are related to vibrations of the MRI scanner. Some authors have reported that turning off the MRI cryogen pump reduces this problem, while others have found the immobilization of wires using sandbags improves data quality (Bénar et al., 2003). In our experience, vibration-related artifacts depend on the specific scanner site and may not be a problem at all scanner sites. 4. Do simultaneous EEG–fMRI recordings provide new insights into the coupling of electrophysiology and hemodynamics? In a seminal study, Logothetis et al. (2001) investigated the BOLD signal and intracranial recordings of single-unit activity, multi-unit activity, and local field potentials (LFPs) in monkeys (see also Logothetis, 2002). They reported that the time course of LFPs correlated best with that of the BOLD signal for rotating checkerboard stimuli of variable durations. Such LFPs typically show discharges at frequencies in the gamma frequency range (approximately 30–80 Hz). Further recordings in cats revealed that correlations between LFPs and BOLD signal are especially high in the gamma band frequency range (Niessing et al., 2005). A similar result was found by correlating intra-cortical electrophysiological recordings and BOLD responses from human patients (Mukamel et al., 2005). While the functional role of the EEG gamma band is still being debated (e.g. Herrmann et al., 2004), simultaneous EEG–fMRI recordings now provide the tools to obtain further insights. Other frequency bands such as slow potentials have also been suggested to show a correspondence to the BOLD signal as well (Hinterberger et al., 2005; Nagai et al., 2004; Schicke et al., 2006; Khader et al., 2008-this issue). With the advent of event-related single-trial EEG analysis, it now seems possible to study the possible direct coupling between EEG and fMRI (Debener et al., 2005; Eichele et al., 2005, 2008-this issue; Moosmann et al., 2008-this issue; Scheeringa et al., 2008-this issue). We propose that this approach, in combination with further hardware and software developments optimising the simultaneous EEG–fMRI approach, can help to address a number of important questions in the field of cognitive neuroscience (Debener et al., 2006). Simultaneous EEG– fMRI may for instance help to clarify the relationship between ongoing EEG and evoked responses, that is, ERPs. It may also be possible to better understand the functional role of different EEG oscillations. Above and beyond the usual claim that EEG– fMRI provides both high spatial and temporal resolution, this fascinating approach will help to better understand the dynamics of cognitive function.

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