GET THE APP

Scalp and Intracranial EEG-fMRI in Epilepsy

Journal of Neurology & Neurophysiology

ISSN - 2155-9562

Review Article - (2013) Volume 4, Issue 3

Scalp and Intracranial EEG-fMRI in Epilepsy

Louis André Van Graan1, Louis Lemieux1 and Umair Javaid Chaudhary2*
1Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, UK
2MRI Unit, Epilepsy Society, Chalfont St. Peter, UK
*Corresponding Author: Umair Javaid Chaudhary, MRI Unit, Epilepsy Society, Chalfont St. Peter, SL9 0RJ, United Kingdom, Tel: 00 44 1494 601360 Email:

Abstract

The choice of treatment for epilepsy patients relies on the identification and characterization of epileptic discharges and the type of epilepsy. The continued lack of a universally applied gold standard in presurgical evaluation has opened the ways to the investigation of new localizing and mapping tools in difficult cases including simultaneous EEG-fMRI, which can be used to map interictal and ictal discharges-related haemodynamic networks. The rapid increase in the literature on EEG-fMRI attests to its promised contribution in understanding of epilepsy-related and physiological networks of the brain. Its applications to investigate epileptic networks in humans have evolved since the
mid 1990’s, and has yielded a better knowledge of the generation, propagation and particularly localisation of epileptic discharges, as well their interaction with the physiological and pathological brain networks. Here, we review the published literature to raise the awareness of different concepts pertaining to the applications of simultaneous EEGfMRI in epilepsy and discuss their potential impact on our understanding of epilepsy and its clinical management. We also describe our perspectives regarding possible future roles of the technique in epilepsy.

Abbreviations

AED: Antiepileptic Drugs; BOLD: Blood Oxygen Level Dependent; DMN: Default Mode Network; ECG: Electrocardiogram; EEG: Electroencephalography; EEGfMRI: Simultaneous Electroencephalography and Functional Magneticresonance Imaging; EOG: Electro-occulogram; EZ: Epileptogenic Zone; fMRI: Functional Magnetic Resonance Imaging; GLM: General Linear Model; GSWDs: Generalized Spike and Wave Discharges; HRF: Haemodynamic Response Function; ICA: Independent Component Analysis; ictal-SPECT = Ictal Single Photon Emission Computed Tomography; icEEG: Intracranial EEG; icEEG fMRI: Intracranial Electroencephalography and Functional Magneticresonance Imaging; IEDs: Interictal Epileptiform Discharges; IGE: Idiopathic Generalized Epilepsy; ILAE: International League Against Epilepsy; IZ: Irritative Zone; MEG: Magneto-encephalography; MR-compatible: Magnetic Resonance compatible; MRI: Magnetic Resonance Imaging; PCA: Principal Component Analysis; PET: Positron Emission Tomography; RF: Radio Frequency; SOZ: Seizure Onset Zone; SPM: Statistical Parametric Mapping; T: Tesla; TCI: Transitory Cognitive Impairment; video-EEG: Simultaneous Video and Scalp Electroencephalography

Background

Epilepsy can be broadly classified into generalised and focal epilepsy. Some 30% of patients with epilepsy remain refractory to antiepileptic medications requiring further assessment for the control of seizures [1]. In refractory focal epilepsy, surgery has proved a valuable intervention. The success of epilepsy surgery depends, for the most part, on the precise identification of the area of seizure onset (seizure onset zone: SOZ) and the area deemed necessary to be resected to render the patient seizure free (epileptogenic zone: EZ) during presurgical assessment. The current approach to localize the SOZ and EZ is consensus, based on the results from a combination of non-invasive (magnetic resonance imaging (MRI), long term video-electroencephalography (video-EEG) monitoring, magneticencephalography (MEG), positron emission tomography (PET) and ictal single photon emission computed tomography: ictal-SPECT) and invasive (intracranial electroencephalography: icEEG) investigations. There is, however, no agreement as to which tests should constitute this combination, and the combination of tests used varies considerably across institutions as well as studies [2]. Furthermore, in current clinical practice, there is no true ‘gold standard’ localization technique; though in major centres icEEG tends to be the last and most reliable tool to localize the SOZ and EZ prior to surgery.

Against this backdrop, the introduction of simultaneous EEG and functional MRI (EEG-fMRI) has allowed the localization of haemodynamic changes associated with epileptic discharges on EEG for both focal [3] and generalized epilepsy [4-6] and thereby provided unique information on the associated brain networks. It is unique because of the nature of the measured effect, but also in the way that localization is achieved, using fMRI which does not have the fundamental limitations of EEG (or MEG) based localization techniques. EEG-fMRI albeit “technically very challenging,” has “furnished the greatest insights into the relationship between fast neuronal dynamics and their spatially resolved haemodynamic correlates” [7] providing a multilateral view of the structure and function of brain.

The intrinsic contrast mechanism that fMRI relies upon, provides a means of localising function-related hemodynamic changes noninvasively. Essentially, the genesis of the fMRI signal constitutes neuronal activity induced changes in cerebral blood characteristics, including a variation in blood oxygen, detected as the blood-oxygenlevel- dependent (BOLD) contrast [8,9]. Despite limitations imposed by slow temporal characteristics of the BOLD signal [10] and unresolved questions on its relationship to the underlying neural activity, fMRI has been employed widely to map brain network involved in cognition and perception [11]. The case for the utility of the BOLD signal to localize epilepsy, received its impetus and verification independent from simultaneous EEG-fMRI as shown in the initial reported cases of seizures recorded during fMRI scanning and their associated BOLD changes [12,13].

In the light of obvious interest in localizing the generators of interictal epileptiform discharges (IED) on EEG, such recordings were first performed in combination with fMRI in 1993 by Ives [14] followed by the development of EEG-triggered fMRI [15] and simultaneous and continuous EEG-fMRI [3]. Subsequently, the literature evidences development of new insights in the context of EEGfMRI methodologies, including the intriguing involvement of specific networks including the thalamus in relation to generalized spike wave discharges (GSWDs) [6,16,17], the effects of epileptiform activity on neurovascular coupling [18], and the distinct patterns associated with interictal and ictal epileptic discharges in focal epilepsy [19-22]. In this reviewwe discuss the current trends in respect of scientific and clinical application of the technique in epilepsy.

Technical Aspects of Scalp EEG-fMRI: Data Acquisition and Artefact Correction

Recent reviews [23-25] comprehensively describe the methodological details of the EEG-fMRI data acquisition process, we offer a synopsis here. In general, standard or modified scalp electrodes (in number ranging between 19 and 92) and specially-designed battery-powered amplifiers are used to record EEG inside the scanner. Most EEG-fMRI studies of epileptic activity are performed while the patient is lying inside the scanner with their eyes closed, taking care to immobilise the head (for example using a vacuum cushion), which helps with image and EEG quality [26]. The digitised EEG signals are transmitted via fibre-optic cables to a recording computer placed in the MRI control room where EEG is displayed on a recording and display monitor using commercially available software. Functional images are typically acquired using T2*-weighted single-shot gradient-echo echo-planar images sequence, and the scanning session (6-20 minutes duration each) can be repeated once or twice to capture sufficient epileptic events. To date the majority of studies have been conducted on 1.5 and 3Tesla MRI scanners with noted advantages to the sensitivity of BOLD changes at higher field strength [27]. However, human EEGfMRI studies have also been performed on 4T MRI scanner [28] whilst investigations into the feasibility of EEG-fMRI at 7T magnet strength are ongoing [29]. However, it has been shown that RF inhomogeneity at 3 and 7T in relation to the longer electrode leads produce a reduction in the signal intensity in images [30].

EEG recorded inside MRI scanner is obscured by heartbeat related and scanner gradient switching induced artefacts. The approach to EEG correction is determined by the nature of the events of interest, such as their frequency [31,32] and new measures are continually evaluated [33]. However, averaged artefact template subtraction is the most commonly applied method for removing MR-gradient and pulserelated artifact [34,35].

In the early EEG-triggered fMRI studies, the images were acquired at a pre-set time (corresponding to the presumed haemodynamic delay) when IEDs were observed on EEG and compared to images acquired following periods of normal (background) EEG [15,36]. This set up had a number of limitations, including the presumption of the value of the haemodynamic delay (which may be abnormal due to the pathological processes) and the difficulty of accounting for possible interactions between discharges occurring in rapid succession, and the artificial nature of the fMRI time series due to the acquisition discontinuities. In contrast, the advent of simultaneous and continuous EEG-fMRI provided ‘real time’ observations of BOLD relative to the epileptic activity on EEG, thereby facilitating the capture of the full range of potential BOLD signals either prior, during or after an events of interest [37,38].

Haemodynamic Mapping of Epileptic Activity: The Correlation Model

The conventional analysis of EEG-fMRI data is predicated on the following question: in what brain region(s) are the fMRI signal variations correlated with the occurrence of epileptic discharges on EEG? This question is answered using the General Linear Modelling (GLM) framework in which the ‘model’ is derived from the EEG [23]. In other words, the EEG is interpreted and the resulting set of events (of interest) is used to form a predictor of BOLD changes at every scanned location (voxel). For this, each EEG events of interest is then represented by a mathematical function, e.g. stick function of short duration, and convolved with an appropriate haemodynamic response function (HRF) which embodies the haemodynamic delay and recovery time course. The correlated BOLD changes associated with EEG events of interest are presented as thresholded statistical parametric maps (SPMs) which are overlaid onto a glass brain and/or co-registered structural scans for the purpose of anatomic localization. BOLD changes as revealed by these SPMs have been interpreted variably in different centres depending upon: the location of the most statistically significant cluster; earliest BOLD cluster; cluster size; and positive or negative BOLD changes [23].

Events of interest, in the context of EEG-fMRI studies on epilepsy include IED and seizures. Various approaches have been employed to represent epileptic discharges mathematically in the design matrix to assess the associated BOLD signal. Most commonly, single IEDs are represented as zero-duration stick functions and runs of IEDs as boxcar function of variable duration depending upon the duration of the discharge [21,39-41]. Similarly, whole seizures can be represented as a single box-car function or, as proposed in [19,22,42] multiple boxcar functions of variable duration, each representing a distinct seizurerelated phase classified on the basis of spatiotemporal evolution of electrical activity and clinical semiology, and has been shown to provide new insight into the onset, evolution and propagation of seizure-related BOLD changes [19,22,42]. Another form of dynamic ictal imaging is obtained through the use of sequential sliding windows [43,44].

In practice the utility and sensitivity of EEG-fMRI had been limited by the unpredictability of epileptiform activity, whilst a great deal of diagnostic information can be affected or masked by confounds i.e., effects of no interest [45]. The aforementioned is juxtaposed with the clinical context of EEG-fMRI which renders significant emphasis on the best possible extraction and interpretation of the data [39]. These confounds include head motion-related artefact and physiological noise.The fact that motion even on a millimetre scale is sufficient to render fMRI unusable has resulted in a range of means and algorithms to explain motion in the design matrix to improve modeling [46,48]. Physiological noise includes the effects of respiration, changes in oxygenation and brain tissue volume during cardiac cycle [49,50-52] background physiological brain rhythms such as alpha, beta, theta, delta activity and various activities performed by patients during EEG-fMRI acquisition such as swallowing and eye blinks which can be identified using simultaneous video recording [39,53]. Accounting for signal variations associated with these confounds in the fMRI modelling procedure is essential to regress out their effect on the data in order to identify signal variations, specifically, related to the epileptiform discharges.

A crucial question relates to the choice of the HRF. The suitability of the commonly so-called ‘canonical’HRF (derived from, and widely used in cognitive fMRI studies to epileptic activity, in view of possible pathology-related alterations has been tested by using variations of it Fourier basis set, multiple HRFs, finite impulse responsefunctions and gamma functions [19,22,42,50,54-57]. These have revealed some deviations from the canonical shape, mostly in relation to generalised discharges.

Another approach which does not require a priori hypothesis of predicted BOLD response associated with an event of interest is Independent Component Analysis (ICA)which has been applied in a number of fMRI studies of epileptic activity revealing interesting patterns, not found using conventional EEG-based correlation [5,42,56,58-60] confirmed positive, overlapping as well as distinctive results for either approach.

The Sensitivity of EEG-fMRI

The potential advantages of simultaneous scalp EEG-fMRI over scalp EEG, PET and ictal-SPECT as localization techniques include higher spatiotemporal resolution and non-invasive nature [23,24,61] . However, as noted previously the technique’s dependence on scalp EEG to capture and identify epileptic discharges during EEG-fMRI has been a major limitation resulting is reduced sensitivity, in part by the lack of IEDs during fMRI, with roughly 40% of cases showing no clear IED, but also because of sub-optimal modelling of the fMRI signal, with 30% of cases with IED not showing significant BOLD changes [45]. Recent advances in modelling by explaining previously unmodelled physiological activities have resulted in increased sensitivity; for example by correlating topographic map features derived from epileptic activity recorded during long-term video-EEG monitoring with the EEG recorded during fMRI.In turn, using the strength of this correlation as a predictor, one can obtain seemingly epileptic patterns of BOLD change even in cases without clear epileptic discharges recorded during fMRI, thereby doubling the technique’s sensitivity to around 80% [62,63].

The sensitivity of ictal studies is generally greater varying between 66 and 100% for cases in whom at least one seizure was captured, possibly reflecting the greater magnitude of the BOLD changes for these events compared to IED [16,19,20,22,42,44,58,64-70]. This wide range of sensitivity largely depends on the variable patient selection criteria, various modelling approaches and concordance criteria to assess localization of BOLD changes.

Localization of Epileptic Focus during Presurgical Assessment: Clinical Value

As it is conventional for the evaluation of novel localization techniques in epilepsy, their value is evaluated in comparison with other, more established methods such as scalp EEG, MRI, ictal-SPECT and icEEG when available [23].

The potential clinical contribution of EEG–fMRI has been demonstrated by reports of patient groups who were reconsidered for surgery after undergoing EEG-fMRI despite previous rejection as surgical candidates in whom the technique revealed BOLD changes in areas which were amenable to surgery [41,71]. Moreover, EEG-fMRI can corroborate negative evidence regarding surgical candidacy and may suggest poor postsurgical outcome, especially when IED-related BOLD networks are widespread in patients with focal cortical dysplasia [21,41]. Moreover, IED-related BOLD localization of the epileptic focus (defined on icEEG) is more specific than scalp EEG alone, with implications for the implantation strategy [72-74].

Early fMRI studies (without EEG) showed that it has valuable role in mapping seizure-related BOLD changes potentially surpassing ictal-SPECT, PET and EEG [12,13]. These initial reports were followed by a large number of case reports showing the utility of EEG-fMRI in localizing the SOZ. However more recent, relatively larger, studies of seizures (captured either fortuitously in the course of studies focused on interictal activity or intentionally have shown that EEG-fMRI can localize the SOZ at sub-lobar level (better than scalp EEG) in a large (~85%) proportion of cases. In addition, it can also separate seizure onset-related BOLD changes from propagation-related BOLD changes [19,22,42,44,58,75,76]. The potential clinical role of ictal studies using EEG-fMRI is constrained by the rarity and unpredictability of seizures and the potential impact of seizure-related motion artefact on data quality. These localizing findings in interictal and ictal studies which can be used during presurgical assessment may render EEG-fMRI a potentially viable clinical tool.

In addition to mapping IED and seizure-related BOLD networks for localization of the EZ, EEG-fMRI has been used to explore BOLD changes associated with asymmetric delta activity in a patient with refractory focal epilepsy. The delta activity related BOLD changes localized the EZ as defined by icEEG and cortical stimulation even in the absence of clear IEDs on EEG [77].

Epileptic Networks in Generalised and Focal Epilepsies

The first deployment of continuous EEG–fMRI in a case of generalized epilepsy with frequent absence seizures and later studies revealed a common pattern of BOLD increases in the thalamus and widespread BOLD decreases in medial and lateral frontal, superior parietal, posterior cingulate, precuneusand caudate and posterior brainstem (reticular formation) [4,6,68,78,79]. This provided further evidence of the importance of cortico-subcortical connectivity and suggested a role for the default mode network (DMN) during GSWDs [80]. The DMN essentially describes an organized network in which there is a balance of relative activity/inactivity distributed amongst brain regions during the (admittedly ill defined) ‘resting state’ or ‘resting wakefulness’[80]. The suggestion is that this balance is altered in the context of function/epileptic discharges-related activity reflected as BOLD changes in the DMN [61,81,82].

Since the DMN is thought to be intimately engaged in processes of attention and working [83], it has been suggested that epileptic activityrelated BOLD decreases in this network reflect transient changes in awareness or consciousness [4,81,82,84]. Also, in patients with refractory focal seizures BOLD decreases in the DMN are found to be significantly correlated with the loss of consciousness [19]. Taking the opposite causal viewpoint, it has also been shown that BOLD changes in the precuenus (part of the DMN) may act to facilitate the occurrence of GSWDs [85-87] which can be seen as consistent with the cortical focus theory of initiation of absences.

In addition to the BOLD changes in the DMN, EEG-fMRI has also shown BOLD changes in other networks in different types of epilepsy, playing its role in moving the debate forward from ‘zones’ to ‘networks’ [139]. In fact, EEG-fMRI studies have shown the involvement of various resting state networks and symptomatogenic areas in refractory focal seizures [23], visual attention network in children with photo paroxysmal response [89,90] musicogenic network in musicogenic seizures [91,92]. Reading epilepsy-related network [70,76] and motor network in epilepsiapartialis continua of the hand [93]. However in children with Dravet Syndrome, thalamic andDMN-related regions were shown to reveal BOLD changes though a syndrome-specific epileptic network could not be identified [94]. BOLD changes have also been revealed prior to the onset epileptic discharges on EEG, which are found to be more focal for IED [89] and more widespread for seizures [19,20,69].

The presence of BOLD changes from the conventionally identified epileptic focus, during and prior to the seizure onset on EEG, is consistent with epileptic network hypothesis [95-97]. BOLD changes in apparently non-pathological cortex may reflect the fluctuations in baseline resting state networkactivity either that are necessary as part of the initiation process through interactions with the pathological region/ network oras a result of the epileptic activity, such as recruitment of these areas in seizure generation or propagation [19,98]. Moreover, relatively widespread preictal (defined by scalp EEG and clinical manifestations) BOLD decreases followed by increases, prior to the electrical activity seen on scalp EEG may reflect active inhibitory circuits [99-101] which are subsequently overtaken by the increase in neuronal activity at the seizure onset [102] or projected neuronal activity not visible in the EEG [103,104].

In relation to the characterisation of the connectivity between different nodes of these epileptic networks [105-107] effective connectivity studies have also helped to identify areas involved in seizure facilitation [86] and propagation[108]. Changes in resting state functional connectivity have been observed in the orbitofrontal cortex [109,110], thalamus [111,112] and the DMN [110,113] in children with absence epilepsy pointing to further investigations of the interaction between cognition and GSWDs-related networks.

One of the early motivations for fMRI studies in generalised epilepsies was the possibility of identifying syndrome-specific BOLD patterns for IGE and secondary generalized epilepsy [16]. Recently, patients with idiopathic generalized epilepsy who are refractory or responsive to Valproate treatment were shown to have different GSWDrelated BOLD patterns [114,115]. Similarly, in different focal epilepsies, syndrome specific IED-related BOLD patterns have been identified which involve the DMN-related and non-DMN related areas [116].

Cognitive Studies in Conjunction with EEG-fMRI

Cognitive impairment affects a large proportion of epilepsy patients and is reported as one of the primary clinical manifestations of pathological interictal behaviour [117,118]. IEDs can be associated with transitory cognitive impairment (TCI) especially for cognitively demanding tasks and >3sec long GSWDs [119,120]. Using attention [66,67] and working memory [121] tasks during EEG-fMRI studies alterations in the respective cortico-subcortical BOLD attention and working memory networks secondary to the presence of GSWDs during the performance of the task were demonstrated. These findings suggest that EEG-fMRI can detect functional changes in the underlying brain networks which might not be detected when tested clinically for a behavioural correlate [121]. Another relatively larger study in children with absence epilepsy also showed decreases in resting functional connectivity in medial frontal cortex implicated in the attention network, whereas impaired performance on attention task was correlated with decreased activation of medial frontal cortex [122]. Conversely, a case report [123] in a girl without cognitive impairment during GSWDs revealed GSWD-related BOLD changes in a similar cortico-subcortical network, raising the question of the causal link between such patterns and the cognitive (‘downstream’) or facilitation (‘upstream’) effects of GSWD [89].

Simultaneous Intracranial EEG-fMRI

The limitations of using scalp EEG to predict BOLD signal variations related to epileptic and to other types of activity, throughout the brain include the EEG’s limited sensitivity and lack of regional specificity. Furthermore, despite developments that have resulted in important increases in sensitivity [63,124] the lack of significant BOLD changes associated with (sometimes abundant) epileptic discharges in a significant proportion of cases and the sometimes complex IED-related patterns remain unexplained. The possibility of recording intracranial EEG and fMRI simultaneously may help us answer these questions.

Typically icEEG using subdural grids and depth electrodes is performed to localise the SOZ, EZ and eloquent cortex during presurgical assessment in refractory focal epilepsy [125] after a thorough assessment including detailed clinical history and examination, epilepsy protocol MRI, long-term scalp video-telemetry, neuropsychology and neuropsychiatry assessments and language fMRI [126]. Initial feasibility and safety studies [127-130] have been performed for simultaneous icEEG-fMRI at 1.5T and 3T suggesting this new investigation technique can be employed without posing any significant additional health risks if a strict protocol is followed. fMRI signal degradation is observed within 1cm of the electrode contacts and is orientation dependent [130]. Simultaneous icEEG-fMRI has the potential advantage over scalp EEG-fMRI of much higher electrophysiological sensitivity and regional specificity, particularly for depth electrodes [131]. Moreover, it may also circumvent limited spatial sampling of icEEG. Significant IED-related BOLD changes both close and remote from the most active electrode contacts were observed [132,133]. In one patient, IED-related BOLD network included regions that could not be sampled by icEEG and were neither resected, a finding which offered notional explanation for the persistence of seizures after surgery [133].

Future Perspectives for EEG-fMRI

Functional MRI combined with EEG and video [134] is a unique and powerful tool for the study of epileptic activity in humans and over the time multiple studies have shown its utility in mapping BOLD changes associated with interictal and ictal discharges [19,21,22,27,42,45,47,71,73,137,138]. However, certain questions remain to be answered in future studies. For example: Is there a single focus or a network responsible for a particular type of epileptic activity and does it have an impact on the surgical outcome? How do seizures and antiepileptic medications affect physiological brain networks in epilepsy? What measures can be taken to improve the clinical utility of the technique? What is the impact of epileptic discharges on cognition/consciousness and can it be measured using EEG-fMRI? What are the neuronal correlates of BOLD networks distributed across pathological as well as healthy cortex as revealed by scalp EEG-fMRI? Here, we present our perspective to address these questions in future.

Recent scalp EEG-fMRI studies point towards the existence of a network associated with IED and seizures [19,88,117]. Moreover, current clinical gold standard i.e., icEEG has its own limitations such as limited spatial sampling; comparison of IED and seizure-related BOLD networks across whole brain with postsurgical outcome will reflect the predictive power and true specificity of the technique.

It has shown that frontal piriform cortex ipsilateral to the presumed focus of epilepsy might be a common area involved in the epileptic activity in different types of epilepsy [135]. Future group studies in subtypes of epilepsy exploring the existence of a common IED and seizurerelated BOLD network in patients with good versus poor postsurgical outcome may help to identify if a single area/focus or a network is responsible for the generation and propagation of epileptic activity.

Recently, it has been shown in a pilot study that BOLD changes in DMN related areas during performance of a cognitive task are different under the influence of Topiramate as compared to other AEDs [136]. This is an interesting observation. IED-related BOLD changes have been shown in DMN related areas previously [82], therefore, it will be valuable to investigate the effect of different AEDs on IED related BOLD networks.

EEG-fMRI studies performed during performance of a cognitive task have suggested that it may be able to show a functional signature for loss of awareness associated with seizures and possibly with IEDs. This would require a thorough assessment of awareness during EEGfMRI studies of epileptic activity. This will not be an easy undertaking and study design may have many possible confounding factors: type of epilepsy, nature and number of epileptic discharges and sensitivity of the task being performed to assess the awareness. This will require a careful selection of an appropriately sensitive task to detect loss of awareness in the future studies of EEG-fMRI on seizures. More specifically icEEGfMRI studies can be more helpful to assess the relationship between awareness and IEDs as they do not suffer from the low sensitivity of scalp EEG.

However, the clinical application of the technique seems to remain limited, probably highlighting the diverse data analysis and concordance assessment schemes used by different research groups which need to be standardized for clinical purposes. In an effort to devise such scheme we propose a flowchart diagram to highlight a proposed scheme of data analysis and interpretation (Figure 1). Furthermore, a comparison of the localisation sensitivity of EEG-fMRI with other non-invasive imaging techniques: PET, ictal SPECT and MEG, and its role in planning the placement of intracranial electrodes prospectively will demonstrate its true utility during the presurgical evaluation of patients with refractory focal epilepsy.

Figure

Figure 1: UCL interictal and ictal scalp EEG-fMRI analysis flowchart
*Seizures identified and divided into phases according to their spatiotemporal evolution
~One GLM for epileptic spikes and seizures which also includes additional regressors explaining effects of no interest such as: motion, pulse and physiological activities
¬Statistically significant at p<0.05 family wise error corrected or p<0.001 uncorrected
+Evaluation of concordance of global maximum and other BOLD clusters with the SOZ

Simultaneous and synchronised recording of video during EEGfMRI [134] have shown to help in event identification and representation [19] and to increase sensitivity by modelling physiological activities in the design matrix [39]. In the future, the identification and labelling of physiological activities such as: eye blinks, chewing and respiration can be automated by adding extra electrodes around the eyes for electroocculography and around the face for EMG and using respiration belt respectively. Moreover, EMG recordings can also be very beneficial to investigate BOLD correlates of myoclonus [137].

The current approaches to analyse seizure-related data can show BOLD changes in areas recruited during the whole seizure or during different ictal phases represented in the design matrix as variable duration blocks; however it cannot show the evolutionary recruitment of these areas on a temporal scale within a single ictal phase. Further exploration of seizure-related fMRI data with dynamic modelling approaches e.g., real-time ICA [138] or with dynamic causal modelling to understand the connectivity between these regions can improve our knowledge regarding seizure spread and associated mechanisms. In the context of seizure spread, the GLM based group analysis approach for seizures may also highlight a common area or network.

Other limitations of current EEG-fMRI studies on seizures include: motion during seizures as a confounding factor to deteriorate data quality and to increase false positive results, and low temporal resolution of fMRI (~3 seconds) being unable to separate propagation related BOLD changes from onset related BOLD changes if occurring too fast. Future studies using fMRI sequences with online motion correction [139] and higher temporal resolution [140] may map activity patterns over the entire brain at the second time scale and improve our understanding of seizure initiation and propagation and help us answer clinically relevant questions. However, it remains to be explored if simultaneous recording of EEG during scanning will be compatible with fMRI sequences with online motion correction and higher temporal resolution.

Simultaneous icEEG-fMRI is potentially useful but only for the relatively small number of patients who undergo invasive evaluation prior to surgery. Future studies comparing BOLD localisation on scalp EEG-fMRI, using combination of conventional GLM based analysis and topographical map correlation based analysis [63], with that of icEEG-fMRI may reveal if the former has the same power and can guide implantation of intracranial electrodes. The application of icEEG-fMRI has also opened new horizons to investigate epileptic activity specific to icEEG such as high frequency oscillations. Evaluation of the transfer function (coupling) linking BOLD changes to IEDs on icEEG will be an important avenue of future research.

In conclusion, application of EEG-fMRI in epilepsy holds great promise in future to address multiple relevant scientific questions and the possibility of being employed as a clinical investigation tool in presurgical assessment for refractory focal epilepsy. However, further larger multicentre studies are required to assess the potential of the technique as a clinical investigation tool and its cost-effectiveness with standardized methods of data collection, analysis and interpretation.

Acknowledgements

We are thankful to the Action Medical Research, UK and Higher Education Commission of Pakistan for funding our research work through grants and bursaries.

References

  1. Kwan P, Sander JW (2004) The natural history of epilepsy: an epidemiological view. J Neurol Neurosurg Psychiatry 75: 1376-1381.
  2. Burch J, Marson A, Beyer F, Soares M, Hinde S, et al. (2012) Dilemmas in the interpretation of diagnostic accuracy studies on presurgical workup for epilepsy surgery. Epilepsia 53: 1294-1302.
  3. Lemieux L, Salek-Haddadi A, Josephs O, Allen P, Toms N, et al. (2001) Event-related fMRI with simultaneous and continuous EEG: description of the method and initial case report. Neuroimage 14: 780-787.
  4. Gotman J, Grova C, Bagshaw A, Kobayashi E, Aghakhani Y, et al. (2005) Generalized epileptic discharges show thalamocortical activation and suspension of the default state of the brain. Proc Natl Acad Sci U S A 102: 15236-15240.
  5. Moeller F, LeVan P, Gotman J (2011) Independent component analysis (ICA) of generalized spike wave discharges in fMRI: comparison with general linear model-based EEG-fMRI. Hum Brain Mapp 32: 209-217.
  6. Salek-Haddadi A, Lemieux L, Merschhemke M, Friston KJ, Duncan JS, et al. (2003) Functional magnetic resonance imaging of human absence seizures. Ann Neurol 53: 663-667.
  7.  Mulert C, Lemieux L (2010) EEG-fMRI: physiological basis, technique, and applications. Heidelberg: Springer.
  8. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, et al. (1992) Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A 89: 5675-5679.
  9. Ogawa S, Lee TM, Nayak AS, Glynn P (1990) Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn Reson Med 14: 68-78.
  10. Vulliemoz S, Lemieux L, Daunizeau J, Michel CM, Duncan JS (2010) The combination of EEG source imaging and EEG-correlated functional MRI to map epileptic networks. Epilepsia 51: 491-505.
  11. Ekstrom A (2010) How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res Rev 62: 233-244.
  12. Detre JA, Sirven JI, Alsop DC, O'Connor MJ, French JA (1995) Localization of subclinical ictal activity by functional magnetic resonance imaging: correlation with invasive monitoring. Ann Neurol 38: 618-624.
  13. Jackson GD, Connelly A, Cross JH, Gordon I, Gadian DG (1994) Functional magnetic resonance imaging of focal seizures. Neurology 44: 850-856.
  14. Ives JR, Warach S, Schmitt F, Edelman RR, Schomer DL (1993) Monitoring the patient's EEG during echo planar MRI. Electroencephalogr Clin Neurophysiol 87: 417-420.
  15. Warach S, Ives JR, Schlaug G, Patel MR, Darby DG, et al. (1996) EEG-triggered echo-planar functional MRI in epilepsy. Neurology 47: 89-93.
  16. Hamandi K, Salek-Haddadi A, Laufs H, Liston A, Friston K, et al. (2006) EEG-fMRI of idiopathic and secondarily generalized epilepsies. Neuroimage 31: 1700-1710.
  17. Kobayashi E, Bagshaw AP, Grova C, Dubeau F, Gotman J (2006) Negative BOLD responses to epileptic spikes. Hum Brain Mapp 27: 488-497.
  18. Carmichael DW, Hamandi K, Laufs H, Duncan JS, Thomas DL, et al. (2008) An investigation of the relationship between BOLD and perfusion signal changes during epileptic generalised spike wave activity. Magn Reson Imaging 26: 870-873.
  19. Chaudhary UJ, Carmichael DW, Rodionov R, Thornton RC, Bartlett P, et al. (2012) Mapping preictal and ictal haemodynamic networks using video-electroencephalography and functional imaging. Brain 135: 3645-3663.
  20. Donaire A, Bargallo N, Falcón C, Maestro I, Carreno M, et al. (2009) Identifying the structures involved in seizure generation using sequential analysis of ictal-fMRI data. Neuroimage 47: 173-183.
  21. Thornton R, Vulliemoz S, Rodionov R, Carmichael DW, Chaudhary UJ, et al. (2011) Epileptic networks in focal cortical dysplasia revealed using electroencephalography-functional magnetic resonance imaging. Ann Neurol 70: 822-837.
  22. Tyvaert L, Hawco C, Kobayashi E, LeVan P, Dubeau F, et al. (2008) Different structures involved during ictal and interictal epileptic activity in malformations of cortical development: an EEG-fMRI study. Brain 131: 2042-2060.
  23. Chaudhary UJ, Duncan JS, Lemieux L (2013) Mapping hemodynamic correlates of seizures using fMRI: A review. Hum Brain Mapp 34: 447-466.
  24. Laufs H, Daunizeau J, Carmichael DW, Kleinschmidt A (2008) Recent advances in recording electrophysiological data simultaneously with magnetic resonance imaging. Neuroimage 40: 515-528.
  25. Laufs H (2012) A personalized history of EEG-fMRI integration. Neuroimage 62: 1056-1067.
  26. Bénar C, Aghakhani Y, Wang Y, Izenberg A, Al-Asmi A, et al. (2003) Quality of EEG in simultaneous EEG-fMRI for epilepsy. Clin Neurophysiol 114: 569-580.
  27. Gholipour T, Moeller F, Pittau F, Dubeau F, Gotman J(2011). Reproducibility of interictal EEG-fMRI results in patients with epilepsy. Epilepsia 52: 433-442.
  28. Difrancesco MW, Holland SK, Szaflarski JP (2008) Simultaneous EEG/functional magnetic resonance imaging at 4 Tesla: correlates of brain activity to spontaneous alpha rhythm during relaxation. J Clin Neurophysiol 25: 255-264.
  29. Mullinger K, Brookes M, Stevenson C, Morgan P, Bowtell R (2008) Exploring the feasibility of simultaneous electroencephalography/functional magnetic resonance imaging at 7 T. Magn Reson Imaging 26: 968-977.
  30. Mullinger K, Debener S, Coxon R, Bowtell R (2008) Effects of simultaneous EEG recording on MRI data quality at 1.5, 3 and 7 tesla. Int J Psychophysiol 67: 178-188.
  31. Grouiller F, Vercueil L, Krainik A, Segebarth C, Kahane P, et al. (2007) A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. Neuroimage 38: 124-137.
  32. Ritter P, Becker R, Graefe C, Villringer A (2007) Evaluating gradient artifact correction of EEG data acquired simultaneously with fMRI. Magn Reson Imaging 25: 923-932.
  33. de Munck JC, van Houdt PJ, Gonçalves SI, van Wegen E, Ossenblok PP (2013) Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction. Neuroimage 64: 407-415.
  34. Allen PJ, Polizzi G, Krakow K, Fish DR, Lemieux L (1998) Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction. Neuroimage 8: 229-239.
  35. Allen PJ, Josephs O, Turner R (2000) A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12: 230-239.
  36. Krakow K, Woermann FG, Symms MR, Allen PJ, Lemieux L, et al. (1999) EEG-triggered functional MRI of interictal epileptiform activity in patients with partial seizures. Brain 122 : 1679-1688.
  37. Bénar CG, Gross DW, Wang Y, Petre V, Pike B, et al. (2002) The BOLD response to interictal epileptiform discharges. Neuroimage 17: 1182-1192.
  38. Masterton RA, Abbott DF, Fleming SW, Jackson GD (2007) Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings. Neuroimage 37: 202-211.
  39. Chaudhary UJ, Rodionov R, Carmichael DW, Thornton RC, Duncan JS, et al. (2012) Improving the sensitivity of EEG-fMRI studies of epileptic activity by modelling eye blinks, swallowing and other video-EEG detected physiological confounds. Neuroimage 61: 1383-1393.
  40. Di Bonaventura C, Vaudano AE, Carnì M, Pantano P, Nucciarelli V, et al. (2006) EEG/fMRI study of ictal and interictal epileptic activity: methodological issues and future perspectives in clinical practice. Epilepsia 47 Suppl 5: 52-58.
  41. Zijlmans M, Huiskamp G, Hersevoort M, Seppenwoolde JH, van Huffelen AC, et al. (2007) EEG-fMRI in the preoperative work-up for epilepsy surgery. Brain 130: 2343-2353.
  42. Thornton RC, Rodionov R, Laufs H, Vulliemoz S, Vaudano A, et al. (2010) Imaging haemodynamic changes related to seizures: comparison of EEG-based general linear model, independent component analysis of fMRI and intracranial EEG. Neuroimage 53: 196-205.
  43. Donaire A, Falcón C, Carreno M, Bargallo N, Rumià J, et al. (2009) Sequential analysis of fMRI images: A new approach to study human epileptic networks. Epilepsia 50: 2526-2537.
  44. Tyvaert L, LeVan P, Dubeau F, Gotman J (2009) Noninvasive dynamic imaging of seizures in epileptic patients. Hum Brain Mapp 30: 3993-4011.
  45. Salek-Haddadi A, Diehl B, Hamandi K, Merschhemke M, Liston A, et al. (2006) Hemodynamic correlates of epileptiform discharges: an EEG-fMRI study of 63 patients with focal epilepsy. Brain Res 1088: 148-166.
  46. Friston KJ, Ashburner J, Frith C, Poline JB, Heather JD, et al. (1995) Spatial Registration and Normalization of Images. Human Brain Mapping 3: 165-189.
  47. Lemieux L, Salek-Haddadi A, Lund TE, Laufs H, Carmichael D (2007) Modelling large motion events in fMRI studies of patients with epilepsy. Magn Reson Imaging 25: 894-901.
  48. Wilke M (2012) An alternative approach towards assessing and accounting for individual motion in fMRI timeseries. Neuroimage 59: 2062-2072.
  49. van Houdt PJ, Ossenblok PP, Boon PA, Leijten FS, Velis DN, et al. (2010) Correction for pulse height variability reduces physiological noise in functional MRI when studying spontaneous brain activity. Hum Brain Mapp 31: 311-325.
  50. Bagshaw AP, Aghakhani Y, Bénar CG, Kobayashi E, Hawco C, et al. (2004) EEG-fMRI of focal epileptic spikes: analysis with multiple haemodynamic functions and comparison with gadolinium-enhanced MR angiograms. Hum Brain Mapp 22: 179-192.
  51. Liston AD, Lund TE, Salek-Haddadi A, Hamandi K, Friston KJ, et al. (2006) Modelling cardiac signal as a confound in EEG-fMRI and its application in focal epilepsy studies. Neuroimage 30: 827-834.
  52. Mullinger KJ, Morgan PS, Bowtell RW (2008) Improved artifact correction for combined electroencephalography/functional MRI by means of synchronization and use of vectorcardiogram recordings. J Magn Reson Imaging 27: 607-616.
  53. Tyvaert L, Levan P, Grova C, Dubeau F, Gotman J (2008) Effects of fluctuating physiological rhythms during prolonged EEG-fMRI studies. Clin Neurophysiol 119: 2762-2774.
  54. Friston KJ, Frith CD, Turner R, Frackowiak RS (1995) Characterizing evoked hemodynamics with fMRI. Neuroimage 2: 157-165.
  55. Lemieux L, Laufs H, Carmichael D, Paul JS, Walker MC, et al. (2008) Noncanonical spike-related BOLD responses in focal epilepsy. Hum Brain Mapp 29: 329-345.
  56. van Houdt PJ, de Munck JC, Zijlmans M, Huiskamp G, Leijten FS, et al. (2010) Comparison of analytical strategies for EEG-correlated fMRI data in patients with epilepsy. Magn Reson Imaging 28: 1078-1086.
  57. Grouiller F, Vercueil L, Krainik A, Segebarth C, Kahane P, et al. (2010) Characterization of the hemodynamic modes associated with interictal epileptic activity using a deformable model-based analysis of combined EEG and functional MRI recordings. Hum Brain Mapp 31: 1157-1173.
  58. LeVan P, Tyvaert L, Moeller F, Gotman J (2010) Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients. Neuroimage 49: 366-378.
  59. Rodionov R, De Martino F, Laufs H, Carmichael DW, Formisano E, et al. (2007) Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI. Neuroimage 38: 488-500.
  60. Caballero-Gaudes C, Van de Ville D, Grouiller F, Thornton R, Lemieux L, et al. (2013) Mapping interictal epileptic discharges using mutual information between concurrent EEG and fMRI. Neuroimage 68: 248-262.
  61. Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453: 869-878.
  62. Elshoff L, Groening K, Grouiller F, Wiegand G, Wolff S, et al. (2012) The value of EEG-fMRI and EEG source analysis in the presurgical setup of children with refractory focal epilepsy. Epilepsia 53: 1597-1606.
  63. Grouiller F, Thornton RC, Groening K, Spinelli L, Duncan JS, et al. (2011) With or without spikes: localization of focal epileptic activity by simultaneous electroencephalography and functional magnetic resonance imaging. Brain 134: 2867-2886.
  64. Archer JS, Waites AB, Abbott DF, Federico P, Jackson GD (2006) Event-related fMRI of myoclonic jerks arising from dysplastic cortex. Epilepsia 47: 1487-1492.
  65. Archer JS, Abbott DF, Masterton RA, Palmer SM, Jackson GD (2010) Functional MRI interactions between dysplastic nodules and overlying cortex in periventricular nodular heterotopia. Epilepsy Behav 19: 631-634.
  66. Bai X, Vestal M, Berman R, Negishi M, Spann M, et al. (2010) Dynamic time course of typical childhood absence seizures: EEG, behavior, and functional magnetic resonance imaging. J Neurosci 30: 5884-5893.
  67. Berman R, Negishi M, Vestal M, Spann M, Chung MH, et al. (2010) Simultaneous EEG, fMRI, and behavior in typical childhood absence seizures. Epilepsia 51: 2011-2022.
  68. Carney PW, Masterton RA, Harvey AS, Scheffer IE, Berkovic SF, et al. (2010) The core network in absence epilepsy. Differences in cortical and thalamic BOLD response. Neurology 75: 904-911.
  69. Federico P, Abbott DF, Briellmann RS, Harvey AS, Jackson GD (2005) Functional MRI of the pre-ictal state. Brain 128: 1811-1817.
  70. Salek-Haddadi A, Mayer T, Hamandi K, Symms M, Josephs O, et al. (2009) Imaging seizure activity: a combined EEG/EMG-fMRI study in reading epilepsy. Epilepsia 50: 256-264.
  71. Zijlmans M, Buskens E, Hersevoort M, Huiskamp G, van Huffelen AC, et al. (2008) Should we reconsider epilepsy surgery? The motivation of patients once rejected. Seizure 17: 374-377.
  72. Pittau F, Dubeau F, Gotman J (2012) Contribution of EEG/fMRI to the definition of the epileptic focus. Neurology 78: 1479-1487.
  73. Pesaresi I, Cosottini M, Belmonte G, Maritato P, Mascalchi M, et al. (2011) Reproducibility of BOLD localization of interictal activity in patients with focal epilepsy: intrasession and intersession comparisons. MAGMA 24: 285-296.
  74. van Houdt PJ, de Munck JC, Leijten FS, Huiskamp GJ, Colon AJ, et al. (2013) EEG-fMRI correlation patterns in the presurgical evaluation of focal epilepsy: a comparison with electrocorticographic data and surgical outcome measures. Neuroimage 75: 238-248.
  75. Meletti S, Vignoli A, Benuzzi F, Avanzini P, Ruggieri A, et al. (2012) Ictal involvement of the nigrostriatal system in subtle seizures of ring chromosome 20 epilepsy. Epilepsia 53: e156-160.
  76. Vaudano AE, Carmichael DW, Salek-Haddadi A, Rampp S, Stefan H, et al. (2012) Networks involved in seizure initiation. A reading epilepsy case studied with EEG-fMRI and MEG. Neurology 79: 249-253.
  77. Laufs H, Hamandi K, Walker MC, Scott C, Smith S, et al. (2006) EEG-fMRI mapping of asymmetrical delta activity in a patient with refractory epilepsy is concordant with the epileptogenic region determined by intracranial EEG. Magn Reson Imaging 24: 367-371.
  78. Archer JS, Abbott DF, Waites AB, Jackson GD (2003) fMRI "deactivation" of the posterior cingulate during generalized spike and wave. Neuroimage 20: 1915-1922.
  79. Moeller F, Siebner HR, Wolff S, Muhle H, Boor R, et al. (2008) Changes in activity of striato-thalamo-cortical network precede generalized spike wave discharges. Neuroimage 39: 1839-1849.
  80. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, et al. (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98: 676-682.
  81. Laufs H, Lengler U, Hamandi K, Kleinschmidt A, Krakow K (2006) Linking generalized spike-and-wave discharges and resting state brain activity by using EEG/fMRI in a patient with absence seizures. Epilepsia 47: 444-448.
  82. Laufs H, Hamandi K, Salek-Haddadi A, Kleinschmidt AK, Duncan JS, et al. (2007) Temporal lobe interictal epileptic discharges affect cerebral activity in "default mode" brain regions. Hum Brain Mapp 28: 1023-1032.
  83. Raichle ME, Mintun MA (2006) Brain work and brain imaging. Annu Rev Neurosci 29: 449-476.
  84. Fahoum F, Zelmann R, Tyvaert L, Dubeau F, Gotman J (2013) Epileptic discharges affect the default mode network - FMRI and intracerebral EEG evidence. PLoS One 8: e68038.
  85. Benuzzi F, Mirandola L, Pugnaghi M, Farinelli V, Tassinari CA, et al. (2012) Increased cortical BOLD signal anticipates generalized spike and wave discharges in adolescents and adults with idiopathic generalized epilepsies. Epilepsia 53: 622-630.
  86. Vaudano AE, Laufs H, Kiebel SJ, Carmichael DW, Hamandi K, et al. (2009) Causal hierarchy within the thalamo-cortical network in spike and wave discharges. PLoS One 4: e6475.
  87. Meeren H, van Luijtelaar G, Lopes da Silva F, Coenen A (2005) Evolving concepts on the pathophysiology of absence seizures: the cortical focus theory. Arch Neurol 62: 371-376.
  88. Laufs H (2012) Functional imaging of seizures and epilepsy: evolution from zones to networks. Curr Opin Neurol 25: 194-200.
  89. Jacobs J, Levan P, Moeller F, Boor R, Stephani U, et al. (2009) Hemodynamic changes preceding the interictal EEG spike in patients with focal epilepsy investigated using simultaneous EEG-fMRI. Neuroimage 45: 1220-1231.
  90. Moeller F, Siebner HR, Ahlgrimm N, Wolff S, Muhle H, et al. (2009) fMRI activation during spike and wave discharges evoked by photic stimulation. Neuroimage 48: 682-695.
  91. Marrosu F, Barberini L, Puligheddu M, Bortolato M, Mascia M, et al. (2009) Combined EEG/fMRI recording in musicogenic epilepsy. Epilepsy Res 84: 77-81.
  92. Mórocz IA, Karni A, Haut S, Lantos G, Liu G (2003) fMRI of triggerable aurae in musicogenic epilepsy. Neurology 60: 705-709.
  93. Vaudano AE, Di Bonaventura C, Carni M, Rodionov R, Lapenta L, et al. (2012) Ictal haemodynamic changes in a patient affected by "subtle" Epilepsia Partialis Continua. Seizure 21: 65-69.
  94. Moehring J, von Spiczak S, Moeller F, Helbig I, Wolff S, et al. (2013) Variability of EEG-fMRI findings in patients with SCN1A-positive Dravet syndrome. Epilepsia 54: 918-926.
  95. Bartolomei F, Wendling F, Chauvel P (2008) [The concept of an epileptogenic network in human partial epilepsies]. Neurochirurgie 54: 174-184.
  96. Bartolomei F, Gavaret M, Hewett R, Valton L, Aubert S, et al. (2011) Neural networks underlying parietal lobe seizures: a quantified study from intracerebral recordings. Epilepsy Res 93: 164-176.
  97. Vaugier L, Aubert S, McGonigal A, Trébuchon A, Guye M, et al. (2009) Neural networks underlying hyperkinetic seizures of "temporal lobe" origin. Epilepsy Res 86: 200-208.
  98. Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A 104: 13170-13175.
  99. Gnatkovsky V, Librizzi L, Trombin F, de Curtis M (2008) Fast activity at seizure onset is mediated by inhibitory circuits in the entorhinal cortex in vitro. Ann Neurol 64: 674-686.
  100. Trombin F, Gnatkovsky V, de Curtis M (2011) Changes in action potential features during focal seizure discharges in the entorhinal cortex of the in vitro isolated guinea pig brain. J Neurophysiol 106: 1411-1423.
  101. Wendling F, Hernandez A, Bellanger JJ, Chauvel P, Bartolomei F (2005) Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG. J Clin Neurophysiol 22: 343-356.
  102. Huberfeld G, Menendez de la Prida L, Pallud J, Cohen I, Le Van Quyen M, et al. (2011) Glutamatergic pre-ictal discharges emerge at the transition to seizure in human epilepsy. Nat Neurosci 14: 627-634.
  103. Ray A, Tao JX, Hawes-Ebersole SM, Ebersole JS (2007) Localizing value of scalp EEG spikes: a simultaneous scalp and intracranial study. Clin Neurophysiol 118: 69-79.
  104. Yu JM, Tyvaert L, Levan P, Zelmann R, Dubeau F, et al. (2009) EEG spectral changes underlying BOLD responses contralateral to spikes in patients with focal epilepsy. Epilepsia 50: 1804-1809.
  105. Bettus G, Ranjeva JP, Wendling F, Bénar CG, Confort-Gouny S, et al. (2011) Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations. PLoS One 6: e20071.
  106. Valton L, Guye M, McGonigal A, Marquis P, Wendling F, et al. (2008) Functional interactions in brain networks underlying epileptic seizures in bilateral diffuse periventricular heterotopia. Clin Neurophysiol 119: 212-223.
  107. Wendling F, Chauvel P, Biraben A, Bartolomei F (2010) From intracerebral EEG signals to brain connectivity: identification of epileptogenic networks in partial epilepsy. Front Syst Neurosci 4: 154.
  108. Murta T, Leal A, Garrido MI, Figueiredo P (2012) Dynamic Causal Modelling of epileptic seizure propagation pathways: a combined EEG-fMRI study. Neuroimage 62: 1634-1642.
  109. Bai X, Guo J, Killory B, Vestal M, Berman R, et al. (2011) Resting functional connectivity between the hemispheres in childhood absence epilepsy. Neurology 76: 1960-1967.
  110. Luo C, Qiu C, Guo Z, Fang J, Li Q, et al. (2011) Disrupted functional brain connectivity in partial epilepsy: a resting-state fMRI study. PLoS One 7: e28196.
  111. Masterton RA, Carney PW, Jackson GD (2012) Cortical and thalamic resting-state functional connectivity is altered in childhood absence epilepsy. Epilepsy Res 99: 327-334.
  112. Wang Z, Zhang Z, Jiao Q, Liao W, Chen G, et al. (2012) Impairments of thalamic nuclei in idiopathic generalized epilepsy revealed by a study combining morphological and functional connectivity MRI. PLoS One 7: e39701.
  113. Luo C, Li Q, Xia Y, Lei X, Xue K, et al. (2012) Resting state basal ganglia network in idiopathic generalized epilepsy. Hum Brain Mapp 33: 1279-1294.
  114. Szaflarski JP, Lindsell CJ, Zakaria T, Banks C, Privitera MD(2010) Seizure control in patients with idiopathic generalized epilepsies: EEG determinants of medication response. Epilepsy Behav 17: 525-530.
  115. Szaflarski JP, Kay B, Gotman J, Privitera MD, Holland SK (2013) The relationship between the localization of the generalized spike and wave discharge generators and the response to valproate. Epilepsia 54: 471-480.
  116. Fahoum F, Lopes R, Pittau F, Dubeau F, Gotman J (2012) Widespread epileptic networks in focal epilepsies: EEG-fMRI study. Epilepsia 53: 1618-1627.
  117. Baxendale S, Heaney D, Thompson PJ, Duncan JS (2010) Cognitive consequences of childhood-onset temporal lobe epilepsy across the adult lifespan. Neurology 75: 705-711.
  118. Jensen FE (2011) Epilepsy as a spectrum disorder: Implications from novel clinical and basic neuroscience. Epilepsia 52 Suppl 1: 1-6.
  119. Binnie CD, de Silva M, Hurst A (1992) Rolandic spikes and cognitive function. Epilepsy Res Suppl 6: 71-73.
  120. Binnie CD, Marston D (1992) Cognitive correlates of interictal discharges. Epilepsia 33 Suppl 6: S11-17.
  121. Chaudhary UJ, Centeno M, Carmichael DW, Vollmar C, Rodionov R, et al. (2012) Imaging the interaction: Epileptic discharges, working memory, and behavior. Hum Brain Mapp .
  122. Killory BD, Bai X, Negishi M, Vega C, Spann MN, et al. (2011) Impaired attention and network connectivity in childhood absence epilepsy. Neuroimage 56: 2209-2217.
  123. Moeller F, Muhle H, Wiegand G, Wolff S, Stephani U, et al. (2010) EEG-fMRI study of generalized spike and wave discharges without transitory cognitive impairment. Epilepsy Behav 18: 313-316.
  124. Lopes R, Lina JM, Fahoum F, Gotman J (2012) Detection of epileptic activity in fMRI without recording the EEG. Neuroimage 60: 1867-1879.
  125. Luders H, Comair YG(2000) Epilepsy surgery. Philadelphia: Lippincott Williams & Wilkins.
  126. Duncan JS (2011) Epilepsy in 2010: Refinement of optimal medical and surgical treatments. Nat Rev Neurol 7: 72-74.
  127. Boucousis SM, Beers CA, Cunningham CJ, Gaxiola-Valdez I, Pittman DJ, et al. (2012) Feasibility of an intracranial EEG-fMRI protocol at 3T: risk assessment and image quality. Neuroimage 63: 1237-1248.
  128. Carmichael DW, Thornton JS, Rodionov R, Thornton R, McEvoy A et al. (2008) Safety of localizing epilepsy monitoring intracranial electroencephalograph electrodes using MRI: radiofrequency-induced heating 17. J Magn Reson Imaging 28: 1233-1244.
  129. Carmichael DW, Thornton JS, Rodionov R, Thornton R, McEvoy AW, et al. (2010) Feasibility of simultaneous intracranial EEG-fMRI in humans: a safety study. Neuroimage 49: 379-390.
  130. Carmichael DW, Vulliemoz S, Rodionov R, Thornton JS, McEvoy AW, et al. (2012) Simultaneous intracranial EEG-fMRI in humans: protocol considerations and data quality. Neuroimage 63: 301-309.
  131. Lüders HO, Najm I, Nair D, Widdess-Walsh P, Bingman W (2006) The epileptogenic zone: general principles. Epileptic Disord 8 Suppl 2: S1-9.
  132. Cunningham CB, Goodyear BG, Badawy R, Zaamout F, Pittman DJ, et al. (2012) Intracranial EEG-fMRI analysis of focal epileptiform discharges in humans. Epilepsia 53: 1636-1648.
  133. Vulliemoz S, Carmichael DW, Rosenkranz K, Diehl B, Rodionov R, et al. (2011) Simultaneous intracranial EEG and fMRI of interictal epileptic discharges in humans. Neuroimage 54: 182-190.
  134. Chaudhary UJ, Kokkinos V, Carmichael DW, Rodionov R, Gasston D, et al. (2010) Implementation and evaluation of simultaneous video-electroencephalography and functional magnetic resonance imaging. Magn Reson Imaging 28: 1192-1199.
  135. Laufs H, Richardson MP, Salek-Haddadi A, Vollmar C, Duncan JS, et al. (2011) Converging PET and fMRI evidence for a common area involved in human focal epilepsies. Neurology 77: 904-910.
  136. Yasuda C, Vollmar C, Centeno M et al. (2012) The effect of topiramate on verbal fluency fMRI: a longitudinal pilot study. Epilepsy Currents 12: 371.
  137. Richardson MP, Grosse P, Allen PJ, Turner R, Brown P (2006) BOLD correlates of EMG spectral density in cortical myoclonus: description of method and case report. Neuroimage 32: 558-565.
  138. Esposito F, Seifritz E, Formisano E, Morrone R, Scarabino T, et al. (2003) Real-time independent component analysis of fMRI time-series. Neuroimage 20: 2209-2224.
  139. Thesen S, Heid O, Mueller E, Schad LR (2000) Prospective acquisition correction for head motion with image-based tracking for real-time fMRI. Magn Reson Med 44: 457-465.
  140. Liou ST, Witzel T, Numenmaa A, Chang WT, Tsai KW, et al. (2011) Functional magnetic resonance inverse imaging of human visuomotor systems using eigenspace linearly constrained minimum amplitude (eLCMA) beamformer. Neuroimage 55: 87-100.
Citation: Graan LAV, Lemieux L, Chaudhary UJ (2013) Scalp and Intracranial EEGfMRI in Epilepsy. J Neurol Neurophysiol 4:156.

Copyright: © 2013 Graan LAV, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.