The combination of EEG Source Imaging and EEG-correlated functional MRI to map epileptic networks

Safety of Intracranial Electroencephalography During Functional Electromagnetic Resonance Imaging in Humans at 1.5 Tesla Using a Head Transmit RF Coil: Histopathological and Heat-Shock Immunohistochemistry Observations

OBJECTIVES

Simultaneous intracranial EEG and functional MRI (icEEG-fMRI) recordings in humans, whereby EEG is recorded from electrodes implanted inside the cranium during fMRI scanning, were made possible following safety studies on test phantoms and our specification of a rigorous data acquisition protocol. In parallel with this work, other investigations in our laboratory revealed the damage caused by the EEG electrode implantation procedure at the cellular level. The purpose of this report is to further explore the safety of performing MRI, including simultaneous icEEG-fMRI data acquisitions, in the presence of implanted intra-cranial EEG electrodes, by presenting some histopathological and heat-shock immunopositive labelling observations in surgical tissue samples from patients who underwent the scanning procedure.

METHODS

We performed histopathology and heat shock protein expression analyses on surgical tissue samples from nine patients who had been implanted with icEEG electrodes. Three patients underwent icEEG-fMRI and structural MRI (sMRI); three underwent sMRI only, all at similar time points after icEEG implantation; and three who did not undergo functional or sMRI with icEEG electrodes.

RESULTS

The histopathological findings from the three patients who underwent icEEG-fMRI were similar to those who did not, in that they showed no evidence of additional damage in the vicinity of the electrodes, compared to cases who had no MRI with implanted icEEG electrodes. This finding was similar to our observations in patients who only underwent sMRI with implanted icEEG electrodes.

CONCLUSION

This work provides unique evidence on the safety of functional MRI in the presence of implanted EEG electrodes. In the cases studied, icEEG-fMRI performed in accordance with our protocol based on low-SAR (≤0.1 W/kg) sequences at 1.5T using a head-transmit RF coil, did not result in measurable additional damage to the brain tissue in the vicinity of implanted electrodes. Furthermore, while one cannot generalize the results of this study beyond the specific electrode implantation and scanning conditions described herein, we submit that our approach is a useful framework for the post-hoc safety assessment of MR scanning with brain implants.

Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors

Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.

Mapping Epileptic Networks Using Simultaneous Intracranial EEG-fMRI

Background: Potentially curative epilepsy surgery can be offered if a single, discrete epileptogenic zone (EZ) can be identified. For individuals in whom there is no clear concordance between clinical localization, scalp EEG, and imaging data, intracranial EEG (icEEG) may be needed to confirm a predefined hypothesis regarding irritative zone (IZ), seizure onset zone (SOZ), and EZ prior to surgery. However, icEEG has limited spatial sampling and may fail to reveal the full extent of epileptogenic network if predefined hypothesis is not correct. Simultaneous icEEG-fMRI has been safely acquired in humans and allows exploration of neuronal activity at the whole-brain level related to interictal epileptiform discharges (IED) captured intracranially.

Methods: We report icEEG-fMRI in eight patients with refractory focal epilepsy who had resective surgery and good postsurgical outcome. Surgical resection volume in seizure-free patients post-surgically reflects confirmed identification of the EZ. IEDs on icEEG were classified according to their topographic distribution and localization (Focal, Regional, Widespread, and Non-contiguous). We also divided IEDs by their location within the surgical resection volume [primary IZ (IZ1) IED] or outside [secondary IZ (IZ2) IED]. The distribution of fMRI blood oxygen level-dependent (BOLD) changes associated with individual IED classes were assessed over the whole brain using a general linear model. The concordance of resulting BOLD map was evaluated by comparing localization of BOLD clusters with surgical resection volume. Additionally, we compared the concordance of BOLD maps and presence of BOLD clusters in remote brain areas: precuneus, cuneus, cingulate, medial frontal, and thalamus for different IED classes.

Results: A total of 38 different topographic IED classes were identified across the 8 patients: Focal (22) and non-focal (16, Regional = 9, Widespread = 2, Non-contiguous = 5). Twenty-nine IEDs originated from IZ1 and 9 from IZ2. All IED classes were associated with BOLD changes. BOLD maps were concordant with the surgical resection volume for 27/38 (71%) IED classes, showing statistical global maximum BOLD cluster or another cluster in the surgical resection volume. The concordance of BOLD maps with surgical resection volume was greater (p < 0.05) for non-focal (87.5%, 14/16) as compared to Focal (59%, 13/22) IED classes. Additionally, BOLD clusters in remote cortical and deep brain areas were present in 84% (32/38) of BOLD maps, more commonly (15/16; 93%) for non-focal IED-related BOLD maps.

Conclusions: Simultaneous icEEG-fMRI can reveal BOLD changes at the whole-brain level for a wide range of IEDs on icEEG. BOLD clusters within surgical resection volume and remote brain areas were more commonly seen for non-focal IED classes, suggesting that a wider hemodynamic network is at play.

Presurgical Evaluation Strategies for Intractable Epilepsy of Childhood

For children who continue to experience seizures despite treatment with antiseizure medications, epilepsy surgery can be considered. The goals of the presurgical evaluation are to determine the best surgical approach to render a good outcome. In patients with drug resistant focal epilepsy, the epileptogenic zone defines the minimal brain volume which must be resected for surgical success and to delineate the relationship of this region with functional cortex. A number of noninvasive tools for these tasks have emerged over the past decade, and existing technologies have been revised and improved. In this review, we examine the recent published evidence for these techniques, specifically as applied to the pediatric population. Discussed herein are the diagnostic value of methods such as video electroencephalography, magnetic resonance imaging, and supportive neuroimaging techniques including single photon emission tomography, photon emission tomography, and magnetoencephalography. Functional testing including functional magnetic resonance imaging, electrical stimulation mapping, and transcranial magnetic stimulation are considered in the context of pediatric epilepsy. The application of emerging techniques to preoperative testing such as source localization, image post-processing, and artificial intelligence is covered. We summarize the relative value of presurgical testing based on patient characteristics, including lesional or nonlesional MRI, temporal or extratemporal epilepsy, and other factors relevant in pediatric epilepsy such as pathological substrate and age.

Preoperative localization of seizure onset zones by magnetic source imaging, EEG-correlated functional MRI, and their combination

OBJECTIVE

Preoperative localization of seizure onset zones (SOZs) is an evolving field in the treatment of refractory epilepsy. Both magnetic source imaging (MSI), and the more recent EEG-correlated functional MRI (EEG-fMRI), have shown applicability in assisting surgical planning. The purpose of this study was to evaluate the capability of each method and their combination in localizing the seizure onset lobe (SL).

METHODS

The study included 14 patients who underwent both MSI and EEG-fMRI before undergoing implantation of intracranial EEG (icEEG) as part of the presurgical planning of the resection of an epileptogenic zone (EZ) during the years 2012-2018. The estimated location of the SL by each method was compared with the location determined by icEEG. Identification rates of the SL were compared between the different methods.

RESULTS

MSI and EEG-fMRI showed similar identification rates of SL locations in relation to icEEG results (88% ± 31% and 73% ± 42%, respectively; p = 0.281). The additive use of the coverage lobes of both methods correctly identified 100% of the SL, significantly higher than EEG-fMRI alone (p = 0.039) and nonsignificantly higher than MSI (p = 0.180). False-identification rates of the additive coverage lobes were significantly higher than MSI (p = 0.026) and EEG-fMRI (p = 0.027). The intersecting lobes of both methods showed the lowest false identification rate (13% ± 6%, p = 0.01).

CONCLUSIONS

Both MSI and EEG-fMRI can assist in the presurgical evaluation of patients with refractory epilepsy. The additive use of both tests confers a high identification rate in finding the SL. This combination can help in focusing implantation of icEEG electrodes targeting the SOZ.

Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG

Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.

Dynamic analysis of fMRI activation during epileptic spikes can help identify the seizure origin

OBJECTIVE

We use the dynamic electroencephalography-functional magnetic resonance imaging (EEG-fMRI) method to incorporate variability in the amplitude and field of the interictal epileptic discharges (IEDs) into the fMRI analysis. We ask whether IED variability analysis can (a) identify additional activated brain regions during the course of IEDs, not seen in standard analysis; and (b) demonstrate the origin and spread of epileptic activity. We explore whether these functional changes recapitulate the structural connections and propagation of epileptic activity during seizures.

METHODS

Seventeen patients with focal epilepsy and at least 30 IEDs of a single type during simultaneous EEG-fMRI were studied. IED variability and EEG source imaging (ESI) analysis extracted time-varying dynamic changes. General linear modeling (GLM) generated static functional maps. Dynamic maps were compared to static functional maps. The dynamic sequence from IED variability was compared to the ESI results. In a subset of patients, we investigated structural connections between active brain regions using diffusion-based fiber tractography.

RESULTS

IED variability distinguished the origin of epileptic activity from its propagation in 15 of 17 (88%) patients. This included two cases where no result was obtained from the standard GLM analysis. In both of these cases, IED variability revealed activation in line with the presumed epileptic focus. Two cases showed no result from either method. Both had very high spike rates associated with dysplasia in the postcentral gyrus. In all 15 cases with dynamic activation, the observed dynamics were concordant with ESI. Fiber tractography identified specific white matter pathways between brain regions that were active at IED onset and propagation.

SIGNIFICANCE

Dynamic techniques involving IED variability can provide additional power for EEG-fMRI analysis, compared to standard analysis, revealing additional biologically plausible information in cases with no result from the standard analysis and gives insight into the origin and spread of IEDs.

Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI

Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks' dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques.

Localizing non-epileptiform abnormal brain function in children using high density EEG: Electric Source Imaging of focal slowing

BACKGROUND

Electric Source Imaging (ESI) of interictal epileptiform discharges (IED) is increasingly validated for localizing epileptic activity. In children, IED can be absent or multifocal even in cases of a focal epileptogenic zone and additional electrophysiological markers are needed. Here, we investigated ESI of pathological focal slowing (FS) recorded on EEG as a new localizing marker in children with drug-resistant epilepsy.

METHODS

We selected 15 children (median: 12; range: 4-18yrs), with high-density EEG (hdEEG), presurgical evaluation and surgical resection. One patient had a non-lesional MRI. ESI of patient-specific focal slow activity was performed (distributed linear inverse solution and individual head model). The maximal average power in the band of interest was considered as the source of focal slowing (ESI-FS). The Euclidian distance between ESI-FS and the resection (5 mm margin) was compared to the localization of maximal ESI of interictal epileptiform discharges (ESI-IED), interictal FDG-PET and ictal SPECT/SISCOM.

RESULTS

In 9/15 patients (60%), ESI of focal slowing (ESI-FS) was inside or ≤5 mm from resection margins. The remaining 6/15 cases had distances ≤15 mm. In 9/15 patients with interictal spikes, the ESI-IED was concordant with the resection. 6/15 patients with concordant ESI-FS showed also interictal concordant ESI of IED; in 3/15 patients, ESI-FS but not ESI-IED was concordant with the resection. In 10/15 patients, ESI-FS was concordant with MRI lesion and for ESI-IED this concordance was on 8/15 patients. Maximal hypometabolism and SISCOM were concordant with the resection for 7/15 and 7/12, respectively.

CONCLUSION

These findings suggest that "non-epileptiform" EEG activity, such as focal slowing, could be a complementary useful marker to localize the epileptogenic zone. ESI-FS may notably be applied in young patients without focal interictal spikes or multifocal spikes. This potential new marker of brain dysfunction has potential applications to other neurological disorders associated with slow EEG activity.

EEG synchronization measures predict epilepsy-related BOLD-fMRI fluctuations better than commonly used univariate metrics

OBJECTIVE

We hypothesize that the hypersynchronization associated with epileptic activity is best described by EEG synchronization measures, and propose to use these as predictors of epilepsy-related BOLD fluctuations.

METHODS

We computed the phase synchronization index (PSI) and global field synchronization (GFS), within two frequency bands, a broadband (1-45 Hz) and a narrower band focused on the presence of epileptic activity (3-10 Hz). The associated epileptic networks were compared with those obtained using conventional unitary regressors and two power-weighted metrics (total power and root mean square frequency), on nine simultaneous EEG-fMRI datasets from four epilepsy patients, exhibiting inter-ictal epileptiform discharges (IEDs).

RESULTS

The average PSI within 3-10 Hz achieved the best performance across several measures reflecting reliability in all datasets. The results were cross-validated through electrical source imaging of the IEDs. The applicability of PSI when no IEDs are recorded on the EEG was evaluated on three additional patients, yielding partially plausible networks in all cases.

CONCLUSIONS

Epileptic networks can be mapped based on the EEG PSI metric within an IED-specific frequency band, performing better than commonly used EEG metrics.

SIGNIFICANCE

This is the first study to investigate EEG synchronization measures as potential predictors of epilepsy-related BOLD fluctuations.

A Simulation Framework for Benchmarking EEG-Based Brain Connectivity Estimation Methodologies

Due to its high temporal resolution, electroencephalography (EEG) is widely used to study functional and effective brain connectivity. Yet, there is currently a mismatch between the vastness of studies conducted and the degree to which the employed analyses are theoretically understood and empirically validated. We here provide a simulation framework that enables researchers to test their analysis pipelines on realistic pseudo-EEG data. We construct a minimal example of brain interaction, which we propose as a benchmark for assessing a methodology's general eligibility for EEG-based connectivity estimation. We envision that this benchmark be extended in a collaborative effort to validate methods in more complex scenarios. Quantitative metrics are defined to assess a method's performance in terms of source localization, connectivity detection and directionality estimation. All data and code needed for generating pseudo-EEG data, conducting source reconstruction and connectivity estimation using baseline methods from the literature, evaluating performance metrics, as well as plotting results, are made publicly available. While this article covers only EEG modeling, we will also provide a magnetoencephalography version of our framework online.

Epileptic network of hypothalamic hamartoma: An EEG-fMRI study

OBJECTIVE

To investigate the brain networks involved in epileptogenesis/encephalopathy associated with hypothalamic hamartoma (HH) by EEG with functional MRI (EEG-fMRI), and evaluate its efficacy in locating the HH interface in comparison with subtraction ictal SPECT coregistered to MRI (SISCOM).

METHODS

Eight HH patients underwent EEG-fMRI. All had gelastic seizures (GS) and 7 developed other seizure types. Using a general linear model, spike-related activation/deactivation was analyzed individually by applying a hemodynamic response function before, at, and after spike onset (time-shift model=-8-+4s). Group analysis was also performed. The sensitivity of EEG-fMRI in identifying the HH interface was compared with SISCOM in HH patients having unilateral hypothalamic attachment.

RESULTS

EEG-fMRI revealed activation and/or deactivation in subcortical structures and neocortices in all patients. 6/8 patients showed activation in or around the hypothalamus with the HH interface with time-shift model before spike onset. Group analysis showed common activation in the ipsilateral hypothalamus, brainstem tegmentum, and contralateral cerebellum. Deactivation occurred in the default mode network (DMN) and bilateral hippocampi. Among 5 patients with unilateral hypothalamic attachment, activation in or around the ipsilateral hypothalamus was seen in 3 using EEG-fMRI, whereas hyperperfusion was seen in 1 by SISCOM.

SIGNIFICANCE

Group analysis of this preliminary study may suggest that the commonly activated subcortical network is related to generation of GS and that frequent spikes lead to deactivation of the DMN and hippocampi, and eventually to a form of epileptic encephalopathy. Inter-individual variance in neocortex activation explains various seizure types among patients. EEG-fMRI enhances sensitivity in detecting the HH interface compared with SISCOM.

Concordance of Epileptic Networks Associated with Epileptic Spikes Measured by High-Density EEG and Fast fMRI

Objective

The present study aims to investigate whether a newly developed fast fMRI called MREG (magnetic resonance encephalography) measures metabolic changes related to interictal epileptic discharges (IED). For this purpose BOLD changes are correlated with the IED distribution and variability.

Methods

Patients with focal epilepsy underwent EEG-MREG using a 64 channel cap. IED voltage maps were generated using 32 and 64 channels and compared regarding their correspondence to the BOLD response. The extents of IEDs (defined as number of channels with >50% of maximum IED negativity) were correlated with the extents of positive and negative BOLD responses. Differences in inter-spike variability were investigated between interictal epileptic discharges (IED) sets with and without concordant positive or negative BOLD responses.

Results

17 patients showed 32 separate IED types. In 50% of IED types the BOLD changes could be confirmed by another independent imaging method. The IED extent significantly correlated with the positive BOLD extent (p = 0.04). In 6 patients the 64-channel EEG voltage maps better reflected the positive or negative BOLD response than the 32-channel EEG; in all others no difference was seen. Inter-spike variability was significantly lower in IED sets with than without concordant positive or negative BOLD responses (with p = 0.04).

Significance

Higher density EEG and fast fMRI seem to improve the value of EEG-fMRI in epilepsy. The correlation of positive BOLD and IED extent could suggest that widespread BOLD responses reflect the IED network. Inter-spike variability influences the likelihood to find IED concordant positive or negative BOLD responses, which is why single IED analysis may be promising.

Characterisation and imaging of cortical impedance changes during interictal and ictal activity in the anaesthetised rat

Epilepsy affects approximately 50 million people worldwide, and 20–30% of these cases are refractory to antiepileptic drugs. Many patients with intractable epilepsy can benefit from surgical resection of the tissue generating the seizures; however, difficulty in precisely localising seizure foci has limited the number of patients undergoing surgery as well as potentially lowered its effectiveness. Here we demonstrate a novel imaging method for monitoring rapid changes in cerebral tissue impedance occurring during interictal and ictal activity, and show that it can reveal the propagation of pathological activity in the cortex. Cortical impedance was recorded simultaneously to ECoG using a 30-contact electrode mat placed on the exposed cortex of anaesthetised rats, in which interictal spikes (IISs) and seizures were induced by cortical injection of 4-aminopyridine (4-AP), picrotoxin or penicillin. We characterised the tissue impedance responses during IISs and seizures, and imaged these responses in the cortex using Electrical Impedance Tomography (EIT). We found a fast, transient drop in impedance occurring as early as 12 ms prior to the IISs, followed by a steep rise in impedance within ~ 120 ms of the IIS. EIT images of these impedance changes showed that they were co-localised and centred at a depth of 1 mm in the cortex, and that they closely followed the activity propagation observed in the surface ECoG signals. The fast, pre-IIS impedance drop most likely reflects synchronised depolarisation in a localised network of neurons, and the post-IIS impedance increase reflects the subsequent shrinkage of extracellular space caused by the intense activity. EIT could also be used to picture a steady rise in tissue impedance during seizure activity, which has been previously described. Thus, our results demonstrate that EIT can detect and localise different physiological changes during interictal and ictal activity and, in conjunction with ECoG, may in future improve the localisation of seizure foci in the clinical setting.

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Cortical impedance was measured simultaneously to EEG during epileptic activity.

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Interictal spikes were preceded by fast decreases in cortical impedance.

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Steep increases in impedance followed both interictal spikes and onset of ictal activity.

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Imaging with Electrical Impedance Tomography revealed the location of epilepsy-related impedance changes.

Incorporating priors for EEG source imaging and connectivity analysis

Electroencephalography source imaging (ESI) is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET). The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, EEG-correlated fMRI, temporally coherent networks (TCNs) and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI) and neuro-stimulation techniques (transcranial magnetic stimulation, TMS) are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach toward the study of brain networks in cognitive and clinical neurosciences.

Methods and utility of EEG-fMRI in epilepsy

Brain activity data in general and more specifically in epilepsy can be represented as a matrix that includes measures of electrophysiology, anatomy and behaviour. Each of these sub-matrices has a complex interaction depending upon the brain state i.e., rest, cognition, seizures and interictal periods. This interaction presents significant challenges for interpretation but also potential for developing further insights into individual event types. Successful treatments in epilepsy hinge on unravelling these complexities, and also on the sensitivity and specificity of methods that characterize the nature and localization of underlying physiological and pathological networks. Limitations of pharmacological and surgical treatments call for refinement and elaboration of methods to improve our capability to localise the generators of seizure activity and our understanding of the neurobiology of epilepsy. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI), by potentially circumventing some of the limitations of EEG in terms of sensitivity, can allow the mapping of haemodynamic networks over the entire brain related to specific spontaneous and triggered epileptic events in humans, and thereby provide new localising information. In this work we review the published literature, and discuss the methods and utility of EEG-fMRI in localising the generators of epileptic activity. We draw on our experience and that of other groups, to summarise the spectrum of information provided by an increasing number of EEG-fMRI case-series, case studies and group studies in patients with epilepsy, for its potential role to elucidate epileptic generators and networks. We conclude that EEG-fMRI provides a multidimensional view that contributes valuable clinical information to localize the epileptic focus with potential important implications for the surgical treatment of some patients with drug-resistant epilepsy, and insights into the resting state and cognitive network dynamics.

Reliability of information-based integration of EEG and fMRI data: a simulation study

Most studies involving simultaneous electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data rely on the first-order, affine-linear correlation of EEG and fMRI features within the framework of the general linear model. An alternative is the use of information-based measures such as mutual information and entropy, which can also detect higher-order correlations present in the data. The estimate of information-theoretic quantities might be influenced by several parameters, such as the numerosity of the sample, the amount of correlation between variables, and the discretization (or binning) strategy of choice. While these issues have been investigated for invasive neurophysiological data and a number of bias-correction estimates have been developed, there has been no attempt to systematically examine the accuracy of information estimates for the multivariate distributions arising in the context of EEG-fMRI recordings. This is especially important given the differences between electrophysiological and EEG-fMRI recordings. In this study, we drew random samples from simulated bivariate and trivariate distributions, mimicking the statistical properties of EEG-fMRI data. We compared the estimated information shared by simulated random variables with its numerical value and found that the interaction between the binning strategy and the estimation method influences the accuracy of the estimate. Conditional on the simulation assumptions, we found that the equipopulated binning strategy yields the best and most consistent results across distributions and bias correction methods. We also found that within bias correction techniques, the asymptotically debiased (TPMC), the jackknife debiased (JD), and the best upper bound (BUB) approach give similar results, and those are consistent across distributions.

Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective

A variety of exciting scientific achievements have been made in the last few decades in brain encoding and decoding via functional magnetic resonance imaging (fMRI). This trend continues to rise in recent years, as evidenced by the increasing number of published papers in this topic and several published survey papers addressing different aspects of research issues. Essentially, these survey articles were mainly from cognitive neuroscience and neuroimaging perspectives, although computational challenges were briefly discussed. To complement existing survey articles, this paper focuses on the survey of the variety of image analysis methodologies, such as neuroimage registration, fMRI signal analysis, ROI (regions of interest) selection, machine learning algorithms, reproducibility analysis, structural and functional connectivity, and natural image analysis, which were employed in previous brain encoding/decoding research works. This paper also provides discussions of potential limitations of those image analysis methodologies and possible future improvements. It is hoped that extensive discussions of image analysis issues could contribute to the advancements of the increasingly important brain encoding/decoding field.

Characterisation and reduction of the EEG artefact caused by the helium cooling pump in the MR environment: validation in epilepsy patient data

The EEG acquired simultaneously with fMRI is distorted by a number of artefacts related to the presence of strong magnetic fields, which must be reduced in order to allow for a useful interpretation and quantification of the EEG data. For the two most prominent artefacts, associated with magnetic field gradient switching and the heart beat, reduction methods have been developed and applied successfully. However, a number of artefacts related to the MR-environment can be found to distort the EEG data acquired even without ongoing fMRI acquisition. In this paper, we investigate the most prominent of those artefacts, caused by the Helium cooling pump, and propose a method for its reduction and respective validation in data collected from epilepsy patients. Since the Helium cooling pump artefact was found to be repetitive, an average template subtraction method was developed for its reduction with appropriate adjustments for minimizing the degradation of the physiological part of the signal. The new methodology was validated in a group of 15 EEG-fMRI datasets collected from six consecutive epilepsy patients, where it successfully reduced the amplitude of the artefact spectral peaks by 95 ± 2 % while the background spectral amplitude within those peaks was reduced by only -5 ± 4 %. Although the Helium cooling pump should ideally be switched off during simultaneous EEG-fMRI acquisitions, we have shown here that in cases where this is not possible the associated artefact can be effectively reduced in post processing.

Pulse artifact detection in simultaneous EEG-fMRI recording based on EEG map topography

One of the major artifact corrupting electroencephalogram (EEG) acquired during functional magnetic resonance imaging (fMRI) is the pulse artifact (PA). It is mainly due to the motion of the head and attached electrodes and wires in the magnetic field occurring after each heartbeat. In this study we propose a novel method to improve PA detection by considering the strong gradient and inversed polarity between left and right EEG electrodes. We acquired high-density EEG-fMRI (256 electrodes) with simultaneous electrocardiogram (ECG) at 3 T. PA was estimated as the voltage difference between right and left signals from the electrodes showing the strongest artifact (facial and temporal). Peaks were detected on this estimated signal and compared to the peaks in the ECG recording. We analyzed data from eleven healthy subjects, two epileptic patients and four healthy subjects with an insulating layer between electrodes and scalp. The accuracy of the two methods was assessed with three criteria: (i) standard deviation, (ii) kurtosis and (iii) confinement into the physiological range of the inter-peak intervals. We also checked whether the new method has an influence on the identification of epileptic spikes. Results show that estimated PA improved artifact detection in 15/17 cases, when compared to the ECG method. Moreover, epileptic spike identification was not altered by the correction. The proposed method improves the detection of pulse-related artifacts, particularly crucial when the ECG is of poor quality or cannot be recorded. It will contribute to enhance the quality of the EEG increasing the reliability of EEG-informed fMRI analysis.

Altered FMRI connectivity dynamics in temporal lobe epilepsy might explain seizure semiology

Temporal lobe epilepsy (TLE) can be conceptualized as a network disease. The network can be characterized by inter-regional functional connectivity, i.e., blood oxygen level-dependent (BOLD) signal correlations between any two regions. However, functional connectivity is not constant over time, thus computing correlation at a given time and then at some later time could give different results (non-stationarity). We hypothesized (1) that non-stationarities can be induced by epilepsy (e.g., interictal epileptic activity) increasing local signal variance and that (2) these transient events contribute to fluctuations in connectivity leading to pathological functioning, i.e., TLE semiology. We analyzed fMRI data from 27 patients with TLE and 22 healthy controls focusing on EEG-confirmed wake epochs only to protect against sleep-induced connectivity changes. Testing hypothesis (1), we identified brain regions where the BOLD signal variance was significantly greater in TLE than in controls: the temporal pole – including the hippocampus. Taking the latter as the seed region and testing hypothesis (2), we calculated the time-varying inter-regional correlation values (dynamic functional connectivity) to other brain regions and found greater connectivity variance in the TLE than the control group mainly in the precuneus, the supplementary and sensorimotor, and the frontal cortices. We conclude that the highest BOLD signal variance in the hippocampi is highly suggestive of a specific epilepsy-related effect. The altered connectivity dynamics in TLE patients might help to explain the hallmark semiological features of dyscognitive seizures including impaired consciousness (precuneus, frontal cortex), sensory disturbance, and motor automatisms (sensorimotor cortices, supplementary motor cortex). Accounting for the non-stationarity and state-dependence of functional connectivity are a prerequisite in the search for potential connectivity-derived biomarkers in TLE.

Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions

There is a growing body of evidence pointing toward large-scale networks underlying the core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response to treatment. The investigation of networks in epilepsy has become a key concept to unlock a deeper understanding of the disease. Functional imaging can provide valuable information to characterize network dysfunction; in particular resting state fMRI (RS-fMRI), which is increasingly being applied to study brain networks in a number of diseases. In patients with epilepsy, network connectivity derived from RS-fMRI has found connectivity abnormalities in a number of networks; these include the epileptogenic, cognitive and sensory processing networks. However, in majority of these studies, the effect of epileptic transients in the connectivity of networks has been neglected. EEG–fMRI has frequently shown networks related to epileptic transients that in many cases are concordant with the abnormalities shown in RS studies. This points toward a relevant role of epileptic transients in the network abnormalities detected in RS-fMRI studies. In this review, we summarize the network abnormalities reported by these two techniques side by side, provide evidence of their overlapping findings, and discuss their significance in the context of the methodology of each technique. A number of clinically relevant factors that have been associated with connectivity changes are in turn associated with changes in the frequency of epileptic transients. These factors include different aspects of epilepsy ranging from treatment effects, cognitive processes, or transition between different alertness states (i.e., awake–sleep transition). For RS-fMRI to become a more effective tool to investigate clinically relevant aspects of epilepsy it is necessary to understand connectivity changes associated with epileptic transients, those associated with other clinically relevant factors and the interaction between them, which represents a gap in the current literature. We propose a framework for the investigation of network connectivity in patients with epilepsy that can integrate epileptic processes that occur across different time scales such as epileptic transients and disease duration and the implications of this approach are discussed.

The role of functional neuroimaging in pre-surgical epilepsy evaluation

The prevalence of epilepsy is about 1% and one-third of cases do not respond to medical treatment. In an eligible subset of patients with drug-resistant epilepsy, surgical resection of the epileptogenic zone is the only treatment that can possibly cure the disease. Non-invasive techniques provide information for the localization of the epileptic focus in the majority of cases, whereas in others invasive procedures are required. In the last years, non-invasive neuroimaging techniques, such as simultaneous recording of functional magnetic resonance imaging and electroencephalogram (EEG-fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), electric and magnetic source imaging (MSI, ESI), spectroscopy (MRS), have proved their usefulness in defining the epileptic focus. The combination of these functional techniques can yield complementary information and their concordance is crucial for guiding clinical decision, namely the planning of invasive EEG recordings or respective surgery. The aim of this review is to present these non-invasive neuroimaging techniques, their potential combination, and their role in the pre-surgical evaluation of patients with pharmaco-resistant epilepsy.

The role of functional neuroimaging in pre-surgical epilepsy evaluation

The prevalence of epilepsy is about 1% and one-third of cases do not respond to medical treatment. In an eligible subset of patients with drug-resistant epilepsy, surgical resection of the epileptogenic zone is the only treatment that can possibly cure the disease. Non-invasive techniques provide information for the localization of the epileptic focus in the majority of cases, whereas in others invasive procedures are required. In the last years, non-invasive neuroimaging techniques, such as simultaneous recording of functional magnetic resonance imaging and electroencephalogram (EEG-fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), electric and magnetic source imaging (MSI, ESI), spectroscopy (MRS), have proved their usefulness in defining the epileptic focus. The combination of these functional techniques can yield complementary information and their concordance is crucial for guiding clinical decision, namely the planning of invasive EEG recordings or respective surgery. The aim of this review is to present these non-invasive neuroimaging techniques, their potential combination, and their role in the pre-surgical evaluation of patients with pharmaco-resistant epilepsy.

Localisation of epileptic foci using novel imaging modalities

Purpose of review

This review examines recent reports on the use of advanced techniques to map the regions and networks involved during focal epileptic seizure generation in humans.

Recent findings

A number of imaging techniques are capable of providing new localizing information on the ictal processes and epileptogenic zone. Evaluating the clinical utility of these findings has been mainly performed through post-hoc comparison with the findings of invasive EEG and ictal single-photon emission computed tomography, using postsurgical seizure reduction as the main outcome measure. Added value has been demonstrated in MRI-negative cases. Improved understanding of the human ictiogenic processes and the focus vs. network hypothesis is likely to result from the application of multimodal techniques that combine electrophysiological, semiological, and whole-brain coverage of brain activity changes.

Summary

On the basis of recent research in the field of neuroimaging, several novel imaging modalities have been improved and developed to provide information about the localization of epileptic foci.

EEG and MEG: relevance to neuroscience

To understand dynamic cognitive processes, the high time resolution of EEG/MEG is invaluable. EEG/MEG signals can play an important role in providing measures of functional and effective connectivity in the brain. After a brief description of the foundations and basic methodological aspects of EEG/MEG signals, the relevance of the signals to obtain novel insights into the neuronal mechanisms underlying cognitive processes is surveyed, with emphasis on neuronal oscillations (ultra-slow, theta, alpha, beta, gamma, and HFOs) and combinations of oscillations. Three main functional roles of brain oscillations are put in evidence: (1) coding specific information, (2) setting and modulating brain attentional states, and (3) assuring the communication between neuronal populations such that specific dynamic workspaces may be created. The latter form the material core of cognitive functions.

Electric source imaging of interictal activity accurately localises the seizure onset zone

OBJECTIVE

It remains controversial whether interictal spikes are a surrogate of the seizure onset zone (SOZ). Electric source imaging (ESI) is an increasingly validated non-invasive approach for localising the epileptogenic focus in patients with drug-resistant epilepsy undergoing evaluation for surgery, using high-density scalp EEG and advanced source localisation algorithms that include the patient's own MRI. Here we investigate whether localisation of interictal spikes by ESI provides valuable information on the SOZ.

METHODS

In 38 patients with focal epilepsy who later underwent intracranial EEG monitoring, we performed ESI of interictal spikes recorded with 128-256-channel EEG. We measured the distance between the ESI maximum and the nearest intracranial electrodes in the SOZ and irritative zone (IZ, the source of interictal spikes). The resection of the region harbouring the ESI maximum was correlated to surgical outcome.

RESULTS

The median distance from the ESI maximum to the nearest electrode involved in the SOZ was 17 mm (IQR 8-27). The IZ and SOZ colocalised in most patients (median distance 0 mm, IQR 0-14), supporting the notion that localising interictal spikes is a valid surrogate for the SOZ. There was no difference in accuracy among patients with temporal or extratemporal epilepsy. In the 32 patients who underwent resective surgery, including the ESI maximum in the resection correlated with favourable outcome (p=0.03).

CONCLUSIONS

Localisation of interictal spikes provides an excellent estimate of the SOZ in the majority of patients. ESI should be taken into account for the management of patients undergoing intracranial recordings.

FACET - a "Flexible Artifact Correction and Evaluation Toolbox" for concurrently recorded EEG/fMRI data

Background

In concurrent EEG/fMRI recordings, EEG data are impaired by the fMRI gradient artifacts which exceed the EEG signal by several orders of magnitude. While several algorithms exist to correct the EEG data, these algorithms lack the flexibility to either leave out or add new steps. The here presented open-source MATLAB toolbox FACET is a modular toolbox for the fast and flexible correction and evaluation of imaging artifacts from concurrently recorded EEG datasets. It consists of an Analysis, a Correction and an Evaluation framework allowing the user to choose from different artifact correction methods with various pre- and post-processing steps to form flexible combinations. The quality of the chosen correction approach can then be evaluated and compared to different settings.

Results

FACET was evaluated on a dataset provided with the FMRIB plugin for EEGLAB using two different correction approaches: Averaged Artifact Subtraction (AAS, Allen et al., NeuroImage 12(2):230–239, 2000) and the FMRI Artifact Slice Template Removal (FASTR, Niazy et al., NeuroImage 28(3):720–737, 2005). Evaluation of the obtained results were compared to the FASTR algorithm implemented in the EEGLAB plugin FMRIB. No differences were found between the FACET implementation of FASTR and the original algorithm across all gradient artifact relevant performance indices.

Conclusion

The FACET toolbox not only provides facilities for all three modalities: data analysis, artifact correction as well as evaluation and documentation of the results but it also offers an easily extendable framework for development and evaluation of new approaches.

Fast fMRI provides high statistical power in the analysis of epileptic networks

EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5-3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals. Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography. 13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation. Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution.

Epileptic neuronal networks: methods of identification and clinical relevance

The main objective of this paper is to examine evidence for the concept that epileptic activity should be envisaged in terms of functional connectivity and dynamics of neuronal networks. Basic concepts regarding structure and dynamics of neuronal networks are briefly described. Particular attention is given to approaches that are derived, or related, to the concept of causality, as formulated by Granger. Linear and non-linear methodologies aiming at characterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed. The relevance of functional dynamical analysis of neuronal networks with respect to clinical queries in focal cortical dysplasias, temporal lobe epilepsies, and "generalized" epilepsies is emphasized. In the light of the concepts of epileptic neuronal networks, and recent experimental findings, the dichotomic classification in focal and generalized epilepsy is re-evaluated. It is proposed that so-called "generalized epilepsies," such as absence seizures, are actually fast spreading epilepsies, the onset of which can be tracked down to particular neuronal networks using appropriate network analysis. Finally new approaches to delineate epileptogenic networks are discussed.

Paradigm free mapping with sparse regression automatically detects single-trial functional magnetic resonance imaging blood oxygenation level dependent responses

The ability to detect single trial responses in functional magnetic resonance imaging (fMRI) studies is essential, particularly if investigating learning or adaptation processes or unpredictable events. We recently introduced paradigm free mapping (PFM), an analysis method that detects single trial blood oxygenation level dependent (BOLD) responses without specifying prior information on the timing of the events. PFM is based on the deconvolution of the fMRI signal using a linear hemodynamic convolution model. Our previous PFM method (Caballero-Gaudes et al., 2011: Hum Brain Mapp) used the ridge regression estimator for signal deconvolution and required a baseline signal period for statistical inference. In this work, we investigate the application of sparse regression techniques in PFM. In particular, a novel PFM approach is developed using the Dantzig selector estimator, solved via an efficient homotopy procedure, along with statistical model selection criteria. Simulation results demonstrated that, using the Bayesian information criterion to select the regularization parameter, this method obtains high detection rates of the BOLD responses, comparable with a model-based analysis, but requiring no information on the timing of the events and being robust against hemodynamic response function variability. The practical operation of this sparse PFM method was assessed with single-trial fMRI data acquired at 7T, where it automatically detected all task-related events, and was an improvement on our previous PFM method, as it does not require the definition of a baseline state and amplitude thresholding and does not compromise on specificity and sensitivity.

EEG-fMRI correlation patterns in the presurgical evaluation of focal epilepsy: a comparison with electrocorticographic data and surgical outcome measures

EEG-correlated functional MRI (EEG-fMRI) visualizes brain regions associated with interictal epileptiform discharges (IEDs). This technique images the epileptiform network, including multifocal, superficial and deeply situated cortical areas. To understand the role of EEG-fMRI in presurgical evaluation, its results should be validated relative to a gold standard. For that purpose, EEG-fMRI data were acquired for a heterogeneous group of surgical candidates (n=16) who were later implanted with subdural grids and strips (ECoG). The EEG-fMRI correlation patterns were systematically compared with brain areas involved in IEDs ECoG, using a semi-automatic analysis method, as well as to the seizure onset zone, resected area, and degree of seizure freedom. In each patient at least one of the EEG-fMRI areas was concordant with an interictally active ECoG area, always including the early onset area of IEDs in the ECoG data. This confirms that EEG-fMRI reflects a pattern of onset and propagation of epileptic activity. At group level, 76% of the BOLD regions that were covered with subdural grids, were concordant with interictally active ECoG electrodes. Due to limited spatial sampling, 51% of the BOLD regions were not covered with electrodes and could, therefore, not be validated. From an ECoG perspective it appeared that 29% of the interictally active ECoG regions were missed by EEG-fMRI and that 68% of the brain regions were correctly identified as inactive with EEG-fMRI. Furthermore, EEG-fMRI areas included the complete seizure onset zone in 83% and resected area in 93% of the data sets. No clear distinction was found between patients with a good or poor surgical outcome: in both patient groups, EEG-fMRI correlation patterns were found that were either focal or widespread. In conclusion, by comparison of EEG-fMRI with interictal invasive EEG over a relatively large patient population we were able to show that the EEG-fMRI correlation patterns are spatially accurate at the level of neurosurgical units (i.e. anatomical brain regions) and reflect the underlying network of IEDs. Therefore, we expect that EEG-fMRI can play an important role for the determination of the implantation strategy.

Mapping interictal epileptic discharges using mutual information between concurrent EEG and fMRI

OBJECTIVE

The mapping of haemodynamic changes related to interictal epileptic discharges (IED) in simultaneous electroencephalography (EEG) and functional MRI (fMRI) studies is usually carried out by means of EEG-correlated fMRI analyses where the EEG information specifies the model to test on the fMRI signal. The sensitivity and specificity critically depend on the accuracy of EEG detection and the validity of the haemodynamic model. In this study we investigated whether an information theoretic analysis based on the mutual information (MI) between the presence of epileptic activity on EEG and the fMRI data can provide further insights into the haemodynamic changes related to interictal epileptic activity. The important features of MI are that: 1) both recording modalities are treated symmetrically; 2) no requirement for a-priori models for the haemodynamic response function, or assumption of a linear relationship between the spiking activity and BOLD responses, and 3) no parametric model for the type of noise or its probability distribution is necessary for the computation of MI.

METHODS

Fourteen patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI and intracranial EEG and/or surgical resection with positive postoperative outcome (seizure freedom or considerable reduction in seizure frequency) was available in 7/14 patients. We used nonparametric statistical assessment of the MI maps based on a four-dimensional wavelet packet resampling method. The results of MI were compared to the statistical parametric maps obtained with two conventional General Linear Model (GLM) analyses based on the informed basis set (canonical HRF and its temporal and dispersion derivatives) and the Finite Impulse Response (FIR) models.

RESULTS

The MI results were concordant with the electro-clinically or surgically defined epileptogenic area in 8/14 patients and showed the same degree of concordance as the results obtained with the GLM-based methods in 12 patients (7 concordant and 5 discordant). In one patient, the information theoretic analysis improved the delineation of the irritative zone compared with the GLM-based methods.

DISCUSSION

Our findings suggest that an information theoretic analysis can provide clinically relevant information about the BOLD signal changes associated with the generation and propagation of interictal epileptic discharges. The concordance between the MI, GLM and FIR maps support the validity of the assumptions adopted in GLM-based analyses of interictal epileptic activity with EEG-fMRI in such a manner that they do not significantly constrain the localization of the epileptogenic zone.

EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.

Functional imaging of seizures and epilepsy: evolution from zones to networks

PURPOSE OF REVIEW

Epilepsy research has extended from studies at the cellular level to the investigation of interactions of large neuronal populations distant from one another: 'epileptic networks'. This article underlines the concept of epilepsies as network disorders, adding empirical evidence from electroencephalography-combined functional MRI (EEG-fMRI) studies.

RECENT FINDINGS

These noninvasive in-vivo EEG-fMRI epilepsy studies have characterized the ictal temporal-spatial evolution and the interictal persistence of altered activity in typical sets of (sub)cortical brain regions responsible for the clinical manifestation of the disease and its underlying encephalopathy, for example, thalamus vs. cortex in generalized; hippocampus vs. cortex in temporal lobe; a frontal near-piriform region universally in focal epilepsies. Models exist validated against intracranial EEG that can explain interictal and ictal activity based on statistical coupling between different brain regions, and if extended could guide the design of new treatments.

SUMMARY

The appreciation of epileptic processes at the network level will foster the development of both anticonvulsive as well as true antiepileptic treatment strategies locally modulating hub regions within the epileptic network architecture as well as entire networks by targeting their characteristic properties such as neurotransmitter or neuronal firing profiles. Treatment should reach beyond seizure control and include the improvement of cognitive function.

Functional MRI applications in epilepsy surgery

Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that has grown rapidly in popularity over the past decade. It is already prevalent in psychology, cognitive and basic neuroscience research and is being used increasingly as a tool for clinical decision-making in epilepsy. It has been used to determine language location and laterality in patients, sometimes eliminating the need for invasive tests. fMRI can been used pre-surgically to guide resection margins, preserving eloquent cortex. Other fMRI paradigms assessing memory, visual and somatosensory systems have limited clinical applications currently, but show great promise. Simultaneous recording of electroencephalogram (EEG) and fMRI has also provided insights into the networks underlying seizure generation and is increasingly being used in epilepsy centres. In this review, we present some of the current clinical applications for fMRI in the pre-surgical assessment of epilepsy patients, and examine a number of new techniques that may soon become clinically relevant.

A multimodal imaging approach to the evaluation of post-traumatic epilepsy

Object

Electroencephalography-functional magnetic resonance imaging (EEG-fMRI) coregistration and high-density EEG (hdEEG) can be combined to map noninvasively abnormal brain activation elicited by epileptic processes. By combining noninvasive imaging techniques in a multimodal approach, we sought to investigate pathophysiological mechanisms underlying epileptic activity in seven patients with severe traumatic brain injury.

Materials and methods

Standard EEG and fMRI data were acquired during a single scanning session. The EEG-fMRI data were analyzed using the general linear model and independent component analysis. Source localization of interictal epileptiform discharges (IEDs) was performed using 256-channel hdEEG. Blood oxygenation level dependent (BOLD) localizations were then compared to EEG source reconstruction.

Results

On hdEEG, focal source localization was detected in all seven patients; in six out of seven it was concordant with the expected epileptic activity as defined by EEG data and clinical evaluation; and in four out of seven in whom IEDs were recorded, BOLD signal changes were observed. These activities were partially concordant with the source localization.

Conclusion

Multimodal integration of EEG-fMRI and hdEEG combining two different methods to localize the same epileptic foci appears to be a promising tool to noninvasively map abnormal brain activation in patients with post-traumatic brain injury.

Noninvasive approach to focal cortical dysplasias: clinical, EEG, and neuroimaging features

Purpose. The main purpose is to define more accurately the epileptogenic zone (EZ) with noninvasive methods in those patients with MRI diagnosis of focal cortical dysplasia (FCD) and epilepsy who are candidates of epilepsy surgery. Methods. Twenty patients were evaluated prospectively between 2007 and 2010 with comprehensive clinical evaluation, video-electroencephalography, diffusion tensor imaging (DTI), and high-resolution EEG to localize the equivalent current dipole (ECD). Key Findings. In 11 cases with white matter asymmetries in DTI the ECDs were located next to lesion on MRI with mean distance of 14.63 millimeters with topographical correlation with the EZ. Significance. We could establish a hypothesis of EZ based on Video-EEG, high-resolution EEG, ECD method, MRI, and DTI. These results are consistent with the hypothesis that the EZ in the FCD is complex and is often larger than visible lesion in MRI.

With or without spikes: localization of focal epileptic activity by simultaneous electroencephalography and functional magnetic resonance imaging

In patients with medically refractory focal epilepsy who are candidates for epilepsy surgery, concordant non-invasive neuroimaging data are useful to guide invasive electroencephalographic recordings or surgical resection. Simultaneous electroencephalography and functional magnetic resonance imaging recordings can reveal regions of haemodynamic fluctuations related to epileptic activity and help localize its generators. However, many of these studies (40-70%) remain inconclusive, principally due to the absence of interictal epileptiform discharges during simultaneous recordings, or lack of haemodynamic changes correlated to interictal epileptiform discharges. We investigated whether the presence of epilepsy-specific voltage maps on scalp electroencephalography correlated with haemodynamic changes and could help localize the epileptic focus. In 23 patients with focal epilepsy, we built epilepsy-specific electroencephalographic voltage maps using averaged interictal epileptiform discharges recorded during long-term clinical monitoring outside the scanner and computed the correlation of this map with the electroencephalographic recordings in the scanner for each time frame. The time course of this correlation coefficient was used as a regressor for functional magnetic resonance imaging analysis to map haemodynamic changes related to these epilepsy-specific maps (topography-related haemodynamic changes). The method was first validated in five patients with significant haemodynamic changes correlated to interictal epileptiform discharges on conventional analysis. We then applied the method to 18 patients who had inconclusive simultaneous electroencephalography and functional magnetic resonance imaging studies due to the absence of interictal epileptiform discharges or absence of significant correlated haemodynamic changes. The concordance of the results with subsequent intracranial electroencephalography and/or resection area in patients who were seizure free after surgery was assessed. In the validation group, haemodynamic changes correlated to voltage maps were similar to those obtained with conventional analysis in 5/5 patients. In 14/18 patients (78%) with previously inconclusive studies, scalp maps related to epileptic activity had haemodynamic correlates even when no interictal epileptiform discharges were detected during simultaneous recordings. Haemodynamic changes correlated to voltage maps were spatially concordant with intracranial electroencephalography or with the resection area. We found better concordance in patients with lateral temporal and extratemporal neocortical epilepsy compared to medial/polar temporal lobe epilepsy, probably due to the fact that electroencephalographic voltage maps specific to lateral temporal and extratemporal epileptic activity are more dissimilar to maps of physiological activity. Our approach significantly increases the yield of simultaneous electroencephalography and functional magnetic resonance imaging to localize the epileptic focus non-invasively, allowing better targeting for surgical resection or implantation of intracranial electrode arrays.