EEG Source Imaging Research Articles

Multimodal Presurgical Evaluation of Refractory Localization-Related Epilepsy Involving Mesial Temporal Lobe Structures

Abstract INTRODUCTION Localization-related epilepsy frequently involves mesial temporal lobe structures (MTLS), but sometimes presurgical workup can be confusing since shortage of definite structural lesional evidence or inconsistency among multimodal tools. Occasionally invasive workup is applied. This study evaluates the application of multimodal workup in surgical strategy making of localization-related epilepsy involving MTLS. METHODS Our study includes patients with significant or subtle MTLS abnormality (or magnetic resonance imaging (MRI) negative) in 3.0T MRI and then underwent resective surgeries encompassing MTLS. In addition to conventional workup tools, accurate EEG source imaging (ESI) technique based on individual head models is available. Patients accepted either 1-stage resective surgeries or staged surgeries (SEEG implantation + stage-2 resective surgeries). Contributions of structural MRI, fluorodeoxyglucose-positron emission tomography (FDG-PET), accurate ESI, and ictal EEG to surgical strategies were evaluated by criteria defining epileptogenic zone (resective scope in cases with good outcome). RESULTS Thirty-five patients who achieved Engel grade I + II outcome after resective surgeries were included. All together, 68.6% cases showed ESI sources totally falling into resective scope. 42.9% ictal EEG estimates, 57.1% PET focal hypometabolistic regions, 74.3% MRI structural lesions fitted the resective scope/side. In total, 75% 1-stage cases showed ESI sources being confined in MTLS region, while PET showed focal hypometabolism in 66.7% and MRI indicated single MTLS lesions (including subtle ones) in 79.2% cases. In total, 54.5% ESI sources and 63.6% MRI abnormality (single, including subtle ones) in staged cases showed complete concordance with SEEG findings. In subtle lesional/MRI negative cases, ESI generated sources confined within MTLS in 77.8% cases, while PET estimates are focally localizing in 44.4% cases and 55.6% showed subtle MRI lesions which were firstly diagnosed “negative.” CONCLUSION Multimodal noninvasive workup are contributable to help decide strategy of 1-stage resective surgeries and SEEG plans in localization-related epilepsy. High-quality structural MRI, accurate ESI technique based on scalp EEG as well as FDG-PET are key to presurgical planning of epilepsy involving MTLS.

A Novel Noninvasive Approach Based on SPECT and EEG for the Location of the Epileptogenic Zone in Pharmacoresistant Non-Lesional Epilepsy.

Background and objectives: The aim of this study is to propose a methodology that combines non-invasive functional modalities electroencephalography (EEG) and single photon emission computed tomography (SPECT) to estimate the location of the epileptogenic zone (EZ) for the presurgical evaluation of patients with drug-resistant non-lesional epilepsy. Materials and Methods: This methodology consists of: (i) Estimation of ictal EEG source imaging (ESI); (ii) application of the subtraction of ictal and interictal SPECT co-registered with MRI (SISCOM) methodology; and (iii) estimation of ESI but using the output of the SISCOM as a priori information for the estimation of the sources. The methodology was implemented in a case series as an example of the application of this novel approach for the presurgical evaluation. A gold standard and a coincidence analysis based on measures of sensitivity and specificity were used as a preliminary assessment of the proposed methodology to localize EZ. Results: In patients with good postoperative evolution, the estimated EZ presented a spatial coincidence with the resection site represented by high values of sensitivity and specificity. For the patient with poor postoperative evolution, the methodology showed a partial incoherence between the estimated EZ and the resection site. In cases of multifocal epilepsy, the method proposed spatially extensive epileptogenic zones. Conclusions: The results of the case series provide preliminary evidence of the methodology’s potential to epileptogenic zone localization in non-lesion drug-resistant epilepsy. The novelty of the article consists in estimating the sources of ictal EEG using SISCOM result as a prior for the inverse solution. Future studies are necessary in order to validate the described methodology. The results constitute a starting point for further studies in order to support the clinical reliability of the proposed methodology and advocate for their implementation in the presurgical evaluation of patients with intractable non-lesional epilepsy.

Noninvasive neuroimaging enhances continuous neural tracking for robotic device control.

Brain-computer interfaces (BCIs) utilizing signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems significantly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly impact the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework utilizing electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the "brain" and "computer" components by respectively increasing user engagement through a continuous pursuit task and associated training paradigm, and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by over 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Finally, this framework was deployed in a physical task, demonstrating a near seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications on the eventual development and implementation of neurorobotics by means of noninvasive BCI.

Discriminating schizophrenia disease progression using a P50 sensory gating task with dense-array EEG, clinical assessments, and cognitive tests

ABSTRACTBackground: Schizophrenia affects approximately 10% of the world’s population. Early detection of schizophrenia may significantly delay its progression. Although sensory gating deficits are reported in schizophrenia, it remains challenging how sensory gating deficits can be used with other metrics for risk detection and early diagnosis.Research design and methods: Using EEG, the authors examined effects of sensory gating on the performance of 136 participants in a P50 sensory gating task, including patients with first-episode schizophrenia (FESZ), ultra-high risk (UHR) individuals, high-risk (HR) individuals, and age- and sex-matched healthy controls (HCs). The authors also explored the differences among all groups using clinical assessments and cognitive tests.Results: Compared with HCs, HR, UHR and FESZ groups showed significant P50 suppression impairment. Furthermore, EEG source localization analyses identified successively stronger activation in prefrontal and anterior temporal regions in the HR, UHR and FESZ groups than in the HC group. Moreover, brain connectivity (HC < HR < UHR < FESZ) in the gamma band of P50 components was increasingly enhanced in accordance with the level of psychosis risks.Conclusions: These findings suggest that EEG source imaging techniques, brain network dynamics, and behavioral tests, can help clearly distinguish different stages of schizophrenia, and may assist in the early diagnosis of schizophrenia.

EEG Source Imaging from Standard Long-term Video EEG Recording in Patients with Medically-refractory Focal Epilepsy (P4.5-006)

EEG Source Imaging from Standard Long-term Video EEG Recording in Patients with Medically-refractory Focal Epilepsy (P4.5-006)

EEG Source Imaging: A Practical Review of the Analysis Steps.

The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.

Subcortical electrophysiological activity is detectable with high-density EEG source imaging

Subcortical neuronal activity is highly relevant for mediating communication in large-scale brain networks. While electroencephalographic (EEG) recordings provide appropriate temporal resolution and coverage to study whole brain dynamics, the feasibility to detect subcortical signals is a matter of debate. Here, we investigate if scalp EEG can detect and correctly localize signals recorded with intracranial electrodes placed in the centromedial thalamus, and in the nucleus accumbens. Externalization of deep brain stimulation (DBS) electrodes, placed in these regions, provides the unique opportunity to record subcortical activity simultaneously with high-density (256 channel) scalp EEG. In three patients during rest with eyes closed, we found significant correlation between alpha envelopes derived from intracranial and EEG source reconstructed signals. Highest correlation was found for source signals in close proximity to the actual recording sites, given by the DBS electrode locations. Therefore, we present direct evidence that scalp EEG indeed can sense subcortical signals.

Decoding of motor imagery EEG based on brain source estimation

The decoding of Motor Imagery EEG (MI-EEG) is the most crucial part of biosignal processing in the Brain-computer Interface (BCI) system. The traditional recognition mode is always devoted to extracting and classifying the spatiotemporal feature information of MI-EEG in the sensor domain, but these brain dynamic characteristics, which are derived from the cerebral cortical neurons, are reflected more immediately and obviously with high spatial resolution in the source domain. With the development of neuroscience, the state-of-the-art EEG Source Imaging (ESI) technology converts the scalp signals into brain source space and excavates the way for source decoding of MI-EEG. Minimum Norm Estimate (MNE) is a classical and original EEG inverse transformation. Due to the lack of depth weighting of dipoles, it may be more suitable for the estimation of superficial dipoles and will be slightly insufficient for further source classification. In addition, the selection of a Region of Interest (ROI) is usually an essential step in the source decoding of MI-EEG by Independent Component Analysis (ICA), and the most relevant independent component of original EEG signals is transformed into the equivalent current dipoles to obtain the ROI by ESI. Although the excellent results of this method can be obtained for unilateral limb motor imaging EEG signals, which shows more distinct phenomena of event-related desynchronization (ERD), the decoding accuracy may be restricted for more complex multi-limb motor imagery tasks, whose ERD is no longer evident. Therefore, in this paper, we propose a novel brain source estimation to decode MI-EEG by applying Overlapping Averaging (OA) in the temporal domain and Weighted Minimum Norm Estimate (WMNE), which overcomes the limitations of general ROI-based decoding methods and introduces weighting factors to complement the estimation of deep dipoles. Its advantages will be evaluated on a public dataset with five subjects by comparing it with MNE, WMNE, sLORETA, OA-MNE and ICA-WMNE. The proposed method reaches a higher average decoding accuracy of 81.32% compared to other methods by 10-fold cross-validation at the same chance level. This study will increase the universality of the source decoding and facilitate the development of a BCI system in the source domain.

Chapter 6 - EEG source localization

Since the discovery of electroencephalography (EEG), when it was hoped that EEG would offer "a window into the brain," researchers and clinicians have attempted to localize the neuronal activity in the brain that generates the scalp potentials measured noninvasively with EEG. Early explorations in the 1950s using electric field theory to infer the location and orientation of the current dipole in the brain from the scalp potential distribution triggered considerable efforts to quantitatively deduce these sources. Initially, dipole fitting, or dipole localization, was the method of choice and many studies used this approach in experimental and clinical studies with remarkable success. Later on, new methods were proposed that attempted to overcome the problem of having to fix the number of sources a priori; these methods are known as distributed source imaging techniques. The introduction and increasing availability of magnetic resonance imaging, allowing detailed realistic anatomy of the brain and head to be incorporated in source localization methods, has drastically increased the precision of such approaches. Today, source localization of EEG (and magnetoencephalography, or MEG) has reached a level of consistency and precision that allows these methods to be placed in the family of brain imaging techniques. The particular advantage that they have over other imaging methods is their high temporal resolution, which allows the origin of activity to be distinguished from its propagation and information flow in large-scale brain networks to be examined. This chapter gives an overview of these methods and illustrates them with several examples, thereby focusing on EEG source imaging in epilepsy and presurgical planning, as clinical applications with remarkable maturation.

Investigating dynamic functional network patterns after propofol-induced loss of consciousness

ObjectiveWe investigated the changes of dynamic brain functional network from awaken state to the anesthesia level suitable for surgery. Methods60-channel EEG data of 22 subjects are acquired at wakefulness, light anesthesia and deep anesthesia. The activity of 68 cortical regions are obtained by using EEG source imaging. Sliding window analysis is employed to obtain a dynamic sequence of brain functional network. K-means clustering algorithm is then employed to identify the common brain functional network patterns. ResultsFive common brain functional network patterns were identified across all conscious levels. The occurrence of each meta-stable network pattern was associated with the level of anesthesia. A transition functional network pattern was found to transfer to the anesthesia dominating or wakefulness dominating network pattern depending on the conscious level. Furthermore, a functional network pattern persisted during both wakefulness and anesthesia is found to be supported by the anatomical connectivity. ConclusionsDynamic changes of brain functional network exist in both awaken and anesthesia state. SignificanceThese findings suggest that dynamic brain functional network analysis plays a critical role in decoding the mechanism of general anesthesia. The obtained five metastable network patterns may be employed for monitoring the depth of anesthesia.

EEG electrode digitization with commercial virtual reality hardware.

Accurate spatial co-registration of EEG electrode positions with individual head models is an important component for EEG source localization and imaging. Due to variations in head shape between individuals, this requires measurements of electrode locations in each individual. Existing hardware for digitization can be accurate, but also relatively expensive. With the goal of making digitization more accessible for a range of research laboratories, we have developed an open-source software tool that can make use of less expensive consumer virtual reality hardware for EEG electrode digitization. Here we describe our developed VRDigitizer system and compare it to existing digitization solutions. Experimental evaluations were performed in a phantom head model and in 12 human subjects. In our comparison experiments, VRDigitizer was able to measure electrode positions with a mean error of 3.74 mm, compared to 1.73 mm and 2.98 mm for the commercial systems tested.

Sparse EEG Source Localization Using LAPPS: Least Absolute l-P (0 < p < 1) Penalized Solution.

The electroencephalographic (EEG) inverse problem is ill-posed owing to the electromagnetism Helmholtz theorem and since there are fewer observations than the unknown variables. Apart from the strong background activities (ongoing EEG), evoked EEG is also inevitably contaminated by strong outliers caused by head movements or ocular movements during recordings. Considering the sparse activations during high cognitive processing, we propose a novel robust EEG source imaging algorithm, LAPPS (Least Absolute -P (0 < p < 1) Penalized Solution), which employs the -loss for the residual error to alleviate the effect of outliers and another -penalty norm (p=0.5) to obtain sparse sources while suppressing Gaussian noise in EEG recordings. The resulting optimization problem is solved using a modified ADMM algorithm. Simulation study was performed to recover sparse signals of randomly selected sources using LAPPS and various methods commonly used for EEG source imaging including WMNE, -norm, sLORETA and FOCUSS solution. The simulation comparison quantitatively demonstrates that LAPPS obtained the best performances in all the conducted simulations for various dipoles configurations under various SNRs on a realistic head model. Moreover, in the localization of brain neural generators in a real visual oddball experiment, LAPPS obtained sparse activations consistent with previous findings revealed by EEG and fMRI. This study demonstrates a potentially useful sparse method for EEG source imaging, creating a platform for investigating the brain neural generators. This method alleviates the effect of noise and recovers sparse sources while maintaining a low computational complexity due to the cheap matrix-vector multiplication.

Perioperative Multimodal Evaluation and Surgical Tactics of Tumor-Related Epilepsy: 2-Dimensional Operative Video

Treatment of tumor-related epilepsy (TRE), especially for tumors near critical areas, requires surgeons to strike a balance between the epileptic benefit and functional outcome after surgery. Here, we present a case in which multimodal evaluation facilitated the achievement of such surgical balance. Informed patient consent was obtained. A 17-yr-old female presented with seizure attacks for 2 yr. Magnetic resonance imaging (MRI) revealed a right parietal mass lesion with hypointense signal on T1W imaging, hyperintense signal on T2W imaging, and homogeneous enhancement. Carbamazepine and valproate administration were unable to control the intermittent seizures. From the patient's history and imaging, the initial diagnosis was refractory TRE. Whether lesionectomy would achieve seizure freedom in this case was not certain. Therefore, dESI (dense array EEG source imaging) was used to localize the epileptic zone preoperatively; results showed that the epileptic zone was very close to the lesion in the primary motor cortex. Surgery was carried out under awake-anesthesia, with the aid of multimodal neuronavigation, intraoperative neurophysiological monitoring, and intraoperative MRI evaluation. A gross total lesion resection was achieved while preserving critical motor areas. Histopathology revealed ganglioglioma grade I diagnosis. No motor deficits following surgery were detected except slight increase of muscle tension in the right lower limb. At 6-mo follow-up, the patient was without any motor impairment or any other neurological deficits and completely seizure-free with the antiepileptic drug Carbamazepine 1200 mg/day.

Automated EEG source imaging: A retrospective, blinded clinical validation study

ObjectiveTo evaluate the accuracy of automated EEG source imaging (ESI) in localizing epileptogenic zone. MethodsLong-term EEG, recorded with the standard 25-electrode array of the IFCN, from 41 consecutive patients with focal epilepsy who underwent resective surgery, were analyzed blinded to the surgical outcome. The automated analysis comprised spike-detection, clustering and source imaging at the half-rising time and at the peak of each spike-cluster, using individual head-models with six tissue-layers and a distributed source model (sLORETA). The fully automated approach presented ESI of the cluster with the highest number of spikes, at the half-rising time. In addition, a physician involved in the presurgical evaluation of the patients, evaluated the automated ESI results (up to four clusters per patient) in clinical context and selected the dominant cluster and the analysis time-point (semi-automated approach). The reference standard was location of the resected area and outcome one year after operation. ResultsAccuracy was 61% (95% CI: 45–76%) for the fully automated approach and 78% (95% CI: 62–89%) for the semi-automated approach. ConclusionAutomated ESI has an accuracy similar to previously reported neuroimaging methods. SignificanceAutomated ESI will contribute to increased utilization of source imaging in the presurgical evaluation of patients with epilepsy.

EEG source imaging of epileptic activity at seizure onset

Surgical resection of the seizure onset zone (SOZ) requires that this region of the cortex is accurately localized. The onset of a seizure may be marked by transient discharges, but it also may be accompanied by oscillatory, sinusoidal electrographic activity, such as the EEG theta rhythm. However, because of the superposition of the seizure signal with other electrical signals, including noise artifacts and non-seizure brain activity, noninvasive Electrical Source Imaging (ESI) of the ictal EEG activity at seizure onset remains a challenging task for surgical planning. In the present study, we localize the SOZ from oscillatory features of the EEG at the ictal onset using 256-channel high density electroencephalography (HD-EEG), exact sensor positions, and individual electrical head models constructed from the patient’s T1 magnetic resonance image (MRI). Epileptic activities at the seizure onset were characterized with joint time-frequency analysis and source estimated by standardized low resolution electromagnetic tomography (sLORETA) inverse method. The consistency of this localization was examined across multiple seizures for individual patients. For validation, results were compared to three clinical criteria: (1) epileptogenic lesions, (2) seizure onset observed in intracranial EEG, and (3) successful surgical outcomes. In this set of 84 seizures, the onsets of 56 seizures could be localized. For the lateralization measure, the results from HD-EEG with interictal spikes (8/10) and with ictal onset (10/10) were more accurate than international 10–20 EEG for interictal spikes (5/10) and ictal onset (5/10). ESI from HD-EEG with ictal onset (9/10) had greater concordance to the clinical criteria than HD-EEG with interictal spikes (6/10). Noninvasive ESI of oscillatory features at ictal onset using 256-channel HD-EEG and high-resolution individual head models can make a useful contribution to the clinical localization of the SOZ in presurgical planning.

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

T04. Clinical utility of electromagnetic source imaging in the Danish presurgical epilepsy evaluation

Introduction The greatest challenge in epilepsy surgery lies in the presurgical evaluation. On their own, none of the diagnostic methods can identify the epileptogenic zone. Therefore, a multimodal approach is used. Over the last decades, advances in recording techniques and in signal analysis made it possible to estimate the source of the epileptiform discharges recorded by electroencephalography (EEG source imaging: ESI) and magnetoencephalography (MEG source imaging: MSI). This prospective study investigates the role of electromagnetic source imaging (EMSI) as a non-invasive tool to guide the multidisciplinary epilepsy surgery team. Methods MEG (306 channels) and simultaneous high-density EEG was recorded in 85 consecutive patients with refractory focal epilepsy, referred for conventional non-invasive presurgical evaluation. EMSI, comprising of electric, magnetic source imaging and analysis of combined MEG-EEG datasets, using commercially available software (BESA and CURRY) was used. The Danish epilepsy surgery team evaluated the patients first blinded to EMSI and then including the data from EMSI. At both sessions the multidisciplinary team (MDT) determined the presumed localisation of the epileptogenic zone and decided on location of surgery, intracranial registration (ICR) placement, or not offering epilepsy surgery. The clinical utility of EMSI was defined as the proportion of patients in whom EMSI changed the decision of the MDT. A change was defined useful as follows: (a) change from stop to ICR: the ICR localized the source; (b) change in implantation strategy: the electrode(s) implanted based on the EMSI identified the source; and (c) change from implantation to operation: the patient became seizure-free. Results The impact of EMSI on patient management-plan was assessed in 85 patients (50 men) in whom EMSI was part of the decision-making process. The age of these patients was between 10 and 70 years (median: 32 years). Thirty-eight patients were MRI negative; in the remaining patients, there were discordance between MRI and data from long-term video-EEG monitoring (semiology and EEG). EMSI changed the management plan in 28/85 patients (33%). For 16.5% (14/85) of the patients the ICR plan changed (additional structures were implanted). For 7% (6/85) of the patients, in whom neither operation nor ICR was suggested, after discussing the EMSI results, ICR was offered. For 9.4% (8/85) of the patients ICR was suggested prior to EMSI; after EMSI these patients skipped ICR and went directly to operation. Finally, for one patient it was decided not to offer operation, after discussing the EMSI. At one-year follow-up 80% (16/20) of these changes proved to be useful, meaning that the EMSI changes located the seizure onset zone, irritative zone or the patient became seizure free. Conclusion Electromagnetic source imagening provides clinically relevant information that supplements the decision-making process in presurgery evaluation for epilepsy surgery.

F10. Co-localization of ictal and interictal activity using high density EEG data

Despite major advances in using high density EEG (hdEEG) and electrical source imaging (ESI) for analysis of interictal activity, research demonstrating its utility in examining ictal activity is still lacking. This may be a result of technical challenges associated with capturing epileptic seizures on scalp hdEEG recordings (physical constraints, such as high number of sensors, lack of standardization in review, comfort, etc.). Furthermore, the value of using interictal discharges as a valid estimate of seizure onset zone (SOZ) still remains disputable. The aim of this study is to use electrical source imaging (ESI) to evaluate the co-localization of ictal and interictal activity through the use of high density scalp EEG. A retrospective analysis was performed on 5 patients with intractable focal temporal and extratemporal lobe epilepsy. EEG scalp recordings were obtained using high density 256 Geodesic Sensory Net (Geodesic Sensor Net, Electrical Geodesic Inc.). ESI was performed using two different commercially available ESI systems, Curry 7.0 (Compumedics) and MIPEGI (Electrical Geodesic Inc.). Sensor locations were determined using the Geodesic Photogrammetry System (GPS) and Geosource 3 (Electrical Geodesic Inc), Realistic geometric individualized head models were constructed from patient specific MRIs using the boundary element model and the finite difference model. Source reconstruction was based on distributed source models and dipole models. The concordance of irritative and ictal onset zones was estimated by the distance between maximal ictal and interictal source activity. ESI identified the seizure onset and irritative zones in all 5 patients. There was good concordance between the estimated seizure onset zone and irritative zone on the sub-lobar level. Similar results were obtained through the use of the boundary element and finite difference models. The results provide insight into whether interictal discharges provide accurate estimate of SOZ. Furthermore, these results assess similarity of source reconstruction obtained through two different types of individual head models that are commercially available.

T12. Automated EEG analysis of long-term EEG to localize the epileptogenic zone in extra-temporal lobe epilepsy

The localization of the epileptogenic focus in patients with extra temporal lobe epilepsy (ETLE) is often complex. Extra investigations such as ictal SPECT, interictal PET and MEG are frequently performed to obtain more information about where the epilepsy originates from. Nevertheless, these patients can have infrequent interictal epileptic discharges and seizures. Long-term video EEG monitoring allows recording interictal and ictal epochs over a longer period of time. In this study we use automated long-term EEG analysis to localize the epileptogenic focus in ETLE patients. We searched the database of Geneva University Hospital for patients that had ETLE surgery. We included patients that (i) had long-term EEG recording, (ii) had pre-operative MRI, (iii) post-operative follow-up of at least 1 years and (iv) no prior brain surgery. This resulted in the inclusion of 24 patients with ETLE. Of these patients, 15 had Engel class I and 9 had Engel class II, III or IV outcome. For all patients automated EEG analysis was performed by Epilog (Epilog NV, Ghent, Belgium). This included automated spike detection and subsequent EEG source imaging to localize the sources of the detected spikes. The spike clusters were scored by an expert electrophysiologist as genuine interictal discharges, artefact or physiological brain waves. Afterwards, the genuine spike clusters were localized at onset, half-rising time and peak of the spike in the brain using patient specific head model constructed from the patient’s MRI. The localization was compared to the resection at sub-lobar level to assess the sensitivity and specificity of the method to localize the epileptogenic zone. In 62% of patients (n = 15/24) genuine interictal spikes were detected by the software according to the scoring of the expert electrophysiologist. Of these 15 patients, 9 had Engel class I outcome and 6 had Engel class II, III or IV outcome. The localization of the spike cluster 1, i.e. the spike cluster with most detected single events, had a sensitivity of 22%, 44% and 44% and specificity of 100%, 83% and 83% to localize the epileptogenic focus at the onset, half-rising time and peak of the spikes, respectively. For spike cluster 2, i.e. the spike cluster with second most detected single events, the sensitivity to localize the epileptogenic focus was 25%, 25% and 0%; while the specificity was equal to 86%, 71% and 71% at onset, half-rising and peak of the spike, respectively. Combining cluster 1 and 2 resulted in a sensitivity 56% coupled to a specificity of 71% and 71% at the half-rising time of the spike. Although the found sensitivity and specificity is lower than that in temporal lobe epilepsy patients (in which we found a sensitivity of 79% and specificity of 75%), it remains useful to be included in the pre-surgical evaluation. This because for the ETLE patients each piece of information that can confirm or contradict a hypothesis should be included given the complexity of most cases.