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Item does not contain fulltext Objective: Our objective was to unravel the dynamics underlying spike-and-wave discharges (SWDs) characteristic for childhood absence epilepsy. Methods: SWDs were recorded for a cohort of 28 children using magnetoencephalography. Non-linear association analyses and a graph theoretical metric of local connectedness (LoC) were utilized in a sliding window starting one s before till four s after ictal onset. Results: A focal pattern of bilateral frontal and parietal areas with high LoC during the spikes alternated by generalized patterns during the waves was found for all children studied during generalization of the SWDs. In the interval preceding the generalization a focal parietal region was most often (16/28) encountered and less often an occipital (4/28), temporal (5/28) or frontal (3/28) region. 55% of the children with a parietal/occipital focal onset became seizure free after the administration of two anti-epileptic drugs, and only 12.5% with a temporal/frontal focal onset. Conclusions: The transition from interictal to the ictal state is for some of the children characterized by dominant LoC at either the parietal/occipital and for others at the frontal/temporal region. Significance: The focal onset of the SWDs varies in location among the children with a clinical similar profile, who, however, seemingly are differing with regard to seizure control. 9 p.

Aim. The diagnostic process for epilepsy can be lengthy and stressful, which may delay the start of treatment. The objective of this study was to determine the benefit of routine magnetoencephalography (MEG) with regard to diagnostic gain, compared to routine electroencephalography (EEG), EEG following sleep deprivation (EEGsd), and 24-hour EEG.Methods. In this prospective study, patients were included from two centres (Academic Centre for Epileptology Kempenhaeghe, Heeze and Elisabeth-Twee Steden Hospital, Tilburg) and MEG recording took place at a single centre (Amsterdam University Medical Centre, Vrije Universiteit Amsterdam) in The Netherlands. Consecutively referred patients from peripheral hospitals were included between August 2013 and March 2016. Patients were offered routine MEG in addition to EEG examination and MRI for the diagnosis of epilepsy. The final clinical diagnosis was based on all available clinical data and test results at the end of the diagnostic process. Sensitivity, specificity, and positive and negative predictive values were calculated for routine EEG, routine EEG plus additional EEG and MEG. In addition, diagnostic gain associated with MEG, relative to the other modalities, was calculated. Secondary outcome was congruence of localization of epileptiform discharges between MEG and MRI or final clinical diagnosis.Results. Based on a cohort of 138 patients, sensitivity and specificity was shown to be 31.6% and 78.4% for routine MEG, 31.6% and 100% for routine EEG, and 52.6% and 97.3% for routine EEG plus additional EEG, respectively. Routine MEG demonstrated a diagnostic gain of 16.8% compared to routine EEG and 9.5% compared to routine EEG plus additional EEG. In 35.7% of patients with a lesion on MRI that was consistent with the final clinical diagnosis, MEG showed epileptiform discharges in the same area.Conclusion. Routine MEG may provide additional value during the initial diagnosis of epilepsy.

Purpose: To study possible detection of structural abnormalities on 7T MRI that were not detected on 3T MRI and estimate the added value of MEG-guidance. For abnormalities found, analysis of convergence between clinical, MEG and 7T MRI localization of suspected epileptogenic foci.Methods: In adult patients with well-documented localization-related epilepsy in whom a previous 3T MRI did not demonstrate an epileptogenic lesion but MEG indicated a plausible epileptogenic focus, 7T MRI was performed. Based on semiologic data, visual analysis of the 7T images was performed as well as based on prior MEG results. Correlation with other data from the patient charts, for as far as these were available, was analysed. To establish the level of concordance between the three observers the generalized or Fleiss kappa was calculated.Results: In 3/19 patients abnormalities that, based on semiology, could plausibly represent an epileptogenic lesion were detected using 7T MRI. In an additional 3/19 an abnormality was detected after MEG-guidance. However, in these later cases there was no concordance among the three observers with regard to the presence of a structural abnormality. In one of these three cases intracranial recording was performed, proving the possible abnormality on 7T MRI to be the epileptogenic focus.Conclusions: In 32% of patients 7T MRI showed abnormalities that could indicate an epileptogenic lesion whereas previous 3T MRI did not, especially when visual inspection was guided by the presence of focal interictal MEG abnormalities.

Purpose: Stereotactic EEG (SEEG) recordings are considered to be the best choice for preoperative invasive evaluation when the epilepsy of the patient is suspected to originate in deep-sited anatomical structures and standard electro-clinical examinations are not conclusive. The interictal epileptic discharges (IEDs) occurring in these recordings in general are abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. Method: A framework is developed to model the spatiotemporal net-work interactions underlying the IEDs. To identify the highly synchronized neural activity underlying these discharges, the variation in correlation over time of the SEEG signals is related to the occurrence of the IEDs using the general linear model [van Houdt et al., 2012]. Subsequently, it is assessed whether the brain regions that reflect highly synchronized neural activity are either independent or interacting within an epileptic network. Independent component analysis is applied followed by clustering of the spatial distributions of the independent components. The spatial distributions of the spike clusters are visualized together with the estimated time delays against the patients’ brain anatomy [Meesters et al., 2015]. Results: The analysis framework was evaluated for five patients who underwent SEEG recordings prior to successful epilepsy surgery. The spatial distribution of the spike cluster that was related to the MRI-visible brain lesions coincided with the seizure onset zone of these patients. Unraveling of the complex network interactions underlying the IEDs of two more patients without satisfactory surgical outcome indicated that an alternative and plausible resection strategy could have been considered. Conclusion: The analysis framework applied to IEDs is considered a valuable additional tool to the current seizure assessment approach, which might lead to a more successful outcome of epilepsy surgery. Acknowledgement: This study is part of the DeNeCor-project that has received funding from the ENIAC Joint Undertaking (grant no. 324257).

Background: Diagnosing epilepsy can be lengthy and stressful, potentially leading to increased use of healthcare resources and a reduction in quality of life. Aim: This study aims to determine cost and quality of life before and after an optimized diagnostic procedure for people suspected of having epilepsy from a societal perspective with a follow-up of 12 months. In addition, this study aims to differentiate between people diagnosed with epilepsy during the follow-up of the study and the people who are diagnosed as not having epilepsy or for whom diagnosis is still uncertain. Methods: A questionnaire regarding the use of healthcare resources was used accompanied by the EQ-5D-3 L. Multiple imputations by chained equations with predictive mean matching was used to account for missing data. To investigate the uncertainty of the results, non-parametric bootstrapped (1000 times) was used. Results: In total, 116 people were included in the study. Total average costs per patient made in the previous 3 months had decreased from (sic)4594 before the optimized diagnostic trajectory to (sic)2609 in the 12 months after the optimized diagnostic trajectory. Healthcare costs were the largest expense group (52-66%) and had decreased significantly from baseline measurement to 12 months after baseline ((sic)2395 vs (sic)1581). Productivity costs had decreased from (sic)1367 to (sic)442 per 3 months. Total annual costs were similar between people diagnosed with epilepsy during the follow-up of the study and the people who are diagnosed as not having epilepsy or for whom diagnosis is still uncertain. Quality of Life had significantly increased over the course of 12 months from 0.80 to 0.84 (Dutch tariff). Discussion: This study indicates that an optimized diagnostic trajectory has positively influenced the use of healthcare resources and the quality of life in people with epilepsy. As chronic care patients make diverse costs, future research should identify the long-term costs after an optimized diagnostic trajectory for patients with epilepsy, possibly identifying patients who are at high risk of becoming high-cost users in the future for early intervention.

Anti-epileptic drugs (AEDs) have a global effect on the neurophysiology of the brain which is most likely reflected in functional brain activity recorded with EEG and fMRI. These effects may cause substantial inter-subject variability in studies where EEG correlated functional MRI (EEG–fMRI) is used to determine the epileptogenic zone in patients who are candidate for epilepsy surgery. In the present study the effects on resting state fMRI are quantified in conditions with AED administration and after withdrawal of AEDs. EEG–fMRI data were obtained from 10 patients in the condition that the patient was on the steady-state maintenance doses of AEDs as prescribed (condition A) and after withdrawal of AEDs (condition B), at the end of a clinically standard pre-surgical long term video-EEG monitoring session. Resting state networks (RSN) were extracted from fMRI. The epileptic component (ICE) was identified by selecting the RSN component with the largest overlap with the EEG–fMRI correlation pattern. Changes in RSN functional connectivity between conditions A and B were quantified. EEG–fMRI correlation analysis was successful in 30% and 100% of the cases in conditions A and B, respectively. Spatial patterns of ICEs are comparable in conditions A and B, except for one patient for whom it was not possible to identify the ICE in condition A. However, the resting state functional connectivity is significantly increased in the condition after withdrawal of AEDs (condition B), which makes resting state fMRI potentially a new tool to study AED effects. The difference in sensitivity of EEG–fMRI in conditions A and B, which is not related to the number of epileptic EEG events occurring during scanning, could be related to the increased functional connectivity in condition B., Highlights • EEG–fMRI measured before and after withdrawal of AEDs in focal epilepsy. • Epilepsy-related fMRI patterns are comparable before and after withdrawal of AEDs. • Increased functional connectivity and EEG–fMRI yield after withdrawal of AEDs.

Background: Diagnosing epilepsy is a lengthy and burdensome process for patients and their family. Although the need for a more patient-centered approach in clinical practice is widely acknowledged, empirical evidence regarding patient preferences for diagnostic modalities in epilepsy is missing. The objectives of this study were 1) to identify to what extent important attributes of diagnostic procedures in epilepsy affect preferences for a procedure, 2) to determine the relative importance of these attributes, and 3) to calculate overall utility scores for routine electroencephalography (EEG) and magnetoencephalography (MEG) recordings.Methods: A discrete choice experiment was performed to determine patients' preferences, which involved presentation of pairwise choice tasks regarding hypothetical scenarios. Scenarios varied along six attributes: "way of measuring brain activity", "duration", "freedom of movement", "travel time", "type of additional examination", and "chance of additional examination". Choice tasks were constructed using a statistically efficient design, and the questionnaire contained 15 unique unlabeled choice tasks. Mixed multinomial logistic regression was used to estimate patients' preferences.Results: A total of 289 questionnaires were included in the analysis. McFadden's pseudo R-2 showed a model fit of 0.28, and all attributes were statistically significant. Heterogeneity in preferences was present for all attributes. "Freedom of movement" and "Chance of additional examination" were perceived as the most important attributes. Overall utility scores did not substantially differ between routine EEG and MEG.Conclusion: This study suggests that the identified attributes are important in determining patients' preference for epilepsy diagnostics. It can be concluded that MEG is not necessarily more patient-friendly than a routine EEG in primary diagnostics and, regarding additional diagnostics, patients have a strong preference for long-term 24-h EEG over EEG after sleep deprivation. Furthermore, barring substantial heterogeneity within the parameters in mind, our study suggests that it is important to take individual preferences into account in medical decision-making. (C) 2013 Elsevier Inc. All rights reserved.

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.

The analysis of simultaneous EEG and fMRI data is generally based on the extraction of regressors of interest from the EEG, which are correlated to the fMRI data in a general linear model setting. In more advanced approaches, the spatial information of EEG is also exploited by assuming underlying dipole models. In this study, we present a semi automatic and efficient method to determine electrode positions from electrode gel artifacts, facilitating the integration of EEG and fMRI in future EEG/fMRI data models. In order to visualize all electrode artifacts simultaneously in a single view, a surface rendering of the structural MRI is made using a skin triangular mesh model as reference surface, which is expanded to a "pancake view". Then the electrodes are determined with a simple mouse click for each electrode. Using the geometry of the skin surface and its transformation to the pancake view, the 3D coordinates of the electrodes are reconstructed in the MRI coordinate frame. The electrode labels are attached to the electrode positions by fitting a template grid of the electrode cap in which the labels are known. The correspondence problem between template and sample electrodes is solved by minimizing a cost function over rotations, shifts and scalings of the template grid. The crucial step here is to use the solution of the so-called "Hungarian algorithm" as a cost function, which makes it possible to identify the electrode artifacts in arbitrary order. The template electrode grid has to be constructed only once for each cap configuration. In our implementation of this method, the whole procedure can be performed within 15 min including import of MRI, surface reconstruction and transformation, electrode identification and fitting to template. The method is robust in the sense that an electrode template created for one subject can be used without identification errors for another subject for whom the same EEG cap was used. Furthermore, the method appears to be robust against spurious or missing artifacts. We therefore consider the proposed method as a useful and reliable tool within the larger toolbox required for the analysis of co-registered EEG/fMRI data. © 2011 Elsevier Inc.

Introduction Intracranial recordings, like intracerebral depth electrode recordings are considered to be the best choice for preoperative invasive evaluation when standard electro-clinical examinations are not conclusive. These recordings reflect a vast amount of interictal epileptic discharges, the so called spikes, which are in general abundant compared to seizure activity. Manually reviewing these recordings to find the spatial origin of seizures and spikes is still state of the art, which is a very exhausting task due to the large number of signals recorded. In order to enable a time-efficient evaluation of such recordings, we developed a spike detection algorithm which performs automatic detection, spatial clustering and smart visualization of spike clusters. Methods The automatic spike detection algorithm for depth electrode recordings is based on our existing spike detection algorithm for surface EEG. The spike detection algorithm assesses morphological and topographical potential field features. Both feature groups had to be adapted for depth electrode recordings. In these recordings spikes are shorter in duration, often have higher amplitude and the properties of the potential field compared to surface EEG are very different. Due to the morphological similarity of individual waves of the posterior alpha rhythm and spikes, the detection within such rhythmic periods is suppressed. In order to speed up the review, we developed a spatial clustering and a smart visualization of spike clusters. In the so-called overlay plot the signals of all individual spikes of each cluster are displayed one above the other in one figure, allowing for quickly distinguishing between true spikes and artifacts. In order to assess the sensitivity and precision of the developed algorithms, 1400 spikes in 3 datasets from different patients recorded at the Academic Center of Epileptology, Kempenhaeghe/MUMC were annotated by an experienced biomedical assistant. Results The automatic spike detection algorithm detected 54% of all spikes on average. The overall precision was 39%. Sensitivity values reach from 18% to 81%. However, also for the patient with 18% sensitivity the algorithm was able to detect several spikes on all those electrodes where also the human reviewer detected them. For one patient a very low precision of 6% was measured. This small precision value came from repetitive artifacts which are very similar to spikes and are even difficult to distinguish for human experts. Removing the most evident artifact clusters by using the overlay plot, the precision increases from 6% to 30%. Doing the same for all patients an average precision of 68% is achieved. Conclusion Our automatic method was able to detect spikes with a high sensitivity on average. Moreover, our algorithm always detected spikes on all the electrodes where the expert also identified them allowing for a profound diagnosis. The smart combination of detection, clustering and visualization potentially enables a more time-efficient review.

Introduction Preoperative evaluation of patients with medically intractable epilepsies requires accurate localization of the epileptogenic zone. Intracerebral depth electrodes are used to asses regions of interest and to enclose the resection zone during presurgical planning. Often a high number of needles is needed to accurately define the resection volume. Manual evaluation of these prolonged recordings including a large number of channels which is highly time consuming. To raise efficiency we developed a computer algorithm for automatic detection of epileptic seizures in depth electrode recordings. Primary goal was to detect seizures with high sensitivity without the need to set patient specific parameters. Methods The automatic seizure detection algorithm for depth electrode data was based on an existing seizure detection algorithm for surface EEG. Evaluation of the frequency and amplitude range was extended to allow detection of ictal activity of up to 25 Hz and amplitudes of up to 1 mV. To reduce the number of false detections a method to recognize loose contacts was implemented. For clinical validation, recordings of 11 patients that underwent depth electrode investigation in the Academic Centre of Epileptology, Kempenhaeghe were utilized. Recordings were evaluated manually by clinical neurophysiologists and seizures were annotated. For 10 patients the first 24 h of their depth electrode recording were analyzed, yielding in total 23 seizures detected for five of the patients studied. For one patient the complete recording was analyzed with a duration of 138 h, yielding 36 seizures. Manual seizure annotations were compared to computer detections for assessment of sensitivity and false detection rate. Results The automatic seizure detection algorithm found 84% of all seizures on average. All or more than 80% of the seizures were detected in the first 24 h of 10 patients. Analysis of the complete patient recording showed suppressed seizure onset activity of less than 2 s duration followed by paroxysmal fast activity. In this patient 14 out of the 36 seizures that evolved into a rhythmic ictal pattern were detected. The average false detection rate was 15 false detections in 24 h. Validation showed that most of the false positives either point to interictal activity or to background alpha activity. Conclusion We proposed a computer aided workflow for evaluation of depth electrode recordings. Our automatic seizure detection algorithm was able to detect either all or more than 7 seizures of a patient which allows deduction of important medical findings. The low false detection rate facilitates fast review of data. Our proposed approach showed that automatic evaluation of seizure activity in depth electrode recordings is feasible and will raise the overall efficiency of diagnostic.

Contains fulltext : 157672.pdf (Publisher’s version ) (Closed access) PURPOSE: Although absence epilepsy is considered to be a prototypic type of generalized epilepsy, it is still under debate whether generalized 3 Hz spike-and-wave discharges (SWDs) might have a cortical focal origin. Here it is investigated whether focal interictal epileptiform discharges (IEDs), which typically occur in the electro- (EEG) and magnetoencephalogram (MEG) in case of focal epilepsy, are present in the MEG of children with absence epilepsy. Next, the location of the sources of the IEDs is established, and it is investigated whether the location is concordant to the earlier established focal cortical regions involved in the generalized SWDs of these children. METHODS: Whole head MEG recordings of seven children with absence epilepsy were reviewed with respect to the presence of IEDs (spikes and sharp waves). These IEDs were grouped into distinct clusters, in which each contribution to a cluster yields a comparable magnetic field distribution. Source localization was then performed onto the average signal of each cluster using an equivalent current dipole model and a realistic head model of the cortical surface. RESULTS: IEDs were detected in 6 out of 7 patients. Source reconstruction indicated most often frontal, central or parietal origins of the IED in either the left and or right hemisphere. Spatiotemporal assessment of the IEDs indicated a stable location of the averages of these discharges, indicating a single underlying cortical source. DISCUSSION: The outcome of this pilot study shows that MEG is well suited for the detection of IEDs and suggests that their estimated sources coincide rather well with the cortical regions involved during the spikes of the SWDs. It is discussed whether the presence of IEDs, classically seen as a marker of focal epilepsies, indicate that absence epilepsy should be considered as a focal type of epilepsy, in which changes in the network are evolving rapidly. 10 p.

Summary Purpose: The diagnosis of frontal lobe epilepsy may be compounded by poor electroclinical localization, due to distributed or rapidly propagating epileptiform activity. This study aimed at developing optimal procedures for localizing interictal epileptiform discharges (IEDs) of patients with localization related epilepsy in the frontal lobe. To this end the localization results obtained for magnetoencephalography (MEG) and electroencephalography (EEG) were compared systematically using automated analysis procedures. Methods: Simultaneous recording of interictal EEG and MEG was successful for 18 out of the 24 patients studied. Visual inspection of these recordings revealed IEDs with varying morphology and topography. Cluster analysis was used to classify these discharges on the basis of their spatial distribution followed by equivalent dipole analysis of the cluster averages. The locations of the equivalent dipoles were compared with the location of the epileptogenic lesions of the patient or, if these were not visible at MRI with the location of the interictal onset zones identified by subdural electroencephalography. Results: Generally IEDs were more abundantly in MEG than in the EEG recordings. Furthermore, the duration of the MEG spikes, measured from the onset till the spike maximum, was in most patients shorter than the EEG spikes. In most patients, distinct spike subpopulations were found with clearly different topographical field maps. Cluster analysis of MEG spikes followed by dipole localization was successful (n = 14) for twice as many patients as for EEG source analysis (n = 7), indicating that the localizability of interictal MEG is much better than of interictal EEG. Conclusions: The automated procedures developed in this study provide a fast screening method for identifying the distinct categories of spikes and the brain areas responsible for these spikes. The results show that MEG spike yield and localization is superior compared with EEG. This finding is of importance for the diagnosis and preoperative evaluation of patients with frontal lobe epilepsy.

Objective A method for automatic detection of epileptic seizures in long-term scalp-EEG recordings called EpiScan will be presented. EpiScan is used as alarm device to notify medical staff of epilepsy monitoring units (EMUs) in case of a seizure. Methods A prospective multi-center study was performed in three EMUs including 205 patients. A comparison between EpiScan and the Persyst seizure detector on the prospective data will be presented. In addition, the detection results of EpiScan on retrospective EEG data of 310 patients and the public available CHB–MIT dataset will be shown. Results A detection sensitivity of 81% was reached for unequivocal electrographic seizures with false alarm rate of only 7 per day. No statistical significant differences in the detection sensitivities could be found between the centers. The comparison to the Persyst seizure detector showed a lower false alarm rate of EpiScan but the difference was not of statistical significance. Conclusions The automatic seizure detection method EpiScan showed high sensitivity and low false alarm rate in a prospective multi-center study on a large number of patients. Significance The application as seizure alarm device in EMUs becomes feasible and will raise the efficiency of video-EEG monitoring and the safety levels of patients.

Spatial independent component analysis (ICA) is increasingly being used to extract resting-state networks from fMRI data. Previous studies showed that ICA also reveals independent components (ICs) related to the seizure onset zone. However, it is currently unknown how these epileptic ICs depend on the presence of interictal epileptic discharges (IEDs) in the EEG. The goal of this study was to explore the relation between ICs obtained from fMRI epochs during the occurrence of IEDs in the EEG and those without IEDs. fMRI data sets with co-registered EEG were retrospectively selected of patients from whom the location of the epileptogenic zone was confirmed by outcome of surgery (n = 8). The fMRI data were split into two epochs: one with IEDs visible in scalp EEG and one without. Spatial ICA was applied to the fMRI data of each part separately. The maps of all resulting components were compared to the resection area and the EEG–fMRI correlation pattern by computing a spatial correlation coefficient to detect the epilepsy-related component. For all patients, except one, there was a remarkable resemblance between the epilepsy-related components selected during epochs with IEDs and those without IEDs. These findings suggest that epilepsy-related ICs are not dependent on the presence of IEDs in scalp EEG. Since these epileptic ICs showed partial overlap with resting-state networks of healthy volunteers (n = 10), our study supports the need for new ways to classify epileptic ICs.

Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time.Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Gonçalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3. T scanner when using a sampling frequency of 2048. Hz. It even gives clean EEG when the EEG is sampled with only 256. Hz.Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with a least squares procedure. The relevance of this approach is illustrated by determining the BCG artefacts in a data set consisting of 29 healthy subjects recorded in a 1.5. T scanner and 15 patients with epilepsy recorded in a 3. T scanner. Analysis of the relationship between artefact amplitude, duration and heartbeat interval shows that in 22% (1.5. T data) to 30% (3. T data) of the cases BCG artefacts show an overlap. The BCG artefacts of the EEG/fMRI data recorded on the 1.5. T scanner show a small negative correlation between HBI and BCG amplitude.In conclusion, the proposed methodology provides a substantial improvement of the quality of the EEG signal without excessive computer power or additional hardware than standard EEG-compatible equipment. © 2012 Elsevier Inc.

EEG correlated functional MRI (EEG‐fMRI) allows the delineation of the areas corresponding to spontaneous brain activity, such as epileptiform spikes or alpha rhythm. A major problem of fMRI analysis in general is that spurious correlations may occur because fMRI signals are not only correlated with the phenomena of interest, but also with physiological processes, like cardiac and respiratory functions. The aim of this study was to reduce the number of falsely detected activated areas by taking the variation in physiological functioning into account in the general linear model (GLM). We used the photoplethysmogram (PPG), since this signal is based on a linear combination of oxy‐ and deoxyhemoglobin in the arterial blood, which is also the basis of fMRI. We derived a regressor from the variation in pulse height (VIPH) of PPG and added this regressor to the GLM. When this regressor was used as predictor it appeared that VIPH explained a large part of the variance of fMRI signals acquired from five epilepsy patients and thirteen healthy volunteers. As a confounder VIPH reduced the number of activated voxels by 30% for the healthy volunteers, when studying the generators of the alpha rhythm. Although for the patients the number of activated voxels either decreased or increased, the identification of the epileptogenic zone was substantially enhanced in one out of five patients, whereas for the other patients the effects were smaller. In conclusion, applying VIPH as a confounder diminishes physiological noise and allows a more reliable interpretation of fMRI results. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.

At present, in epilepsy, magnetoencephalography (MEG) is mostly used for presurgical evaluations. It has proven to be robust for detecting and localizing interictal epileptiform discharges. Whether this is also true for first-line investigation in the diagnosis of epilepsy has not been investigated yet. We present our data on the usefulness of MEG in the earliest phase of diagnosing epilepsy. We examined 51 patients with suspicion of neocortical epilepsy and an inconclusive routine EEG. A method to integrate MEG in daily routine was developed. Results of visually assessed MEG recordings were compared, retrospectively, with clinical data and with the results of EEG after sleep deprivation. After a finding of inconclusive, routine MEG generated a gain in diagnostic value of 63% when compared with "final" clinical diagnosis. This is comparable with the added value of EEG after sleep deprivation recorded previously in the same patients. However, MEG is less of a burden for patient and hospital and has no association with risk of increase in seizure frequency. The routine MEG with visual assessment only is a reliable diagnostic tool in the routine diagnosis of epilepsy and may replace or precede EEG after sleep deprivation in daily clinical practice. Furthermore, MEG together with MRI enables magnetic source imaging and, thus, may provide additional information on the cortical localization of the epilepsy of a patient.

Contains fulltext : 77431.pdf (Publisher’s version ) (Closed access) Purpose: A nonlinear association and a source localization technique were used to describe the onset and propagation of spike-and-slow-wave discharges (SWDs) in children with absence seizures. Previous studies have emphasized a leading cortical role in the generation of absence seizures in genetic epileptic rats. Methods: Synchronization between all magnetoencephalography (MEG) sensor-couples before and during SWDs in five patients was investigated over time. A source localization [beamformer, SAM(g(2))] technique was used to find brain regions associated with the origin of the spikes of the SWDs. Results: The onset of SWDs was characterized by high associations at left and right frontal regions. An alternating pattern of high synchronization was found during trains of SWDs: generalized during the wave and localized during the spike; the origin of the spike was different from the onset of SWDs, more frontal lateral and medial parietal. The localization of this latter region was confirmed with SAM(g(2)). Discussion: The outcome of the nonlinear association techniques demonstrated that SWDs have a local cortical onset, whereas the association and beamformer technique support a local or even a focal cortical involvement in the occurrence of the spike in a train of SWDs. In all, the cortex contains local frontal and parietal sites relevant before the onset of the generalized pattern of SWDs and other ones that might contain the driving force behind the spike in trains of 3-4 Hz SWDs.

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG–fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Controversy remains regarding the preferred modality, magnetoencephalography (MEG) or EEG, for the presurgical evaluation of patients with epilepsy. In general, it appears that the spike yields for MEG and EEG are similar in patients with temporal lobe epilepsy, and that for neocortical epilepsy the MEG spike yields may be larger than for EEG. In general, MEG/EEG spike yields depend on factors such as (1) the number of sensors, (2) the source depth and orientation, (3) the background activity, and (4) the smearing of the potential fields due to variations in skull resistivity in EEG. Because the contribution of all these factors are of the same order of magnitude, the authors took them all into account to predict the signal-to-noise ratio (SNR) of hypothetical spikes in different brain areas. In this study, it was assumed that spike sensitivity (and therefore the spike yield) increases with SNR. The estimated SNR values at temporal areas were comparable for MEG and EEG, which is in agreement with clinical findings that spike yields in temporal lobe epilepsy are similar. Furthermore, the SNR of MEG was substantially higher in the frontal area, indicating that in frontal lobe epilepsy MEG may be highly relevant to prescreening of epilepsy patients. This model-based approach indicates that SNR mapping clarifies differences between MEG and EEG findings that are difficult to understand on the basis of patient studies only.