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The effect of the non-conducting substrate of a subdural grid on the scalp electric potential distribution is studied through simulations. Using a detailed head model and the finite element method we show that the governing physics equations predict an important attenuation in the scalp potential for generators located under the grid, and an amplification for generators located under holes in the skull filled with conductive media. These effects are spatially localized and do not cancel each other. A 4×8cm grid can produce attenuations of 2 to 3 times, and an 8×8cm grid attenuation of up to 8 times. As a consequence, when there is no subdural grid, generators of 4 to 8cm 2 produce scalp potentials of the same maximum amplitude as generators of 10 to 20cm 2 under the center of a subdural grid. This means that the minimum cortical extents necessary to produce visible scalp activity determined from simultaneous scalp and subdural recordings can be overestimations. Fil: Von Ellenrieder, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. McGill University. Montreal Neurological Institute and Hospital; Canadá Fil: Beltrachini, Leandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. University of Sheffield; Reino Unido Fil: Muravchik, Carlos Horacio. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina Fil: Gotman, Jean. McGill University. Montreal Neurological Institute and Hospital; Canadá

Summary Objective Interictal high frequency oscillations (HFOs) in the 40–200 Hz range have been identified in scalp electroencephalography (EEG) recordings of patients with focal epilepsy. HFOs usually co-occur with interictal epileptiform discharges (IEDs), and are specific and accurate markers for the epileptic focus, but the brain regions involved when HFOs are generated are unknown. We investigated this question with combined EEG–functional magnetic resonance imaging (fMRI), measuring the blood oxygenation level–dependent (BOLD) signal, and examined HFOs in the gamma (40–80 Hz) and ripple (80–200 Hz) bands. Methods Twenty-eight consecutive patients with focal epilepsy who underwent HFO and EEG-fMRI studies were selected; six were excluded because of negative EEG-fMRI. The remaining 22 patients were divided into two equal groups (11 patients each) based on the frequency of co-occurrence of gamma or ripples with IEDs: low versus high gamma (LG/HG) and low versus high ripples (LR/HR). Results No significant changes were found in the BOLD characteristics between the LG and HG groups. As a group, HR had a larger IED concordant BOLD cluster than the LR group, despite similar IED rates. In addition, the HR group had significantly more thalamic BOLD changes than the LR group (11/11 vs. 2/11). In HR, 5 of 11 patients had thalamic activation only, 4 of 11 had thalamic deactivation only, and 2 of 11 had activation and deactivation in different thalamic regions. In the LR group, 2 of 11 had thalamic activation. The lateralization of thalamic BOLD responses was concordant with the lateralization of cortical ripples in 12 of 13 patients. Significance Scalp IEDs, when frequently accompanied by HFOs in the ripple but not in the gamma band, are associated with larger cortical metabolic responses and with thalamic involvement lateralized to the side of cortical ripples. We propose that a high rate of epileptic ripples is associated with a more active pathologic cortical-thalamocortical network.

Mesial temporal lobe epilepsy (MTLE) is characterized in humans and in animal models by a seizure-free latent phase that follows an initial brain insult; this period is presumably associated to plastic changes in temporal lobe excitability and connectivity. Here, we analyzed the occurrence of interictal spikes and high frequency oscillations (HFOs; ripples: 80–200 Hz and fast ripples: 250–500 Hz) from 48 h before to 96 h after the first seizure in the rat pilocarpine model of MTLE. Interictal spikes recorded with depth EEG electrodes from the hippocampus CA3 area and entorhinal cortex (EC) were classified as type 1 (characterized by a spike followed by a wave) or type 2 (characterized by a spike with no wave). We found that: (i) there was a switch in the distribution of both types of interictal spikes before and after the occurrence of the first seizure; during the latent phase both types of interictal spikes predominated in the EC whereas during the chronic phase both types of spikes predominated in CA3; (ii) type 2 spike duration decreased in both regions from the latent to the chronic phase; (iii) type 2 spikes associated to fast ripples occurred at higher rates in EC compared to CA3 during the latent phase while they occurred at similar rates in both regions in the chronic phase; and (iv) rates of fast ripples outside of spikes were higher in EC compared to CA3 during the latent phase. Our findings demonstrate that the transition from the latent to the chronic phase is paralleled by dynamic changes in interictal spike and HFO expression in EC and CA3. We propose that these changes may represent biomarkers of epileptogenicity in MTLE.

Activation detection in functional Magnetic Resonance Imaging (fMRI) typically assumes the hemodynamic response to neuronal activity to be invariant across brain regions and subjects. Reports of substantial variability of the morphology of blood-oxygenation-level-dependent (BOLD) responses are accumulating, suggesting that the use of a single generic model of the expected response in general linear model (GLM) analyses does not provide optimal sensitivity due to model misspecification. Relaxing assumptions of the model can limit the impact of hemodynamic response function (HRF) variability, but at a cost on model parsimony. Alternatively, better specification of the model could be obtained from a priori knowledge of the HRF of a given subject, but the effectiveness of this approach has only been tested on simulation data. Using fast BOLD fMRI, we characterized the variability of hemodynamic responses to a simple event-related auditory-motor task, as well as its effect on activation detection with GLM analyses. We show the variability to be higher between subjects than between regions and variation in different regions to correlate from one subject to the other. Accounting for subject-related variability by deriving subject-specific models from responses to the task in some regions lead to more sensitive detection of responses in other regions. We applied the approach to epilepsy patients, where task-derived patient-specific models provided additional information compared to the use of a generic model for the detection of BOLD responses to epileptiform activity identified on scalp electro-encephalogram (EEG). This work highlights the importance of improving the accuracy of the model for detecting neuronal activation with fMRI, and the fact that it can be done at no cost to model parsimony through the acquisition of independent a priori information about the hemodynamic response.

Introduction Blood oxygenation level-dependent (BOLD) signal changes at the time of interictal epileptic discharges (IEDs) identify their associated vascular/hemodynamic responses. BOLD activations and deactivations can be found within the epileptogenic zone but also at a distance. Source imaging identifies electric (ESI) and magnetic (MSI) sources of IEDs, with the advantage of a higher temporal resolution. Therefore, the objective of our study was to evaluate the spatial concordance between ESI/MSI and BOLD responses for similar IEDs. Methods Twenty-one patients with similar IEDs in simultaneous electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) and in simultaneous EEG/magnetoencephalogram (MEG) recordings were studied. IEDs in EEG/fMRI acquisition were analyzed in an event-related paradigm within a general linear model (GLM). ESI/MSI of averaged IEDs was performed using the Maximum Entropy on the Mean. We assessed the spatial concordance between ESI/MSI and clusters of BOLD activations/deactivations with surface-based metrics. Results ESI/MSI were concordant with one BOLD cluster for 20/21 patients (concordance with activation: 14/21 patients, deactivation: 6/21 patients, no concordance: 1/21 patients; concordance with MSI only: 3/21, ESI only: 2/21). These BOLD clusters exhibited in 19/20 cases the most significant voxel. BOLD clusters that were spatially concordant with ESI/MSI were concordant with IEDs from invasive recordings in 8/11 patients (activations: 5/8, deactivations: 3/8). Conclusion As the results of BOLD, ESI and MSI are often concordant, they reinforce our confidence in all of them. ESI and MSI confirm the most significant BOLD cluster within BOLD maps, emphasizing the importance of these clusters for the definition of the epileptic focus. Hum Brain Mapp 35:4396–4414, 2014. © 2014 Wiley Periodicals, Inc.

There is evidence that focal epilepsy may involve the dysfunction of a brain network in addition to the focal region. To delineate the characteristics of this epileptic network, we collected EEG/fMRI data from 23 patients with frontal lobe epilepsy. For each patient, EEG/fMRI analysis was first performed to determine the BOLD response to epileptic spikes. The maximum activation cluster in the frontal lobe was then chosen as the seed to identify the epileptic network in fMRI data. Functional connectivity analysis seeded at the same region was also performed in 63 healthy control subjects. Nine features were used to evaluate the differences of epileptic network patterns in three connection levels between patients and controls. Compared with control subjects, patients showed overall more functional connections between the epileptogenic region and the rest of the brain and higher laterality. However, the significantly increased connections were located in the neighborhood of the seed, but the connections between the seed and remote regions actually decreased. Comparing fMRI runs with interictal epileptic discharges (IEDs) and without IEDs, the patient-specific connectivity pattern was not changed significantly. These findings regarding patient-specific connectivity patterns of epileptic networks in FLE reflect local high connectivity and connections with distant regions differing from those of healthy controls. Moreover, the difference between the two groups in most features was observed in the strictest of the three connection levels. The abnormally high connectivity might reflect a predominant attribute of the epileptic network, which may facilitate propagation of epileptic activity among regions in the network., Highlights • Patient-specific patterns of epileptic networks were identified in patients with FLE. • The specific patterns showed local high connectivity and decreased distant links. • IEDs did not change the specific connectivity pattern significantly. • The abnormally high connectivity was a predominant attribute of epileptic network. • The high connectivity facilitates propagation of epileptic activity in FLE.

Neurophysiological studies have reported functional network alterations in epilepsy, most consistently in the theta frequency band. Highly interconnected brain regions (so-called 'hubs') seem to be important in these epileptic networks. High frequency oscillations (HFOs) in intracranial EEG recordings are recently discovered biomarkers that can identify the epileptogenic area and are thought to result from altered neuronal interactions. We studied whether the epileptogenic zone (identified by HFOs and seizure onset zone) is associated with pathological hubs. Bilateral depth electrode recordings from the hippocampus and amygdala were available from twelve patients suspected of temporal lobe epilepsy. HFOs, classified as ripples (80-250 Hz) and fast ripples (250-500 Hz), and epileptiform spikes were marked for all patients in a five-minute epoch of slow-wave sleep. For each channel, we computed hub-measures from a period without epileptiform spikes and found that the epileptogenic zone was associated with a decreased hub-value in the theta frequency band. The amount of HFOs, especially fast ripples, was negatively correlated with the hub-value per channel. Results from post-hoc analyses of other frequency bands, particularly the broad- and gamma frequency band, pointed in the same direction as the results for the theta frequency band. These findings suggest a pathological functional 'isolation' of the epileptogenic zone in the interictal state.

Because seizures originate from different pathological substrates, the question arises of whether distinct or similar mechanisms underlie seizure generation across different pathologies. Better defining intracranial electroencephalographic morphological patterns at seizure-onset could improve the understanding of such mechanisms. To this end, we investigated intracranial electroencephalographic seizure-onset patterns associated with different epileptogenic lesions, and defined high-frequency oscillation correlates of each pattern. We analysed representative seizure types from 33 consecutive patients with drug-resistant focal epilepsy and a structural magnetic resonance imaging lesion (11 mesial temporal sclerosis, nine focal cortical dysplasia, six cortical atrophy, three periventricular nodular heterotopia, three polymicrogyria, and one tuberous sclerosis complex) who underwent depth-electrode electroencephalographic recordings (500 Hz filter, 2000 Hz sampling rate). Patients were included only if seizures arose from contacts located in lesional/peri-lesional tissue, and if clinical manifestations followed the electrographic onset. Seizure-onset patterns were defined independently by two reviewers blinded to clinical information, and consensus was reached after discussion. For each seizure, pre-ictal and ictal sections were selected for high-frequency oscillation analysis. Seven seizure-onset patterns were identified across the 53 seizures sampled: low-voltage fast activity (43%); low-frequency high-amplitude periodic spikes (21%); sharp activity at ≤13 Hz (15%); spike-and-wave activity (9%); burst of high-amplitude polyspikes (6%); burst suppression (4%); and delta brush (4%). Each pattern occurred across several pathologies, except for periodic spikes, only observed with mesial temporal sclerosis, and delta brush, exclusive to focal cortical dysplasia. However, mesial temporal sclerosis was not always associated with periodic spikes nor focal cortical dysplasia with delta brush. Compared to other patterns, low-voltage fast activity was associated with a larger seizure-onset zone (P = 0.04). Four patterns, sharp activity at ≤13 Hz, low-voltage fast activity, spike-and-wave activity and periodic spikes, were also found in regions of seizure spread, with periodic spikes only emerging from mesial temporal sclerosis. Each of the seven patterns was accompanied by a significant increase in high-frequency oscillations upon seizure-onset. Overall, our data indicate that: (i) biologically-distinct epileptogenic lesions share intracranial electroencephalographic seizure-onset patterns, suggesting that different pathological substrates can affect similarly networks or mechanisms underlying seizure generation; (ii) certain pathologies are associated with intracranial electroencephalographic signatures at seizure-onset, e.g. periodic spikes which may reflect mechanisms specific to mesial temporal sclerosis; (iii) some seizure-onset patterns, including periodic spikes, can also be found in regions of spread, which cautions against relying on the morphology of the initial discharge to define the epileptogenic zone; and (iv) high-frequency oscillations increase at seizure-onset, independently of the pattern.

We aim to analyze the fast oscillations in the scalp EEG of focal epilepsy patients with low-to-high rates of interictal epileptiform discharges (IEDs), in order to determine how this neurophysiological feature influences fast oscillation occurrence and their significance as markers of the seizure onset zone (SOZ).Thirty-two patients were studied, subdivided in four categories based on IED frequency: groups A, B and C respectively with high, intermediate and low IED rate, and group D with no IED. Thirty minutes of slow-wave sleep EEG, low-pass filtered at 300Hz and sampled at 1000Hz, were reviewed. IEDs and fast oscillations (gamma activity, 40-80Hz; and ripples,80Hz) were marked. Each channel was classified as inside or outside the irritative zone and the SOZ. We calculated the number and rates of IEDs and fast oscillation, their co-occurrence, their frequency in the irritative zone and SOZ, and the specificity, sensitivity and accuracy to determine the SOZ in the overall population and separately for each group.We analyzed 984 channels. Group A (high IED rate) showed the highest fast oscillation rate (gamma: 0.37±0.73; ripples: 0.17±0.26), followed by group B (gamma: 0.08±0.06; ripples: 0.07±0.05), group C (gamma: 0.06±0.06; ripples: 0.04±0.01), and finally group D, with very low values (gamma: 0.03±0; ripples: 0.03±0). IEDs co-occurred with gamma in 9.5% and with ripples in 3.2%; and gamma and ripples co-occurred with IEDs in 46.2% and 44.4%, respectively. The fast oscillations were more frequent inside than outside the irritative zone and the SOZ (p0.001). Compared to the IEDs, the fast oscillations were less sensitive (sensitivity: IEDs 78%, gamma 66% and ripples 48%) but more specific (specificity: IEDs 50%, gamma 76% and ripples 83%) and accurate (accuracy: IEDs 54%, gamma 74% and ripples 77%) in identifying the SOZ; the same results were reproduced for the different groups separately.This study confirms that fast oscillations can be recorded from the scalp EEG. Gamma activity and ripples are more frequent in patients with frequent IEDs and, in general, inside the irritative zone. However, compared to IEDs, gamma and ripples are less sensitive but more specific and accurate in identifying the SOZ, and this remains in patients with low fast oscillation rates. These findings suggest that IEDs and fast oscillations could share some common neuronal network, but gamma activity and ripples are a better biomarker of epileptogenicity.

In patients with intractable epilepsy, predicting seizures above chance and with clinically acceptable performance has yet to be demonstrated. In this study, an intracranial EEG-based seizure prediction method using measures of similarity with a reference state is proposed.1565 h of continuous intracranial EEG data from 17 patients with mesial temporal lobe epilepsy were investigated. The recordings included 175 seizures. In each patient the data was split into a training set and a testing set. EEG segments were analyzed using continuous wavelet transform. During training, a reference state was defined in the immediate preictal data and used to derive three features quantifying the discrimination between preictal and interictal states. A classifier was then trained in the feature space. Its performance was assessed using testing set and compared with a random predictor for statistical validation.Better than random prediction performance was achieved in 7 patients. The sensitivity was higher than 85%, the warning rate was less than 0.35/h and the proportion of time under warning was less than 30%.Seizures are predicted above chance in 41% of patients using measures of state similarity.Sensitivity and specificity levels are potentially interesting for closed-loop seizure control applications.

Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80–500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures.

Summary Purpose Idiopathic generalized epilepsy (IGE) resistant to treatment is common, but its neuronal correlates are not entirely understood. Therefore, the aim of this study was to examine resting-state default mode network (DMN) functional connectivity in patients with treatment-resistant IGE. Methods Treatment resistance was defined as continuing seizures despite an adequate dose of valproic acid (valproate, VPA). Data from 60 epilepsy patients and 38 healthy controls who underwent simultaneous electroencephalography (EEG) and resting-state functional magnetic resonance imaging (fMRI) were included (EEG/fMRI). Independent component analysis (ICA) and dual regression were used to quantify DMN connectivity. Confirmatory analysis using seed-based voxel correlation was performed. Key Findings There was a significant reduction of DMN connectivity in patients with treatment-resistant epilepsy when compared to patients who were treatment responsive and healthy controls. Connectivity was negatively correlated with duration of epilepsy. Significance Our findings in this large sample of patients with IGE indicate the presence of reduced DMN connectivity in IGE and show that connectivity is further reduced in treatment-resistant epilepsy. DMN connectivity may be useful as a biomarker for treatment resistance.

Clinically, idiopathic generalized epilepsies (IGEs) are defined as any combination of absence seizures, generalized tonic–clinic seizures, and myoclonus or myoclonic seizures; patients with IGEs usually have electroencephalography (EEG) showing generalized spike and wave discharges (GSWDs) and are cognitively normal. The usual presentation age is preteen or teenage years (Commission, 1989). GSWDs and seizures in IGEs are thought to originate from or involve at some point a bilaterally distributed network of cortical and subcortical areas (Bai et al., 2010; Berg et al., 2010; Szaflarski et al., 2010a). However, the origin of the GSWDs and/or seizures in patients with IGEs remains elusive, with some studies pointing to cortical and others to subcortical (i.e., thalamic) onset (Avoli, 2012; Seneviratne et al., 2012). Previously, several theories regarding the origins of GSWD and seizures in IGEs have been postulated based on animal and human data. These theories include the “centrencephalic theory” put forward by Penfield & Jasper (1954), who proposed that the onset of EEG abnormalities in IGEs is in the midline and intralaminar nuclei of the thalamus; this theory was later redefined by the “thalamic clock” theory based on the examination of cortical and thalamic ablations on high-voltage thalamic spindles (Buzsaki, 1991). The two theories of a peripheral IGE onset include “cortical” and “cortical focus” theories, which are based on the measured delays in GSWD propagation between the hemispheres, and the fact that in some animal models of IGE the leading spikes may originate around the cortical somatosensory areas (Meeren et al., 2005). Finally, the corticoreticular theory proposed by Gloor may be the cement that unifies all the above theories—it suggests that the thalamic and brainstem reticular system is responsible for the genesis of the GSWDs that are a result of or represent abnormal oscillations within the corticoreticular neurons and of interaction between cortical and thalamic neurons (Gloor, 1968). Therefore, based on these theories, the notion of cortical and subcortical involvement in the origination of GSWD in IGEs is evident. However, although all these theories are supported by ample experimental and clinical data, none of them address the question of why some patients respond to antiepileptic drugs (AEDs), including valproate (VPA), which is prototypical for these patients, whereas some patients do not. Lack of seizure control in IGEs is observed in approximately 10–30% of adult patients taking AEDs (Gelisse et al., 2001; Baykan et al., 2008; Szaflarski et al., 2010b). Some of these patients continue to have seizures despite optimized therapy and management by an epilepsy specialist (Szaflarski et al., 2008). Many features of IGEs are listed as reasons for medication resistance, and include poor adherence to medication regimen, poor sleep hygiene, or other lifestyle factors called globally “pseudo-resistance,” presence of comorbid psychiatric conditions, presence of generalized tonic–clonic seizures, focal EEG features, or early epilepsy onset (Wolf & Inoue, 1984; Fernando-Dongas et al., 2000; Berg et al., 2001; Szaflarski et al., 2008; Iqbal et al., 2009). However, in many patients these or other reasons for poor seizure control cannot be identified. Overall, it has been noted that patients with IGEs tend to respond better to VPA than to other AEDs, and that response to VPA predicts a positive response to another AED (Fernando-Dongas et al., 2000; Szaflarski et al., 2010b). Therefore, VPA is considered the archetypical AED for patients with IGEs with patients’ response to this drug (or lack thereof) possibly identifying different subtypes of IGEs or even differentiating patients with IGEs from patients with other, possibly focal types of epilepsies (e.g., frontal lobe onset based on the “cortical” theory) (Meeren et al., 2005). Recently, neuroimaging studies have significantly contributed to our understanding of IGEs. Several studies combining EEG with functional magnetic resonance imaging (fMRI; EEG/fMRI) implicated thalamus or specific thalamic nuclei to be involved in GSWD generation more than the cortical regions (Moeller et al., 2008; Tyvaert et al., 2009). Other studies postulated more widespread cortical and subcortical involvement (Aghakhani et al., 2004; Gotman et al., 2005; Hamandi et al., 2006), whereas some studies even suggested that cortical involvement may precede or drive thalamic contribution to the generation of the absence seizures (Bai et al., 2010; Szaflarski et al., 2010a). None of the above studies contradict the theories discussed above, as most if not all of these studies observed interactions between cortical and thalamic activations suggesting again that large cortical and subcortical networks are involved in generation and propagation of GSWD in IGEs. But, none of the above-mentioned studies examined the difference in cortical and subcortical correlates of GSWD generators in patients with VPA-resistant compared to VPA-responsive IGEs. Therefore, in this EEG/fMRI study, we examine the neuroimaging correlates of GSWDs in drug-resistant IGE using EEG/fMRI with VPA as an archetype drug. The central focus of this study is to test the hypothesis that there is a correlation between localization of the blood oxygenation–level dependent (BOLD) signal responses to GSWDs and drug response in patients with IGEs. We hypothesized that in patients with VPA-resistant IGEs, poor seizure control is associated with nonthalamic (possibly cortical) sources of GSWD. By using EEG/fMRI to identify the generators of GSWDs in both groups of IGE patients, we investigated whether there are differences in sources of the GSWDs in patients with VPA-resistant and in VPA-responsive (possibly thalamic) IGEs.

Objective The stage of sleep is a known modulator of high-frequency oscillations (HFOs). For instance, high amplitude slow waves during NREM sleep and the subtypes of REM sleep were shown to contribute to a better separation between physiological and pathological HFOs. This study investigated rates and spatial spread of the different HFO types (physiological and pathological ripples in the 80–250 Hz frequency band, and fast ripples above 250 Hz) depending on time spent in sleep across the different sleep cycles. Methods Fifteen patients with focal pharmaco-resistant epilepsy underwent one night of video-polysomnography during chronic intracranial EEG recording for presurgical epilepsy evaluation. The HFO rate and spread across the different sleep cycles were determined with an automatic HFO detector. We built models to explain the observed rate and spread based on time in sleep and other variables i.e. sleep stage, delta band and sigma band activity, and slow wave amplitude. Statistical significance of the different variables was determined by a model comparison using the Akaike information criterion. Results The rate of HFOs depends significantly on the accumulated time of sleep. As the night advanced, the rate of pathological ripples and fast ripples decreased during NREM sleep (up to 15% per hour spent in the respective sleep stages), while the rate of physiological ripples increased during REM sleep (8% per hour spent in REM sleep). Interestingly, the stage of sleep but not the sleep cycle determined the extent of spread of HFOs, showing a larger field during NREM sleep and a more restricted field during REM sleep. Conclusion The different dependence with sleep time for physiological and pathological ripples is in keeping with their distinct underlying generating mechanisms. From a practical point of view, the first sleep cycle seems to be best suitable for studying HFOs in epilepsy, given that the contrast between physiological and pathological ripple rates is largest during this time., Highlights • The rate of the different HFO types depends on the duration of sleep. • The rate of pathological HFOs decreases during NREM sleep as the night advances. • The rate of physiological HFOs increases during REM sleep as the night advances. • The stage of sleep determines the extent of spread of the different HFO types. • Given the largest contrast, HFOs are best studied during the first sleep cycle.

Objective Experimental research demonstrated that distinct underlying mechanisms go along with different seizure-onset patterns on EEG. These different mechanisms may reflect different tissue abnormalities which, we hypothesize, could also be reflected in morphological differences in the interictal epileptic and background EEG activity. Methods We searched our database of intracranial EEG recordings for mesiotemporal lobe epilepsy patients with either predominant low-voltage fast activity (LVF) or periodic spiking (PS). Interictal epileptiform discharges (IEDs) were characterized by the spike/polyspike amplitude, the amplitude of the post-spike slow wave, and the associated low-frequency and high-frequency power increases. The EEG background was assessed with the root mean square amplitude, the distribution of the instantaneous amplitude relative to the root mean square value, and the power spectral density. Results We identified 18 patients with predominant LVF or PS. IEDs in PS were 1.7-times sharper as determined by the high-frequency power increase than IEDs in LVF. In contrast, IEDs in LVF had a 1.2-times higher post-spike slow wave amplitude, and a 1.5-times larger low-frequency power content than IEDs in PS. There was no difference in rates of IEDs/HFOs, spike amplitude, HFO co-occurrence, and EEG background. Conclusions We demonstrated an association between the morphology of IEDs and the type of the seizure-onset pattern in mesiotemporal lobe epilepsy. Significance Our findings therefore suggest that IED morphology is a marker of the underlying mechanisms of seizure generation.

Purpose: To examine the ability of the BOLD response to EEG spikes to assess the epileptogenicity of the lesion in patients with focal cortical dysplasia (FCD). Method: Patients with focal epilepsy and FCD who underwent 3T EEG-fMRI from 2006 to 2010 were included. Diagnosis of FCD was based on neuroradiology (MRI+), or histopathology in MRI-negative cases (MRI−). Patients underwent 120 min EEG-fMRI recording session. Spikes similar to those recorded outside the scanner were marked in the filtered EEG. The lesion (in MRI+) or the removed cortex (in MRI−) was marked on the anatomical T1 sequence, blindly to the BOLD response, after reviewing the FLAIR images. For each BOLD response we assessed the concordance with the spike field and with the lesion in MRI+ or the removed cortex in MRI−. BOLD responses were considered “concordant” if the maximal t-value was inside the marking. Follow-up after resection was used as gold-standard. Results: Twenty patients were included (13 MRI+, 7 MRI−), but in seven the EEG was not active or there were artifacts during acquisition. In all 13 studied patients, at least one BOLD response was concordant with the spike field; in 9/13 (69%) at least one BOLD response was concordant with the lesion: in 6/7 (86%) MRI+ and in 3/6 (50%) MRI− patients. Conclusions: Our study shows a high level of concordance between FCD and BOLD response. This data could provide useful information especially for MRI negative patients. Moreover, it shows in almost all FCD patients, a metabolic involvement of remote cortical or subcortical structures, corroborating the concept of epileptic network.

Structural and functional imaging studies in focal epilepsy often reveal distributed regions of abnormality. These are interpreted as representing the existence of epileptic networks, but the presence of actual neuronal interactions between these regions has not been demonstrated. We sought to determine whether the distributed hemodynamic responses often seen in functional magnetic resonance imaging (fMRI) studies of scalp interictal epileptic discharges (IEDs) actually correspond to synchronized neuronal activities when examining the intracerebral electroencephalogram (iEEG) at distant nodes of the network.We studied 28 patients who underwent first EEG-fMRI and then iEEG, and had significant hemodynamic responses in the gray matter. We coregistered the hemodynamic responses to the iEEG electrode contact positions and analyzed synchrony, measured by correlation, between IEDs recorded by iEEG in regions with and without hemodynamic responses.The synchrony of intracerebral IED activity between pairs of regions showing a hemodynamic response was higher compared to that between pairs of regions without (p 0.0001) and between pairs of regions, one with and one without hemodynamic response (p 0.0001). These differences were found during the interictal periods with IEDs but were absent during the interictal periods without IEDs. Higher synchrony was also observed between regions involved at seizure onset (p 0.0001).EEG-fMRI studies are unique in their ability to reveal hemodynamic concomitants of IEDs anywhere in the brain. This study proves that iEEG activity is synchronized between these regions of hemodynamic response, thus demonstrating the existence of an actual neuronally based interictal epileptic network. This also validates the EEG-fMRI approach to reveal this network noninvasively. Ann Neurol 2017;82:57-66.

Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named “shared and specific independent component analysis” (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i.e., components that could be considered “specific” for a group or condition. Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences. We examined SSICA robustness with respect to the number of allowable extracted specific components and between-group orthogonality assumptions. Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-level t-statistics maps based on SSICA results. We also evaluated the consistency and specificity of the extracted specific components by SSICA. The results on realistic simulated and real fMRI data showed that SSICA outperforms the regular group ICA approach in terms of reconstruction and classification performance. We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state fMRI. Our findings in task fMRI show that SSICA confirms results of the general linear model (GLM) analysis and when combined with clustering analysis, it complements GLM findings by providing additional information regarding the reliability and specificity of networks.

Objective It has been reported that interictal epileptic discharges (IEDs) recorded in temporal regions on scalp EEG are unlikely to originate from mesial temporal structures. However, EEG-fMRI sometimes show mesial temporal activation. We hypothesized that BOLD activation in the temporal neocortex is weaker than in the mesial structures, reflecting the fact that propagated activity has less metabolic demand than the original discharge. Methods Twelve patients with epilepsy who have BOLD response in mesial temporal structures were selected from our EEG-fMRI database. We searched the temporal lobe ipsilateral to IEDs and checked whether there is positive BOLD response in the neocortex. Results All IED types showed a BOLD response in the temporal neocortex ipsilateral to the mesial temporal BOLD response. T-values were higher in mesial temporal structures than in neocortex in 13/16 cases. Conclusions Hemodynamic changes were observed in the mesial temporal lobe at the time of IEDs recorded from the temporal region on the scalp. The finding of smaller BOLD changes in the ipsilateral neocortex is in agreement with our hypothesis. Significance Our study indicates that scalp-recorded temporal lobe spikes are likely to result from mesial temporal spikes propagating neuronally to the neocortex.

OBJECTIVE: To assess whether there is a difference in the background activity in the ripple band (80-200Hz) between epileptic and non-epileptic channels, and to assess whether this difference is sufficient for their reliable separation. METHODS: We calculated mean and standard deviation of wavelet entropy in 303 non-epileptic and 334 epileptic channels from 50 patients with intracerebral depth electrodes and used these measures as predictors in a multivariable logistic regression model. We assessed sensitivity, positive predictive value (PPV) and negative predictive value (NPV) based on a probability threshold corresponding to 90% specificity. RESULTS: The probability of a channel being epileptic increased with higher mean (p=0.004) and particularly with higher standard deviation (p

Recent studies have applied the new magnetic resonance encephalography (MREG) sequence to the study of interictal epileptic discharges (IEDs) in the electroencephalogram (EEG) of epileptic patients. However, there are no criteria to quantitatively evaluate different processing methods, to properly use the new sequence.We evaluated different processing steps of this new sequence under the common generalized linear model (GLM) framework by assessing the reliability of results. A bootstrap sampling technique was first used to generate multiple replicated data sets; a GLM with different processing steps was then applied to obtain activation maps, and the reliability of these maps was assessed.We applied our analysis in an event-related GLM related to IEDs. A higher reliability was achieved by using a GLM with head motion confound regressor with 24 components rather than the usual 6, with an autoregressive model of order 5 and with a canonical hemodynamic response function (HRF) rather than variable latency or patient-specific HRFs. Comparison of activation with IED field also favored the canonical HRF, consistent with the reliability analysis.The reliability analysis helps to optimize the processing methods for this fast fMRI sequence, in a context in which we do not know the ground truth of activation areas. Magn Reson Med 78:370-382, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

We investigate to what degree the synchronous activation of a smooth patch of cortex is necessary for observing EEG scalp activity. We perform extensive simulations to compare the activity generated on the scalp by different models of cortical activation, based on intracranial EEG findings reported in the literature. The spatial activation is modeled as a cortical patch of constant activation or as random sets of small generators (0.1 to 3cm(2) each) concentrated in a cortical region. Temporal activation models for the generation of oscillatory activity are either equal phase or random phase across the cortical patches. The results show that smooth or random spatial activation profiles produce scalp electric potential distributions with the same shape. Also, in the generation of oscillatory activity, multiple cortical generators with random phase produce scalp activity attenuated on average only 2 to 4 times compared to generators with equal phase. Sparse asynchronous cortical generators can produce measurable scalp EEG. This is a possible explanation for seemingly paradoxical observations of simultaneous disorganized intracranial activity and scalp EEG signals. Thus, the standard interpretation of scalp EEG might constitute an oversimplification of the underlying brain activity.

Identification of consistent distinguishing features between preictal and interictal periods in the EEG is an essential step towards performing seizure prediction. We propose a novel method to separate preictal and interictal states based on the analysis of the high frequency activity of intracerebral EEGs in patients with mesial temporal lobe epilepsy.Wavelet energy and entropy were computed in sliding window fashion from preictal and interictal epochs. A comparison of their organization in a 2 dimensional space was carried out using three features quantifying the similarities between their underlying states and a reference state. A discriminant analysis was then used in the features space to classify epochs. Performance was assessed based on sensitivity and false positive rates and validation was performed using a bootstrapping approach.Preictal and interictal epochs were discriminable in most patients on a subset of channels that were found to be close or within the seizure onset zone.Preictal and interictal states were separable using measures of similarity with the reference state. Discriminability varies with frequency bands.This method is useful to discriminate preictal from interictal states in intracerebral EEGs and could be useful for seizure prediction.

Electroencephalography–functional magnetic resonance imaging (EEG-fMRI) is a noninvasive technique that allows visualizing the brain’s hemodynamic changes associated with epileptic activity. This method utilizes the high temporal resolution of EEG and the high spatial resolution of fMRI to study metabolic activity of the whole brain, simultaneously. EEG-fMRI has become a valuable tool in the presurgical evaluation of patients with pharmacoresistant epilepsy and to study epileptic networks (Zijlmans et al., 2007; Gotman, 2008; Thornton et al., 2010). Group analyses of EEG-fMRI scans from selected sets of patients demonstrate metabolic changes that may not be apparent in individual patients. For instance, studies of patients with generalized epilepsies show common bilateral activation in cortical regions, thalami, and cerebellum, whereas the caudate nuclei and default mode network (DMN) regions were deactivated (Gotman et al., 2005; Carney et al., 2010; Siniatchkin et al., 2011). The extent of networks involved in focal epileptic activity is much less clear; group analysis of EEG-fMRI has been limited to studies of temporal lobe epilepsy (TLE) and heterogeneous groups of focal epilepsy patients and using 1.5 Tesla scans (Laufs et al., 2007; Kobayashi et al., 2009; Laufs et al., 2011). A group analysis of 19 patients with different focal epileptic syndromes converged to a single activation cluster in the ipsilateral anterior piriform cortex (Laufs et al., 2011). We report the epileptic networks associated with focal interictal epileptiform discharges (IEDs) using 3T EEG-fMRI. We selected patients with TLE, frontal lobe epilepsy (FLE), and posterior quadrant epilepsy (PQE) and performed group analyses. We describe blood oxygen–level dependent (BOLD) responses of cortical, subcortical, and DMN regions differentially involved in each patient group.

Mesial temporal lobe epilepsy (MTLE) is the most common focal epilepsy in adults, and hippocampal sclerosis the most frequent lesion in these patients. Seizures are often resistant to antiepileptic drugs, but many patients can be cured by resecting the epileptogenic zone (Thadani et al., 1995). To offer surgical resection, it is important to accurately localize the epileptogenic zone and understand the networks affected by it. Patients with left MTLE usually present verbal memory deficits, whereas those with right MTLE exhibit deficits of nonverbal (visual) memory (Kim et al., 2003). In addition to these dysfunctions related to damage in mesial temporal structures, other cognitive abilities are impaired: verbal comprehension, perceptual organization, academic achievement, language, and visuospatial function (Hermann et al., 1997; Jones-Gotman et al., 2010). Finally, patients often present psychiatric comorbidities, such as anxiety and depression (Shukla et al., 1979; de Oliveira et al., 2010). Magnetic resonance imaging (MRI) can identify in vivo hippocampal sclerosis (atrophy and increased T2 signal), but refined MRI analyses, such as voxel-based morphometry, have demonstrated that patients also exhibit abnormalities outside the hippocampus, not easily detectable by inspection of diagnostic images (Bonilha et al., 2010): decreased gray matter volume in other limbic structures (amygdala, fornix, entorhinal cortex), in the ipsilateral and contralateral temporal neocortex, and in extratemporal regions, such as thalamus, cerebellum, frontal lobe areas and cingulate gyrus, and occipital regions (Moran et al., 2001; Bernasconi et al., 2004; Riederer et al., 2008). Abnormalities affect a network of regions functionally or anatomically connected to the hippocampus, probably resulting in the complex cognitive and behavioral conditions. The mechanisms underlying brain damage in MTLE remain largely unknown. One theory hypothesizes that seizure propagation determines the distribution of damage (Riederer et al., 2008). Another suggests that loss of hippocampal connections results in deafferentation and neuronal damage; for instance, structures directly connected to the hippocampus, like the thalamus, show atrophy (Bonilha et al., 2010). Changes in the level of neuronal activity are accompanied by changes in the concentration of deoxyhemoglobin in the blood; this is measured through the blood oxygen level–dependent (BOLD) effect with functional MRI (fMRI, Ogawa et al., 1992). Biswal et al. (1995) observed that functionally related brain regions show temporal correlations in the low frequency component of the BOLD signal and that such correlations occur during tasks and also at rest, revealing several functional networks (e.g., attention, vision, and motor system). Functional connectivity detects zones that exhibit correlated BOLD fluctuations and, as a result, belong to the same functional network (Greicius et al., 2003). Reduced functional connectivity has been reported for several pathologic conditions including Alzheimer’s disease, multiple sclerosis, and autism (Fox & Raichle, 2007). In MTLE, studies showed connectivity changes and impaired functional networks: in left MTLE, Waites et al. (2006), using language regions as seed, found disrupted connectivity in language areas. In unilateral MTLE, an impairment within and between hippocampi was shown by Pereira et al. (2010). Bettus et al. (2009) observed decreased connectivity within mesial structures in the affected temporal lobe and contralateral increased connectivity. Other studies have suggested that perceptual (Zhang et al., 2009a) and attention networks (Zhang et al., 2009b) are impaired in MTLE. In this study, we compare fMRI connectivity of patients with MTLE with healthy subjects by using an approach more specific to mesial temporal structures than the ones used in previous studies. We expect that patients will show reduced connectivity, the interest being to determine in what regions.

Objective—We aim to automatically detect fast oscillations (40–200 Hz) related to epilepsy on scalp EEG recordings. Methods—The detector first finds localized increments of the signal power in narrow frequency bands. A simple classification based on two features, a narrowband to wideband signal amplitude ratio and an absolute narrowband signal amplitude, then allows for an important reduction in the number of false positives. Results—When compared to an expert, the performance in 15 focal epilepsy patients resulted in 3.6 false positives per minute at 95% sensitivity, with at least 40% of the detected events being true positives. In most of the patients the channels showing the highest number of events according to the expert and the automatic detector were the same. Conclusions—A high sensitivity is achieved with the proposed automatic detector, but results should be reviewed by an expert to remove false positives. Significance—The time required to mark fast oscillations on scalp EEG recordings is drastically reduced with the use of the proposed detector. Thus, the automatic detector is a useful tool in studies aiming to create a better understanding of the fast oscillations visible on the scalp., Facultad de Ingeniería

The discovery that electroencephalography (EEG) contains useful information at frequencies above the traditional 80Hz limit has had a profound impact on our understanding of brain function. In epilepsy, high-frequency oscillations (HFOs, >80Hz) have proven particularly important and useful. This literature review describes the morphology, clinical meaning, and pathophysiology of epileptic HFOs. To record HFOs, the intracranial EEG needs to be sampled at least at 2,000Hz. The oscillatory events can be visualized by applying a high-pass filter and increasing the time and amplitude scales, or EEG time-frequency maps can show the amount of high-frequency activity. HFOs appear excellent markers for the epileptogenic zone. In patients with focal epilepsy who can benefit from surgery, invasive EEG is often required to identify the epileptic cortex, but current information is sometimes inadequate. Removal of brain tissue generating HFOs has been related to better postsurgical outcome than removing the seizure onset zone, indicating that HFOs may mark cortex that needs to be removed to achieve seizure control. The pathophysiology of epileptic HFOs is challenging, probably involving populations of neurons firing asynchronously. They differ from physiological HFOs in not being paced by rhythmic inhibitory activity and in their possible origin from population spikes. Their link to the epileptogenic zone argues that their study will teach us much about the pathophysiology of epileptogenesis and ictogenesis. HFOs show promise for improving surgical outcome and accelerating intracranial EEG investigations. Their potential needs to be assessed by future research.

We aim to analysis the relationship between HFOs-generating regions and the seizure onset zone (SOZ) in epileptic patients without a visible lesion on MRI.Intracerebral EEGs were recorded in 17 patients with intractable focal seizures and normal MRIs. The rates of interictal HFOs and spikes inside and outside the SOZ were analyzed as well as the specificity, sensitivity and accuracy of HFOs and spikes to determine the SOZ.The mean rate of spikes, ripples and fast ripples (FR) was higher in the SOZ than in the non-SOZ channels. In regard to the identification of the SOZ the sensitivity was 91% for spikes, 91% for ripples and 66% for FR, the specificity was 30% for spikes, 42% for ripples and 80% for FR, and the accuracy was 44% for spikes, 54% for ripples and 76% for FR.The rates of spikes and HFOs were higher inside than outside the SOZ. However, HFOs are also more specific and accurate than spikes to delineate the SOZ.Analysis of interictal HFOs during 5-10 min of sleep recording is a good tool to localize the SOZ in patients with epilepsy and normal MRI, and could potentially reduce the duration of chronic intracerebral EEG recordings.

The combining of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a unique noninvasive method for investigating the brain regions involved at the time of epileptic discharges. The neuronal discharges taking place during an interictal spike or spike-wave burst result in an increase in metabolism and blood flow, which is reflected in the blood oxygen–level dependent (BOLD) signal measured by fMRI. This increase is most intense in the region generating the discharge but is also present in regions affected by the discharge. On occasion, epileptic discharges result in decreased metabolism, the origin of which is only partially understood. EEG-fMRI applied to focal epilepsy results in maxima of the BOLD signal most often concordant with other methods of localization and has been shown to help in localizing epileptic foci in nonlesional frontal lobe epilepsy. It has also demonstrated the involvement of the thalamus in generalized epileptic discharges. In patients with new-onset epilepsy it could be used to evaluate the source and extent of the brain structures involved during discharges and their evolution as the disease progresses.

Summary Two-dimensional temporal clustering analysis (2D-TCA) is a relatively new functional MRI (fMRI) based technique that breaks blood oxygen level dependent activity into separate components based on timing and has shown potential for localizing epileptic activity independently of electroencephalography (EEG). 2D-TCA has only been applied to detect epileptic activity in a few studies and its limits in detecting activity of various forms (i.e. activation size, amplitude, and frequency) have not been investigated. This study evaluated 2D-TCA's ability to detect various forms of both simulated epileptic activity and EEG-fMRI activity detected in patients. When applied to simulated data, 2D-TCA consistently detected activity in 6 min runs containing 5 spikes/run, 10 spikes/run, and one 5 s long event with hemodynamic response function amplitudes of at least 1.5%, 1.25%, and 1% above baseline respectively. When applied to patient data, while detection of interictal spikes was inconsistent, 2D-TCA consistently produced results similar to those obtained by EEG-fMRI when at least 2 prolonged interictal events (a few seconds each) occurred during the run. However, even for such cases it was determined that 2D-TCA can only be used to validate localization by other means or to create hypotheses as to where activity may occur, as it also detects changes not caused by epileptic activity.

Functional magnetic resonance imaging (fMRI) uses the blood oxygen level dependent (BOLD) effect to map the neuronal activity linked to a particular event (Ogawa et al., 1992). Simultaneous electroencephalography (EEG) and fMRI, EEG/fMRI, is a noninvasive technique that aims to detect hemodynamic changes in the brain related to interictal epileptic activity identified on scalp EEG. Combining the high temporal resolution of EEG signal with the high spatial resolution of BOLD images, EEG/fMRI has been shown to be useful to characterize various forms of focal and generalized epileptic discharges (Gotman et al., 2006). The quantitative measure of the BOLD response, the hemodynamic response function (HRF), has been derived from the auditory response in event-related BOLD fMRI measured in healthy subjects (Glover, 1999). It has been demonstrated that the HRF to epileptic spikes is most often not significantly different from that physiologic response (Lemieux et al., 2001). The BOLD changes are thought to result from synchronized neuronal activity arising at the time of the spike and are usually detected with a delay of 4–6 s (Benar et al., 2002; Kobayashi et al., 2006). In some studies, HRFs with different shapes (Benar et al., 2002; Lemieux et al., 2008) and peak times (Gotman et al., 2006; Jacobs et al., 2007) have been observed. Unexpectedly, BOLD changes occurring or at least starting before the spike and not detected by standard analysis have been described in patients with focal (Hawco et al., 2007; Jacobs et al., 2009) and generalized epilepsy (Moeller et al., 2008). Similar early hemodynamic changes preceding the visible neuronal epileptic activity were described in animal models (Makiranta et al., 2005; Brevard et al., 2006) and supported by near infrared spectroscopy studies in humans (Roche-Labarbe et al., 2008) and in rats (Osharina et al., 2010). We will refer to a BOLD response that occurs at least in part earlier than the corresponding EEG event, and, therefore, cannot result exclusively from it, as an early BOLD response. Although from the first descriptions of this early BOLD response some hypotheses about its origin have been formulated (Hawco et al., 2007), the pathophysiology of this phenomenon is still not understood. It has been first hypothesized that the early BOLD response corresponds to a change in metabolic activity not resulting from a change in neuronal activity. An alternative hypothesis is that the early BOLD response corresponds to a synchronized neuronal event that is not visible in the scalp EEG but occurs a few seconds before the visible epileptic discharge. Quantitative studies demonstrated the absence of a subtle scalp EEG change corresponding to the early BOLD activity (Jacobs et al., 2009). The aim of this study is to characterize the origin and the electrophysiologic correlate of the early BOLD signal in a group of patients with focal refractory seizures who underwent both EEG/fMRI and stereo-EEG (SEEG). In such patients we looked for electrographic changes in the SEEG recordings preceding the scalp interictal epileptic discharge (that for practical reason we will call spike) to determine if the early BOLD response corresponds to a synchronized neural activity invisible on the scalp EEG, but detectable with intracerebral electrodes.

Resume Introduction De nombreux arguments cliniques, metaboliques et electrophysiologiques suggerent l’existence d’un etat precritique, transition entre l’etat intercritique et la crise. Etat des connaissances L’analyse de l’EEG intracrânien par des methodes mathematiques a montre l’existence de changements de la dynamique cerebrale plusieurs minutes avant la survenue de crises partielles. Ces modifications dynamiques precritiques ne semblent pas limitees au foyer epileptogene, ce qui explique qu’elles puissent aussi etre apprehendees par l’EEG de scalp et dans des epilepsies partielles qu’un foyer epileptogene soit ou non identifie. Plusieurs scenarios peuvent rendre compte de la periode precritique : recrutement progressif des neurones, comme le suggerent des analyses a un niveau cellulaire, ou etat facilitateur, a haute probabilite de survenue de crises, comme le suggerent des analyses a l’echelle de reseaux neuronaux. Aucune methode de prediction des crises ne peut aujourd’hui etre proposee aux patients, car le taux de faux-positifs reste trop important. Perspectives Une etude multicentrique europeenne ( Evolving platform for improving living expectation of patients suffering from IctAl events [EPILEPSIAE]) evalue actuellement une combinaison de plus de 44 methodes d’analyse des signaux EEG et ECG, sur des enregistrements issus d’une vaste base de donnees. Conclusion D’importants efforts doivent etre poursuivis en combinant les analyses de signaux a de multiples niveaux : EEG intracrânien, microelectrodes intracerebrales, EEG de scalp, analyse in vitro de tissus post-operatoires.

Idiopathic generalized epilepsy (IGE) is characterized by electroencephalography (EEG) recordings with generalized spike wave discharges (GSWDs) typically arising from normal background activity. Animal models of IGE show that cortico-subcortical-cortical loops play an important role in the generation of GSWDs (Gloor, 1968; Meeren et al., 2002; Slaght et al., 2004; Timofeev & Steriade, 2004; Paz et al., 2005). In humans, EEG–functional magnetic resonance imaging (fMRI) studies in patients with IGE show that thalamus, default mode areas (Raichle & Mintun, 2006), and caudate nuclei are involved in short GSWD paroxysms (Aghakhani et al., 2004; Gotman et al., 2005; Hamandi et al., 2006) and absence seizures (Salek-Haddadi et al., 2003; Labate et al., 2005; Laufs et al., 2006; Moeller et al., 2008a). Although GSWDs are the result of highly synchronized activity in the thalamocortical network (Blumenfeld, 2005), EEG without GSWDs is believed to represent normal baseline brain activity. However, it is possible that subtle pathological thalamocortical interactions persist during baseline activity without GSWDs, which might facilitate GSWDs. Such baseline activity can be investigated in functional connectivity studies: Spontaneous slow blood oxygen level dependent (BOLD) fluctuation (

High-frequency (HF) changes were analysed in relation to anatomical origin of spikes, shape and occurrence within the seizure onset zone (SOZ). We evaluated whether HF changes are linked to the SOZ, as established for distinct high-frequency oscillations.SEEG was filtered at 500 Hz and sampled at 2000 Hz. Spikes were selected by shape (spike/spike-slow wave) and location (SOZ/non-SOZ and neocortex/amygdala/hippocampus) in 15 patients. About 50 spikes were averaged for each set. Changes compared to baseline were quantified with false discovery rate controlled t-statistics using time-frequency spectra. Counts of increased or decreased time-frequency values were compared across spike categories in the 80-250 and 250-500 Hz bands.Seventy-seven spike types were analysed. Differences between spike categories were most prominent between 250 and 500 Hz. HF changes were more frequent and larger in mesial temporal than in neocortical spikes and for spikes with slow waves than spikes alone. HF changes above 250 Hz were more frequent in spikes within than outside the SOZ.HF increases above 250 Hz show regional differences and are very prominent in the SOZ. Hippocampal spikes have the strongest HF components.Analysis of HF changes during spikes may provide information on differing pathophysiological mechanisms of spikes and on epileptogenicity of the tissue.

The development of methods that help to recover electroencephalography (EEG) signal recorded in the harsh environment of the magnetic resonance imaging (MRI) scanner allowed EEG–functional MRI (fMRI) to become a feasible and a potentially useful clinical tool to study epileptic activity (Ives et al., 1993; Benar et al., 2003). EEG-fMRI combines the high temporal resolution of EEG signal with the high spatial resolution of blood oxygen level dependent (BOLD) MRI images. An increasing number of studies describe the interictal epileptic discharge (IED) –related BOLD signal changes and show that these measures are useful to characterize various forms of focal and generalized epilepsy and may help in the presurgical evaluation of patients with refractory focal seizures (Zijlmans et al., 2007; Jacobs et al., 2008b; Tyvaert et al., 2008; Jacobs et al., 2009; Moeller et al., 2009). However, the reproducibility of EEG-fMRI results has scarcely been addressed in the literature (Krakow et al., 1999; Salek-Haddadi et al., 2006) and needs to be established. A good quality of the images and of the EEG recording, as well as frequently occurring spikes are essential to obtain good EEG-fMRI results (Gotman, 2008; Walker et al., 2010). Results have been reported for a variety of epileptic discharges. For instance, a significant BOLD response has been shown to be present for temporal lobe spikes in 83% of cases (Kobayashi et al., 2006a) and in 93% of idiopathic generalized epilepsy (IGE) discharges (Aghakhani et al., 2004). These results have been consistently found by different research centers (Salek-Haddadi et al., 2006). The BOLD response is usually an activation, which may be followed by a deactivation. Less often, this BOLD response consists only of a deactivation (Kobayashi et al., 2006b). On the other hand, some scans yield no significant BOLD response despite frequent epileptic events (Salek-Haddadi et al., 2006). In this study, we addressed a fundamental question about the potential use of EEG-fMRI as a clinical tool: How reproducible are the results? We performed a controlled scan–rescan study of epileptic activity in patients with focal or generalized epilepsy, in order to show to what extent EEG-fMRI results, independently from the etiology or pathogenesis, could be reproduced. Higher magnetic field MR has significantly contributed to better structural imaging (Alvarez-Linera, 2008) and is known to improve the signal-to-noise ratio of fMRI studies (Kruger et al., 2001). However, the yield of this improvement has not been systematically evaluated in the clinical context of epileptic discharges. To address this issue, we also examined the possible advantage of using a higher magnetic field (3T) MRI scanner over the more standard 1.5T in a subgroup of our patients.

We analyzed with EEG-video monitoring the epileptic activity recorded during the latent and chronic periods in rats undergoing 30 or 120 min pilocarpine-induced convulsive status epilepticus (SE). Interictal discharges frequency in the entorhinal cortex (EC) of animals exposed to 120 min SE was significantly higher in the chronic than in the latent period. Following seizure appearance, interictal spikes diminished in duration in the CA3 of the 120 min SE group, and occurred at higher rates in the amygdala in all animals. Rats exposed to 120 min SE generated shorter seizures but presented twice as many non-convulsive seizures per day as the 30 min group. Finally, seizures most frequently initiated in CA3 in the 120 min SE group but had similar onset in CA3 and EC in the 30 min group. These findings indicate that convulsive SE duration influences the development of interictal and ictal activity, and that interictal discharges undergo structure-specific changes after seizure appearance.

Absences are characterized by an abrupt onset and ending of generalized 3–4 Hz spike and wave discharges (GSWs), accompanied by a variable degree of unresponsiveness. Typically absences arise from a normal electroencephalography (EEG) background leading to the question how the brain switches from a normal to a highly synchronized brain activity. Animal models of absence seizures showed that corticosubcortical-cortical loops play an important role in the generation of GSWs (Gloor, 1968; Meeren et al., 2002; Slaght et al., 2004; Timofeev & Steriade, 2004; Paz et al., 2005). Although cortex and thalamus are involved in the generation of 3 Hz GSW discharges, it is still unclear which structure initiates the cascade of processes underlying absence seizures (Blumenfeld, 2005). Animal studies in genetic models of absence epilepsy strongly suggest that GSWs are triggered in a restricted region of the cerebral cortex (Meeren et al., 2002; Klein et al., 2004; Manning et al., 2004; Polack et al., 2007), more specifically in the face region of the somatosensory cortex (Meeren et al., 2002; Klein et al., 2004; Polack et al., 2007). In humans, combined recordings of electroencephalography (EEG) and functional MRI (fMRI) allow the noninvasive study of structures involved in absences. Previous EEG-fMRI studies in patients with idiopathic generalized epilepsy (IGE) showed that thalamus, default mode areas (Raichle & Mintun, 2006), and caudate nuclei are involved in short GSW paroxysms (Aghakhani et al., 2004; Gotman et al., 2005; Hamandi et al., 2006) and absence seizures (Salek-Haddadi et al., 2003; Labate et al., 2005; Laufs et al., 2006; Moeller et al., 2008a). Because previous studies on absence seizures were analyzed in a block design, the contribution of thalamus, default mode areas caudate nucleus, and the frontal cortex throughout the absences remained unclear in these studies. A dynamic analysis of absences could contribute to the understanding of initiation, maintenance, and termination of absence seizures. In this study we investigated dynamic changes during absences in a sliding window analysis yielding a new F-map every 2.5 s.

The in vitro brain slice preparation has been extensively used to study the mechanisms underlying epileptiform synchronization and epileptogenesis (Prince, 1999; Traub et al., 1999), as well as the action of antiepileptic drugs (AEDs) (Schneiderman & Schwartzkroin, 1982; Heinemann et al., 1985; Fueta & Avoli, 1992; Watts & Jefferys, 1993; Zhang et al., 1995). Early work performed in isolated rat hippocampal slices treated with the K+ channel blocker 4-aminopyridine (4AP) has shown in young tissue that several AEDs can abolish ictal-like discharges at concentrations that are unable to influence shorter, interictal-like events (Fueta & Avoli, 1992). This study also demonstrated that AEDs reduced interictal-like activity in adult tissue only at very high doses (Fueta & Avoli, 1992). Similar findings have been later obtained from combined adult rat hippocampus-entorhinal cortex (EC) slices. Thus, Bruckner and Heinemann (2000) have reported that prolonged ictal-like discharges that are initiated by EC networks are blocked by AEDs, whereas interictal-like activity continues to occur under these conditions. In addition, Bruckner et al. (1999) have found that recurrent, interictal-like activity generated by EC networks during concomitant application of 4AP and bicuculline [which is known to antagonize γ-aminobutyric acid (GABA)A receptors] is insensitive to AEDs. Because recurrent epileptiform discharges induced by Mg2+-free medium are also unaffected by AEDs at a time when GABAA receptor–mediated inhibition is presumably weakened (Whittington et al., 1995; Zhang et al., 1995), it has been proposed that the inability of AEDs to influence the occurrence of these epileptiform events represents an in vitro model of pharmacoresistance (Zhang et al., 1995; Dreier et al., 1998). These studies also raised the interesting possibility that weakened inhibition may cause refractoriness to pharmacologic treatment in epileptic patients. The EC plays a role in the initiation of limbic seizures in patients presenting with temporal lobe epilepsy (Rutecki et al., 1989; Spencer & Spencer, 1994; Bartolomei et al., 2001) and in animal models mimicking this disorder (Dreier & Heinemann, 1991; Bragdon et al., 1992; Avoli et al., 1996; Nagao et al., 1996). Hence, we further analyzed here the effects induced by the AEDs carbamazepine (CBZ), topiramate (TPM), and valproate (VPA) on the epileptiform activity generated by rat EC slices superfused with 4AP-containing medium. Our findings demonstrate that at nearly therapeutic doses, all AEDs abolished prolonged (>3 s) ictal-like epileptiform discharges resembling electrographic limbic seizures, but failed to block shorter epileptiform events reminiscent of the interictal activity. In addition, by using repetitive stimulation at different rates in the presence of 4AP, we found evidence indicating that such difference in AED efficacy may mainly reflect an activity-dependent mechanism rather than specific neuronal changes such as a decrease in GABAA receptor–mediated inhibition.

Thirty percent to 40% of patients with focal epilepsy are medically intractable,1 and for some, surgical removal of epileptogenic areas is the best option to gain seizure freedom. Intracranial electroencephalographic (iEEG) investigations are indicated for patients in whom noninvasive methods fail to identify a single focal seizure generator.2 iEEG is used to define the seizure onset zone (SOZ).3 Removal of the SOZ alone, however, does not always predict the surgical benefit.4,5 It is uncertain whether the outcome can be improved by removing areas of interictal spiking, often more widespread than the SOZ.6,7 Intracranial studies also have limitations, as their results depend on electrode location and type of implantation (intracortical vs subdural). For instance, iEEG electrodes only record neuronal activity in their direct vicinity and are blind for other areas,8 making it hard to judge whether the activity at seizure onset really represents the seizure generator or is the result of propagation from else-where. Thus the actual focus and its extent may be missed, leading to unsuccessful surgery. Microelectrode-recorded high-frequency oscillations (HFOs), ripples (80 –250Hz), and fast ripples (FRs, 250 –500Hz), were found predominantly in epileptogenic tissue.9 –11 They can also be recorded with macroelectrodes during clinical iEEG investigation.12,13 HFOs were more specific in indicating the SOZ than spikes.14 Additionally, they were linked to the SOZ independently of the underlying lesion and were infrequent in lesional areas outside the SOZ.15 Evidence therefore suggests that HFOs are good markers of epileptic tissue and may help to identify epileptogenic areas. We hypothesize that removing areas generating HFOs results in good surgical outcome. The correlation between removal of HFO-generating areas and seizure outcome was compared to that coming from spikes and to the current gold standard, removing the SOZ.

We measured metabolic changes associated with temporal lobe (TL) spikes using combined electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We selected 18 patients with temporal lobe epilepsy (TLE) who underwent a 2-h simultaneous EEG-fMRI and had unilateral or bilateral independent TL spikes for interindividual group analysis, in order to identify consistent blood oxygenation level dependent (BOLD) responses to TL spikes.EEG was postprocessed and spikes were visually identified. fMRI data were preprocessed with motion correction, spatial smoothing, and removal of low frequency drifts. Spike timings were used as events for fMRI statistical analysis. Four hemodynamic response functions were used to account for variability in the BOLD response.Group analysis revealed common areas of BOLD activations and deactivations. The hemodynamic response function (HRF) peaking 3 s after the spike showed activation involving ipsilaterally the mesial temporal structures (presumably the hippocampus), putamen/globus pallidus, inferior insula, and superior temporal gyrus. The HRF peaking at 5 s showed activations involving ipsi- and contralaterally the superior temporal gyrus and inferior insula. Both HRFs showed bilateral posterior cingulate deactivations.We disclosed involvement of a network of activated areas during unilateral TL spikes, including ipsilateral mesial temporal structures, basal ganglia, and bilateral neocortical temporal regions. Despite the low temporal resolution of fMRI we demonstrated that contralateral temporal involvement occurred later than ipsilateral activation. This contralateral change took place in the absence of visible EEG changes. The posterior cingulate deactivation may reflect the interconnections between this region and other limbic structures. It may also partially correspond to a suspension of the default mode network, as previously described for TL spikes.

In this study, we proposed and evaluated the use of Independent Component Analysis (ICA) combining the EEG dipole model to automatically remove eye movement artifacts from the EEG without needing EOG as a reference. We separated the EEG data into independent components using the ICA method, and determined the source localization of these independent components with a single dipole model. The EEG signal was reconstructed by automatically excluding those components localized within a preset eye model. EEGs from 12 patients were analyzed. The experimental results indicate that ICA with the dipole model is very efficient at automatically subtracting the eye movement artifacts, while retaining the EEG slow waves and making their interpretation easier.

High frequency oscillations (HFOs) have been associated with epileptogenicity. In rats, the extent of HFOs (>200 Hz) is correlated with seizure frequency. We studied whether the same applies to patients with focal epilepsy. Thirty-nine patients with intracerebral EEG sampled at 2000 Hz were studied for interictal ripples (80–250 Hz), fast ripples (FR, 250–500 Hz) and spikes. Seizure frequency before implantation was compared to numbers of channels with HFOs (>1/min). Analyses were repeated for HFO rates of >5, >10 and >20. Separate analyses were done for 25 patients with temporal lobe epilepsy only and for a selection of similar unilateral temporal channels in 12 patients. No linear correlation or trend was found relating the number of channels with HFOs and seizure frequency. There was a linear positive correlation between the number of channels with more than 20 FRs/min and seizure frequency. The hypothesis that the more tissue generating HFOs, the higher the seizure frequency, was not confirmed, though there might be a correlation for high FR rates.

Approximately one-third of epileptic patients have inadequate seizure control through anticonvulsant medications. For these patients the unforeseeable way in which seizures occur represents the most disabling and dangerous aspect of their disease. Even if seizures seem to occur suddenly and without warning in most patients, there has been a longstanding discussion on whether seizures may be building up slowly in the hours or minutes before the clinical event (Le Van Quyen et al., 1999; Lehnertz et al., 1999; Litt et al., 2001). Some patients describe unspecific premonitory symptoms hours before the seizure (Schulze-Bonhage et al., 2006; Haut et al., 2007), and several studies detected electroencephalography (EEG) changes during the preictal period (for review see Mormann et al., 2007). However, the presence and time-frame of consistent EEG changes prior to seizures remain uncertain and no method of seizure prediction can consistently predict seizures in different patients (Schelter et al., 2006; Mormann et al., 2007). The conventional range of EEG analysis involves frequencies below 100 Hz, but studies over the last decade suggest that localized higher frequencies may also be important. Frequencies above 100 Hz have been extensively characterized in human epileptic mesial temporal structures using depth microelectrodes. Whereas 100–200 Hz oscillations appear related to physiologic memory processing, higher frequencies, between 200 and 500 Hz, are associated with epileptogenic tissue (Bragin et al., 1999). These high frequency oscillations (HFOs) have been termed ripples (80–250 Hz) and fast ripples (250–500 Hz). Recently depth macroelectrodes and spectral and visual analysis techniques have also revealed focal HFOs in humans during interictal (Urrestarazu et al., 2006, 2007; Jacobs et al., 2008; Worrell et al., 2008) and ictal recordings (Jirsch et al., 2006; Ochi et al., 2007; Ramachandrannair et al., 2008). Discrete HFOs occurred mainly in regions of seizure onset and rarely in regions of secondary spread in mesial temporal as well as neocortical seizures. Moreover, no ictal high frequency activities occurred in the seizures of patients with poor localization (Jirsch et al., 2006). In animal studies, a clear relationship between the presence of HFOs and their degree of activity with spontaneous seizures could be shown (Bragin et al., 2004). In an in vitro model of low-Mg2+ seizures, an increase of HFOs preceded seizure activity (Khosravani et al., 2005). In rats, an increase of ripple and fast ripple bands could be observed within the dentate gyrus 1 s before seizure onset (Bragin et al., 2005). The behavior of HFOs in the preictal period in epilepsy patients has been evaluated in only one study (Khosravani et al., 2008), which found changes in the seconds preceding seizures. The possibility that HFOs may reflect basic epileptogenic processes and changes during the preictal period bears scientific and clinical interests. For instance, potential therapies involving EEG-triggered anticonvulsant injections or electrical stimulation could result in seizure control. Such antiseizure therapies may have greater chance of success if seizures could be predicted from the interictal EEG (Elger, 2001; Osorio et al., 2005). We hypothesized that HFOs in the range of 80–500 Hz measured using depth macroelectrodes in patients with intractable temporal and extratemporal epilepsy change in frequency of occurrence during the preictal state. Visual and spectral analyses techniques were used to assess HFO rates and band power in the 15 min prior to seizure onset. These two methods were chosen to detect changes in high frequency power in general but also in distinct HFOs. Distinct HFOs are very short events and, therefore, a change in their rate may not be detectable with spectral analysis.

Epileptic seizures are due to abnormal synchronized neuronal discharges. Techniques measuring electrical changes are commonly used to analyze seizures. Neuronal activity can be also defined by concomitant hemodynamic and metabolic changes. Simultaneous electroencephalogram (EEG)-functional MRI (fMRI) measures noninvasively with a high-spatial resolution BOLD changes during seizures in the whole brain. Until now, only a static image representing the whole seizure was provided. We report in 10 focal epilepsy patients a new approach to dynamic imaging of seizures including the BOLD time course of seizures and the identification of brain structures involved in seizure onset and discharge propagation. The first activation was observed in agreement with the expected location of the focus based on clinical and EEG data (three intracranial recordings), thus providing validity to this approach. The BOLD signal preceded ictal EEG changes in two cases. EEG-fMRI may detect changes in smaller and deeper structures than scalp EEG, which can only record activity form superficial cortical areas. This method allowed us to demonstrate that seizure onset zone was limited to one structure, thus supporting the concept of epileptic focus, but that a complex neuronal network was involved during propagation. Deactivations were also found during seizures, usually appearing after the first activation in areas close or distant to the activated regions. Deactivations may correspond to actively inhibited regions or to functional disconnection from normally active regions. This new noninvasive approach should open the study of seizure generation and propagation mechanisms in the whole brain to groups of patients with focal epilepsies.

EEG-fMRI is a non-invasive technique that allows the investigation of epileptogenic networks in patients with epilepsy. Lately, BOLD changes occurring before the spike were found in patients with generalized epilepsy. The study of metabolic changes preceding spikes might improve our knowledge of spike generation. We tested this hypothesis in patients with idiopathic and symptomatic focal epilepsy. Eleven consecutive patients were recorded at 3 T: five with idiopathic focal and 6 with symptomatic focal epilepsy. Thirteen spike types were analyzed separately. Statistical analysis was performed using the timing of spikes as events, modeled with HRFs peaking between − 9 s and + 9 s around the spike. HRFs were calculated the most focal BOLD response. Eleven of the thirteen studies showed prespike BOLD responses. Prespike responses were more focal than postspike responses. Three studies showed early positive followed by later negative BOLD responses in the spike field. Three had early positive BOLD responses in the spike field, which remained visible in the later maps. Three others had positive BOLD responses in the spike field, later propagating to surrounding areas. HRFs peaked between − 5 and + 6 s around the spike timing. No significant EEG changes could be identified prior to the spike. BOLD changes prior to the spike frequently occur in focal epilepsies. They are more focal than later BOLD changes and strongly related to the spike field. Early changes may result from increased neuronal activity in the spike field prior to the EEG spike and reflect an event more localized than the spike itself.

High frequency oscillations (HFOs) called ripples (80-250 Hz) and fast ripples (FR, 250-500 Hz) can be recorded from intracerebral EEG macroelectrodes in patients with intractable epilepsy. HFOs occur predominantly in the seizure onset zone (SOZ) but their relationship to the underlying pathology is unknown. It was the aim of this study to investigate whether HFOs are specific to the SOZ or result from pathologically changed tissue, whether or not it is epileptogenic. Patients with different lesion types, namely mesial temporal atrophy (MTA), focal cortical dysplasia (FCD) and nodular heterotopias (NH) were investigated. Intracranial EEG was recorded from depth macroelectrodes with a sampling rate of 2000 Hz. Ripples (80-250 Hz) and Fast Ripples (250-500 Hz) were visually marked in 12 patients: five with MTA, four with FCD and three with NH. Rates of events were statistically compared in channels in four areas: lesional SOZ, non-lesional SOZ, lesional non-SOZ and non-lesional non-SOZ. HFO rates were clearly more linked to the SOZ than to the lesion. They were highest in areas in which lesion and SOZ overlap, but in patients with a SOZ outside the lesion, such as in NHs, HFO rates were clearly higher in the non-lesional SOZ than in the inactive lesions. No specific HFO pattern could be identified for the different lesion types. The findings suggest that HFOs represent a marker for SOZ areas independent of the underlying pathology and that pathologic tissue changes alone do not lead to high rates of HFOs.

Objective: High-frequency oscillations (HFOs) can be recorded in epileptic patients with clinical intracranial EEG. HFOs have been associated with seizure genesis because they occur in the seizure focus and during seizure onset. HFOs are also found interictally, partly co-occurring with epileptic spikes. We studied how HFOs are influenced by antiepileptic medication and seizure occurrence, to improve understanding of the pathophysiology and clinical meaning of HFOs. Methods: Intracerebral depth EEG was partly sampled at 2,000 Hz in 42 patients with intractable focal epilepsy. Patients with five or more usable nights of recording were selected. A sample of slow-wave sleep from each night was analyzed, and HFOs (ripples: 80–250 Hz, fast ripples: 250–500 Hz) and spikes were identified on all artifact-free channels. The HFOs and spikes were compared before and after seizures with stable medication dose and during medication reduction with no intervening seizures. Results: Twelve patients with five to eight nights were included. After seizures, there was an increase in spikes, whereas HFO rates remained the same. Medication reduction was followed by an increase in HFO rates and mean duration. Conclusions: Contrary to spikes, high-frequency oscillations (HFOs) do not increase after seizures, but do so after medication reduction, similarly to seizures. This implies that spikes and HFOs have different pathophysiologic mechanisms and that HFOs are more tightly linked to seizures than spikes. HFOs seem to play an important role in seizure genesis and can be a useful clinical marker for disease activity. AED = antiepileptic drug; CBZ = carbamazepine; CLOB = clobazam; FR = fast ripple; FR_isol = fast ripples without co-occurring spikes; FR_Sp = fast ripples with co-occurring spikes; GBP = gabapentin; HFO = high-frequency oscillation; Lai/s = left anterior inferior/superior electrode (porencephalic cyst); LEV = levetiracetam; LF/p/a = left frontal/posterior/anterior electrode; LOP = left frontal operculum electrode; Lpi/s = left posterior inferior/superior electrode; L/RA = left/right amygdale electrode; L/RC/a/s = left/right cingulate/anterior/superior electrode; L/RE = left/right epidural electrode; L/RH = left/right hippocampus electrode; L/ROF = left/right orbitofrontal electrode; L/RO/i/s = left/right occipital/infracalcine/supracalcine electrode; L/RP = left/right parahippocampus electrode; L/RS = left/right supramarginal gyrus electrode; LSMAa/p = left supplementary motor area anterior/posterior electrode; LT = left anteriotemporal electrode; LTG = lamotrigine; OXC = oxcarbamazepine; PRI = primidone; PTH = phenytoin; R = ripple; R_isol = ripples without co-occurring spikes; R_Sp = ripples with co-occurring spikes; SEEG = stereo-EEG; SEZ = one or more seizures; SOZ = seizure onset zone; Sp = spike; TPM = topiramate.

Biological data often takes the form of regular grids of samples, e.g., obtained by magnetic resonance imaging (MRI). When the finite-element method is used to predict the electromagnetic fields in volumes specified in this way, the data is first transformed to a geometric model, and then the model is subdivided into finite elements. These steps are expensive and can be unreliable. An alternative is proposed that avoids both steps. A regular mesh of rectangular finite elements is superimposed on the MRI grid. Each element may straddle boundaries between different tissues, but the basis functions are constructed in such a way that they respect the material interfaces. The new method is applied to the forward problem in electroencephalography. A circular head model and a model derived from real MRI data are analyzed with the new method. Sampled potentials at the surface of the scalp compare well with those obtained using the conventional finite-element method.

In many patients with epilepsy, the area responsible for seizure generation, or the seizure onset zone (SOZ), may be difficult to define. Even in patients with identifiable lesions on brain MR imaging, noncongruent clinical and laboratory studies often indicate poor localization of the SOZ, which precludes a surgical approach in those patients with refractory epilepsy. Invasive intracranial EEG evaluation may help in those cases to finally obtain a good definition of the SOZ (Diehl & Lueders, 2000). These methods, however, do not always result in the finding of one clear SOZ, as seizures might be originating from more than one brain area and also because intracranial techniques are always spatially limited. For instance, seizures originating from areas not covered by electrodes but propagating to the actual electrode positions might lead to misinterpretation. Additionally, interictal epileptiform discharges (IED) or spikes, which define the irritative zone (IZ), might be seen in several electrode positions outside the SOZ; it remains unclear how these discharges are related to epileptogenesis (Hufnagel et al., 2000; Rosenow & Lueders, 2000) and how much importance one should put on them to define the epileptogenic area. It is known that patients with spikes generated in multiple brain areas are less likely to become seizure free after surgery than patients with well-localized spikes (Bautista et al., 1999). This apparent multiplicity of epileptic generators may represent widespread disease or might be related to secondary epileptogenesis (Rosenow & Lueders, 2000). Therefore, new measurements for epileptogenicity are required additionally to spikes and the SOZ in stereo EEG (SEEG). Traditionally, EEG oscillations are believed to be relevant up to frequencies in the gamma band (40–100 Hz) but recent findings suggest that high-frequency oscillations (HFOs) ranging between 100 and 500 Hz might be closely linked to epileptogenesis. They were studied in rodents as well as in humans using microwires (with a surface contact of 70 μm2) (Bragin et al., 1999; Staba et al., 2002) and more recently detected also in humans by using macroelecrode contacts, with a surface contact of 0.8 mm2 (Jirsch et al., 2006; Urrestarazu et al., 2006, 2007). HFOs ranging from 100 to 250 Hz and described as ripples were seen in the hippocampus (Hc) and entorhinal cortex of normal rats (Chrobak & Buzsaki, 1996; Chrobak et al., 2000). In humans, similar oscillations generally showed a lower frequency range, between 80 and 160 Hz, and were found within epileptic tissue and outside in the less affected Hc of epileptic patients studied with bilateral temporal lobe intracerebral depth electrodes (Bragin et al., 1999; Staba et al., 2004). It was hypothesized that they are physiological rhythms closely linked to memory consolidation (Draguhn et al., 2000), but additional “pathological” ripples could be observed as well (Bragin et al., 2004). The differentiation between pathological and physiological events remains unclear (Le Van Quyen et al., 2006). HFOs between 250 and 500 Hz, called fast ripples, have been recorded from normal rodent and human brains (Curio et al., 1994, 1997; Jones et al., 2000; Ikeda et al., 2005). It was hypothesized that they are related to somatosensory stimulation and sensory information processing (Curio et al., 1997; Gobbele et al., 2004). Nevertheless, fast ripples in mesial temporal structures were more clearly linked to epileptogenesis than ripples (Staba et al., 2002). Ripples and fast ripples occur frequently during IEDs and may reflect pathological hypersynchronous events. In general, these HFOs are seen more frequently during non-rapid-eye movement sleep (NREM) sleep compared to rapid-eye movement sleep (REM) sleep or wakefulness (Staba et al., 2004). Recently, it has been shown that HFOs may not exclusively be recorded from microwires but also from intracranial macroelectrodes (Jirsch et al., 2006; Urrestarazu et al., 2006). During ictal recordings, HFOs could be identified and occurred mostly in the region of primary epileptogenesis and less frequently in areas of secondary spread (Jirsch et al., 2006). Analysis of interictal recordings showed a clear relation between spikes and fast ripples and again a close relation between HFOs and the SOZ (Urrestarazu et al., 2007). While most fast ripples were noted during spikes, others could be found independently. Fast ripples were more restricted to the presumed SOZ than ripples. In the studies with microwires, the relationship between HFOs and spikes was not specifically addressed but most examples of fast ripples show themoccurring during spikes. The exact relationship between interictal discharges and HFOs, so far, remains unclear. All previous studies focused on channels that showed spiking activity in the first place. It is therefore unknown whether HFOs in general are produced by the same generators as IEDs or derive from independent structures. HFOs have never been measured in interictally inactive channels of epileptic patients, and their presence in these channels might provide additional information on epileptogenesis. Furthermore, HFOs occurring within spiking channels but independently of spikes need to be investigated more closely to gain information about the value of HFOs for the identification of the SOZ independently of spikes. In this study we analyzed HFOs in spiking and nonspiking channels, looking at interictal intracerebral SEEG recordings of 10 patients with lesional focal epilepsy. Differences between HFOs occurring at the same time or independent of spikes, as well as the exact relationship between spikes and HFOs were investigated. We also compared the capacity of the limbic and neocortical (Nc) structures to generate fast oscillations, and, finally, we determined if HFOs could help identify the SOZ. We hypothesize that HFOs occur to a large extent independently of spikes and can add additional localizing information to the SEEG investigation.