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Summary Purpose: To assess the time course of increases in N-acetyl-aspartate/creatine (NAA/Cr), which can be measured using proton MR spectroscopic imaging (1H-MRSI), in patients with intractable nonlesional temporal lobe epilepsy (TLE) after successful epilepsy surgery. Methods: We performed pre- and postoperative 1H-MRSI in 16 seizure-free (SF) patients and 16 not seizure-free (NSF) TLE patients. We calculated a mixed-design analysis of variance (ANOVA) between SF and NSF groups, ipsi- and contralateral to the side of operation, and pre- and postoperative NAA/Cr measurements. We applied nonlinear regression between pre- and postoperative NAA/Cr differences and the time interval between 1H-MRSI scans to fit a negative exponential model to NAA recovery. Results: Mixed-design ANOVA revealed that (a) postoperative NAA/Cr was significantly higher in SF than in NSF patients (p = 0.02) and that (b) in the SF group, postoperative NAA/Cr values were significantly higher than preoperative values (p

Malformations of cortical development (MCDs) are commonly complicated by intractable focal epilepsy. Epileptogenesis in these disorders is not well understood and may depend on the type of MCD. The cellular mechanisms involved in interictal and ictal events are notably different, and could be influenced independently by the type of pathology. We evaluated the relationship between interictal and ictal zones in eight patients with different types of MCD in order to better understand the generation of these activities: four had nodular heterotopia, two focal cortical dysplasia and two subcortical band heterotopia (double-cortex). We used the non-invasive EEG-fMRI technique to record simultaneously all cerebral structures with a high spatio-temporal resolution. We recorded interictal and ictal events during the same session. Ictal events were either electrical only or clinical with minimal motion. BOLD changes were found in the focal cortical dysplasia during interictal and ictal epileptiform events in the two patients with this disorder. Heterotopic and normal cortices were involved in BOLD changes during interictal and ictal events in the two patients with double cortex, but the maximum BOLD response was in the heterotopic band in both patients. Only two of the four patients with nodular heterotopia showed involvement of a nodule during interictal activity. During seizures, although BOLD changes affected the lesion in two patients, the maximum was always in the overlying cortex and never in the heterotopia. For two patients intracranial recordings were available and confirm our findings. The dysplastic cortex and the heterotopic cortex of band heterotopia were involved in interictal and seizure processes. Even if the nodular gray matter heterotopia may have the cellular substrate to produce interictal events, the often abnormal overlying cortex is more likely to be involved during the seizures. The non-invasive BOLD study of interictal and ictal events in MCD patients may help to understand the role of the lesion in epileptogenesis and also determine the potential surgical target.

It is not easy to determine the location of the cerebral generators and the other brain regions that may be involved at the time of an epileptic spike seen in the scalp EEG. The possibility to combine EEG recording with functional MRI scanning (fMRI) opens the opportunity to uncover the regions of the brain showing changes in metabolism and blood flow in response to epileptic spikes seen in the EEG. These regions are presumably involved in the abnormal neuronal activity at the origin of epileptic discharges. This paper reviews the methodology involved in performing such studies, including the special techniques required for recording the EEG inside the scanner and the statistical issues in analyzing the fMRI signal. We then discuss the results obtained in patients with different types of focal epileptic disorders and in patients with primary generalized epilepsy. The results in general indicate that interictal epileptic discharges may affect brain areas well beyond the presumed region in which they are generated. The noninvasive nature of this method opens new horizons in the investigation of brain regions involved and affected by epileptic discharges.

In order to analyze where epileptic spikes are generated, we assessed the level of concordance between EEG source localization using distributed source models and simultaneous EEG-fMRI which measures the hemodynamic correlates of EEG activity. Data to be compared were first estimated on the same cortical surface and two comparison strategies were used: (1) MEM-concordance: a comparison between EEG sources localized with the Maximum Entropy on the Mean (MEM) method and fMRI clusters showing a significant hemodynamic response. Minimal geodesic distances between local extrema and overlap measurements between spatial extents of EEG sources and fMRI clusters were used to quantify MEM-concordance. (2) fMRI-relevance: estimation of the fMRI-relevance index alpha quantifying if sources located in an fMRI cluster could explain some scalp EEG data, when this fMRI cluster was used to constrain the EEG inverse problem. Combining MEM-concordance and fMRI-relevance (alpha) indexes, each fMRI cluster showing a significant hemodynamic response (p

This study examined BOLD changes prior to interictal discharges in the EEG of patients with epilepsy. From a database of 143 EEG-fMRI studies, we selected the 16 data sets that showed both strong fMRI activation in the original analysis and only a single spike type in the EEG. Scans were then analyzed using seven model HRFs, peaking 3 or 1 s before the event, or 1, 3, 5, 7, or 9 s after it. An HRF was calculated using a deconvolution method for all activations seen in each analysis. The results showed that seven data sets had HRFs that peaked 1 s after the event or earlier, indicating a BOLD change starting prior to the spike seen on the scalp EEG. This is surprising since the BOLD change is expected to result from the spike. For most of the data sets with early peaking HRFs, the maximum activation in all of the statistical maps was when the model HRF peaked 1 s after the event, suggesting that the early activation was at least as important as any later activation. We suggest that this early activity is the result of neuronal changes occurring several seconds prior to a surface EEG event, but that these changes are not visible on the scalp. This is the first report of a BOLD response occurring several seconds prior to an interictal event seen on the scalp and could have important implications for our understanding of the generation of epileptic discharges.

Summary Background Cerebral cavernous angiomas (CCA) are frequently associated with focal intractable epilepsy. Epileptogenicity is usually attributed to hemosiderin deposits. Combined recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) is a new method to investigate epileptic discharges. Methods We used EEG-fMRI to identify BOLD responses to interictal spikes in patients with CCA. Responses were analyzed in the lesional and perilesional area, and at distance. Results Eight patients were scanned, all with temporal lobe epilepsy and spikes. One patient had bilateral spikes, analyzed separately, and three studies with less than two spikes were excluded. All six studies analyzed showed BOLD responses. No response was found in the lesion itself or immediate periphery, and the raw fMRI signal showed signal loss in lesional and perilesional areas in all patients. Responses were close to the perilesional area in 2/6 studies. All patients showed responses at a distance from the lesions. Conclusions Despite susceptibility artifacts caused by iron deposits, we demonstrated EEG-fMRI responses involving the perilesional areas in 33% of studies. We also demonstrated diffuse rather than focal responses, in accordance with previous studies in lesional and non-lesional temporal lobe epilepsy, confirming the frequent distant effect of epileptic spikes.

Low-voltage fast (LVF) and hypersynchronous (HYP) patterns are the seizure-onset patterns most frequently observed in intracranial EEG recordings from mesial temporal lobe epilepsy (MTLE) patients. Both patterns also occur in models of MTLE in vivo and in vitro, and these studies have highlighted the predominant involvement of distinct neuronal network/neurotransmitter receptor signaling in each of them. First, LVF-onset seizures in epileptic rodents can originate from several limbic structures, frequently spread, and are associated with high-frequency oscillations in the ripple band (80–200 Hz), whereas HYP onset seizures initiate in the hippocampus and tend to remain focal with predominant fast ripples (250–500 Hz). Second, in vitro intracellular recordings from principal cells in limbic areas indicate that pharmacologically induced seizure-like discharges with LVF onset are initiated by a synchronous inhibitory event or by a hyperpolarizing inhibitory postsynaptic potential barrage; in contrast, HYP onset is associated with a progressive impairment of inhibition and concomitant unrestrained enhancement of excitation. Finally, in vitro optogenetic experiments show that, under comparable experimental conditions (i.e., 4-aminopyridine application), the initiation of LVF- or HYP-onset seizures depends on the preponderant involvement of interneuronal or principal cell networks, respectively. Overall, these data may provide insight to delineate better therapeutic targets in the treatment of patients presenting with MTLE and, perhaps, with other epileptic disorders as well.

OBJECTIVES The significance of hippocampal sleep spindles and their relation to epileptic activity is still a matter of controversy. Hippocampal spindles have been considered a physiological phenomenon, an evoked response to afferent epileptic discharges, or even the expression of an epileptic manifestation. To address this question, we investigated the presence and rate of hippocampal spindles in focal pharmacoresistant epilepsy patients undergoing scalp-intracerebral electroencephalography (EEG). DESIGN Sleep recording with scalp-intracerebral EEG. SETTING Tertiary referral epilepsy center. PATIENTS Twenty-five epilepsy patients (extratemporal: n = 6, temporal: n = 15, and multifocal including the temporal lobe: n = 4). INTERVENTIONS N/A. MEASUREMENTS AND RESULTS We analyzed associations between hippocampal spindles and hippocampal electrophysiological findings (interictal spiking, seizure onset zone) and magnetic resonance imaging volumetry. Sixteen of 25 patients (64%) had hippocampal spindles (extratemporal epilepsy: 6/6; temporal epilepsy: 10/15; and multifocal epilepsy: 0/4; P = 0.005). Median spindle rate was 0.6 (range, 0.1-8.6)/min in nonrapid eye movement sleep. Highest spindle rates were found in hippocampi of patients with extratemporal epilepsy (P < 0.001). A negative association was found between hippocampal spiking activity and spindle rate (P = 0.003). We found no association between the presence (n = 21) or absence (n = 17) of hippocampal seizure onset zone and hippocampal spindle rate (P = 0.114), and between a normal (n = 30) or atrophic (n = 8) hippocampus and hippocampal spindle rate (P = 0.195). CONCLUSIONS Hippocampal spindles represent a physiological phenomenon, with an expression that is diminished in epilepsy affecting the temporal lobe. Hippocampal spiking lowered the rate of hippocampal spindles, suggesting that epileptic discharges may at least in part be a transformation of these physiological events, similar to the hypothesis considering generalized spike-and-waves a transformation of frontal spindles.

Functional hubs are defined as the specific brain regions with dense connections to other regions in a functional brain network. Among them, connector hubs are of great interests, as they are assumed to promote global and hierarchical communications between functionally specialized networks. Damage to connector hubs may have a more crucial effect on the system than does damage to other hubs. Hubs in graph theory are often identified from a correlation matrix, and classified as connector hubs when the hubs are more connected to regions in other networks than within the networks to which they belong. However, the identification of hubs from functional data is more complex than that from structural data, notably because of the inherent problem of multicollinearity between temporal dynamics within a functional network. In this context, we developed and validated a method to reliably identify connectors and corresponding overlapping network structure from resting-state fMRI. This new method is actually handling the multicollinearity issue, since it does not rely on counting the number of connections from a thresholded correlation matrix. The novelty of the proposed method is that besides counting the number of networks involved in each voxel, it allows us to identify which networks are actually involved in each voxel, using a data-driven sparse general linear model in order to identify brain regions involved in more than one network. Moreover, we added a bootstrap resampling strategy to assess statistically the reproducibility of our results at the single subject level. The unified framework is called SPARK, i.e. SParsity-based Analysis of Reliable k-hubness, where k-hubness denotes the number of networks overlapping in each voxel. The accuracy and robustness of SPARK were evaluated using two dimensional box simulations and realistic simulations that examined detection of artificial hubs generated on real data. Then, test/retest reliability of the method was assessed using the 1000 Functional Connectome Project database, which includes data obtained from 25 healthy subjects at three different occasions with long and short intervals between sessions. We demonstrated that SPARK provides an accurate and reliable estimation of k-hubness, suggesting a promising tool for understanding hub organization in resting-state fMRI.

Our inability to adequately treat many patients with refractory epilepsy caused by focal cortical dysplasia (FCD), surgical inaccessibility and failures are significant clinical drawbacks. The targeting of physiologic features of epileptogenesis in FCD and colocalizing functionality has enhanced completeness of surgical resection, the main determinant of outcome. Electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) and magnetoencephalography are helpful in guiding electrode implantation and surgical treatment, and high-frequency oscillations help defining the extent of the epileptogenic dysplasia. Ultra high-field MRI has a role in understanding the laminar organization of the cortex, and fluorodeoxyglucose-positron emission tomography (FDG-PET) is highly sensitive for detecting FCD in MRI-negative cases. Multimodal imaging is clinically valuable, either by improving the rate of postoperative seizure freedom or by reducing postoperative deficits. However, there is no level 1 evidence that it improves outcomes. Proof for a specific effect of antiepileptic drugs (AEDs) in FCD is lacking. Pathogenic mutations recently described in mammalian target of rapamycin (mTOR) genes in FCD have yielded important insights into novel treatment options with mTOR inhibitors, which might represent an example of personalized treatment of epilepsy based on the known mechanisms of disease. The ketogenic diet (KD) has been demonstrated to be particularly effective in children with epilepsy caused by structural abnormalities, especially FCD. It attenuates epigenetic chromatin modifications, a master regulator for gene expression and functional adaptation of the cell, thereby modifying disease progression. This could imply lasting benefit of dietary manipulation. Neurostimulation techniques have produced variable clinical outcomes in FCD. In widespread dysplasias, vagus nerve stimulation (VNS) has achieved responder rates >50%; however, the efficacy of noninvasive cranial nerve stimulation modalities such as transcutaneous VNS (tVNS) and noninvasive (nVNS) requires further study. Although review of current strategies underscores the serious shortcomings of treatment-resistant cases, initial evidence from novel approaches suggests that future success is possible.

To evaluate the possibility of detecting fast ripples (FRs) on the surface EEG of patients with focal pharmacoresistant epilepsy, and to investigate the relationship between scalp FRs and localization of the seizure onset zone (SOZ). We included 10 patients undergoing combined surface-intracranial EEG with ≥10 spikes in the surface EEG during the first 30 consecutive minutes of N3 sleep. FRs (≥4 consecutive oscillations above 250 Hz with an amplitude clearly exceeding that of the background) on the surface EEG (F3-C3, C3-P3, Fz-Cz, Cz-Pz, F4-C4, C4-P4) were visually marked, and verified by two EEG experts. FRs were categorized as related to the SOZ, if localized in the brain lobe of the SOZ. Low-amplitude FRs with a rate of 0.09/min were found in 6/10 patients: two exhibited events related to the SOZ, three showed no relationship with the SOZ, and in one patient the SOZ was not identified. It may be possible to detect FRs with surface EEG using subdermal electrodes in patients with focal epilepsy. The relationship between surface FRs and the SOZ remains unclear. Future studies aiming at a higher spatial EEG coverage are needed to elucidate their significance.

EEG-fMRI is an established technique to allow mapping BOLD changes in response to interictal discharges recorded in the EEG of epilepsy patients. Traditional fMRI experiments rely on an echo planar imaging (EPI) sequence with a time resolution given by its time-to-repetition (TR) of ~2 s. Recently, multiple fast fMRI sequences have been developed to get around the limited temporal resolution of the EPI sequence, and achieved a TR in the 100 ms range or lower. One such sequence is called Magnetic Resonance EncephaloGraphy (MREG). Its high temporal resolution should offer increased detection sensitivity and statistical power in the context of epilepsy studies and in fMRI experiments in general. The aim of this work was to investigate the advantages and disadvantages offered by MREG. This was done by superimposing artificial event-related BOLD responses on EPI and MREG background signals, from 5 epileptic patients, that were free of epileptic discharges (spikes) on simultaneously recorded EEG. These functional datasets simulated different spiking rates and hemodynamic response amplitudes, and were analyzed with the commonly used General Linear Model (GLM) with the canonical hemodynamic response function (HRF) as a fixed model of the response. Robustness to violation of the assumptions of the GLM was additionally assessed with similar simulations using variable spike-to-spike response amplitudes and 8 non-canonical HRFs. Consistent with previous work, MREG yields higher maximum statistical t-values than EPI, but our simulations showed these statistics to be inflated, as the false positive rate at a standard threshold was high. At thresholds set to appropriately control specificity, EPI showed better true positive rate and larger cluster size than MREG. However, the lack of an appropriate calibration of the amplitude of the responses across the sequences precludes definitive judgment on their relative sensitivity. In addition, we show that a mismatch between the assumed and actual HRF impairs more MREG detection performance, but that EPI is more affected by non-modeled spike-to-spike variations of response amplitude. Filtering-out physiological noise, which is not aliased at the fast sampling rate of MREG, and the modeling of temporal autocorrelation are advantageous in increasing the detection power of MREG. This simulation study 1) warrants care when interpreting statistical t-values from fast fMRI sequences, 2) proposes thresholds for valid inferences and processing methods for maximal sensitivities, and 3) demonstrates the relative robustness/susceptibility of MREG and EPI to violation of the GLM's assumptions.

Objective Ripples (80–250 Hz) have been shown to be a more specific biomarker for the epileptogenic zone than epileptic spikes in intracranial EEG and even surface EEG. Ripples often co-occur with spikes. We investigated the spatiotemporal relation between spikes and ripples, and differences between spikes that do and do not co-occur with ripples. Methods We marked 50 time points with spikes in bipolar surface EEG during NREM sleep in patients with focal or multifocal epilepsy. We marked ripples that occurred with spikes and calculated parameters relating spikes and ripples: the duration, amplitude and slope of spikes, the timing of the start of ripples and spikes and the proportion of overlap. Results In total 219 ripples and 5995 individual spikes were marked in 31 patients. Spikes with ripples were on average shorter, had higher amplitude and higher slope than spikes without ripples. 64% of ripples started before spikes started. Spikes occurred on 13 (5–26) channels per patient, and ripples on 3 (0–14) channels, which were also spike channels. Conclusion Ripples precede rather than follow spikes, so ripples are unlikely to result from spikes. Significance Ripples and spikes seem not one-on-one coupled, but certain states of the brain can accommodate both.

Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many remain in need of new therapeutic approaches. Predicting the impending seizures in these patients could significantly enhance their quality of life if the prediction performance is clinically practical. In this study, we investigate the improvement of the performance of a seizure prediction algorithm in 17 patients with mesial temporal lobe epilepsy by means of a novel measure. Scale-free dynamics of the intracerebral EEG are quantified through robust estimates of the scaling exponents--the first cumulants--derived from a wavelet leader and bootstrap based multifractal analysis. The cumulants are investigated for the discriminability between preictal and interictal epochs. The performance of our recently published patient-specific seizure prediction algorithm is then out-of-sample tested on long-lasting data using combinations of cumulants and state similarity measures previously introduced. By using the first cumulant in combination with state similarity measures, up to 13 of 17 patients had seizures predicted above chance with clinically practical levels of sensitivity (80.5%) and specificity (25.1% of total time under warning) for prediction horizons above 25 min. These results indicate that the scale-free dynamics of the preictal state are different from those of the interictal state. Quantifiers of these dynamics may carry a predictive power that can be used to improve seizure prediction performance.

Epilepsy is both a disease of the brain and the mind. Here, we present the second of two papers with extended summaries of selected presentations of the Third International Congress on Epilepsy, Brain and Mind (April 3-5, 2014; Brno, Czech Republic). Humanistic, biologic, and therapeutic aspects of epilepsy, particularly those related to the mind, were discussed. The extended summaries provide current overviews of epilepsy, cognitive impairment, and treatment, including brain functional connectivity and functional organization; juvenile myoclonic epilepsy; cognitive problems in newly diagnosed epilepsy; SUDEP including studies on prevention and involvement of the serotoninergic system; aggression and antiepileptic drugs; body, mind, and brain, including pain, orientation, the "self-location", Gourmand syndrome, and obesity; euphoria, obsessions, and compulsions; and circumstantiality and psychiatric comorbidities.

Summary: Purpose: High-frequency activity has been recorded with intracerebral microelectrodes in epileptic patients and related to seizure genesis. Our goal was to analyze high-frequency activity recorded with electroencephalograph (EEG) macroelectrodes during the slow wave immediately following interictal spikes, given the potential importance of this presumed hyperpolarization in transforming spikes into seizures. Methods: Depth electrode EEG recordings from 10 patients with intractable focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Spikes were categorized according to localization and morphology. Segments of 256 ms were selected immediately following (postspike), and 2 s before each spike (baseline). Power was estimated in subgamma (0‐40 Hz), gamma (40‐100 Hz), high frequency (100‐200 Hz), and very high frequency (250‐500 Hz) bands. Results: Changes in power above 100 Hz were seen in 22 of 29 spike categories, consisting primarily of a widespread decrease in frequencies above 100 Hz. This decrease became spatially more restricted as frequencies increased, and coincided with the localization of largest spikes for the highest frequencies. High-frequency power decreases were prominent in the hippocampus but less common in amygdala and neocortex. High-frequency power increases were observed in the amygdala. Conclusions: Thus high-frequency EEG activity can be recorded with macroelectrodes in humans and may provide insights on neuronal mechanisms related to human epilepsy. This activity undergoes consistent modifications after EEG spikes. We propose that the reduction in high frequencies reflects a postspike depression in neuronal activity that is more pronounced in the region of spike generation. This depression is almost always seen in hippocampus but less in amygdala. Key Words: Intracerebral EEG—High frequency—Spikes.

Analyzing functional magnetic resonance imaging (fMRI) data restricted to the cortical surface is of particular interest for two reasons: (1) to increase detection sensitivity using anatomical constraints and (2) to compare or use fMRI results in the context of source localization from magneto/electro-encephalography (MEEG) data, which requires data to be projected on the same spatial support. Designing an optimal scheme to interpolate fMRI raw data or resulting activation maps on the cortical surface relies on a trade-off between choosing large enough interpolation kernels, because of the distributed nature of the hemodynamic response, and avoiding mixing data issued from different anatomical structures. We propose an original method that automatically adjusts the level of such a trade-off, by defining interpolation kernels around each vertex of the cortical surface using a geodesic Voronoi diagram. This Voronoi-based interpolation method was evaluated using simulated fMRI activation maps, manually generated on an anatomical MRI, and compared with a more standard approach where interpolation kernels were defined as local spheres of radius r = 3 or 5 mm. Several validation parameters were considered: the spatial resolution of the simulated activation map, the spatial resolution of the cortical mesh, the level of anatomical/functional data misregistration and the location of the vertices within the gray matter ribbon. Using an activation map at the spatial resolution of standard fMRI data, robustness to misregistration errors was observed for both methods, whereas only the Voronoi-based approach was insensitive to the position of the vertices within the gray matter ribbon.

Objective To devise an automated system to remove artifacts from ictal scalp EEG, using independent component analysis (ICA). Methods A Bayesian classifier was used to determine the probability that 2 s epochs of seizure segments decomposed by ICA represented EEG activity, as opposed to artifact. The classifier was trained using numerous statistical, spectral, and spatial features. The system's performance was then assessed using separate validation data. Results The classifier identified epochs representing EEG activity in the validation dataset with a sensitivity of 82.4% and a specificity of 83.3%. An ICA component was considered to represent EEG activity if the sum of the probabilities that its epochs represented EEG exceeded a threshold predetermined using the training data. Otherwise, the component represented artifact. Using this threshold on the validation set, the identification of EEG components was performed with a sensitivity of 87.6% and a specificity of 70.2%. Most misclassified components were a mixture of EEG and artifactual activity. Conclusions The automated system successfully rejected a good proportion of artifactual components extracted by ICA, while preserving almost all EEG components. The misclassification rate was comparable to the variability observed in human classification. Significance Current ICA methods of artifact removal require a tedious visual classification of the components. The proposed system automates this process and removes simultaneously multiple types of artifacts.

Patients with epilepsy often present in their electroencephalogram (EEG) short electrical potentials (spikes or spike-wave bursts) that are not accompanied by clinical manifestations but are of important diagnostic significance. They result from a population of abnormally hyperactive and hypersynchronous neurons. It is not easy to determine the location of the cerebral generators and the other brain regions that may be involved as a result of this abnormal activity. The possibility to combine EEG recording with functional MRI (fMRI) scanning opens the opportunity to uncover the regions of the brain showing changes in the fMRI signal in response to epileptic spikes seen in the EEG. These regions are presumably involved in the abnormal neuronal activity at the origin of epileptic discharges. This paper reviews the methodology involved in performing such studies, particularly the challenge of recording a good quality EEG inside the MR scanner while scanning is taking place, and the methods required for the statistical analysis of the combined EEG and fMRI time series. We review the results obtained in patients with different types of epileptic disorders and discuss the difficult theoretical problems raised by the interpretation of an increase (activation) and decrease (deactivation) in blood oxygen level dependent (BOLD) signal, both frequently seen in response to spikes.

Background: The intracarotid amobarbital procedure (IAP) is an important part of comprehensive investigation of patients who are candidates for surgical treatment of epilepsy. Owing to repeated and lengthy shortages of amobarbital, causing delays in elective surgery, attempts have been made to find a suitable alternative anesthetic. The authors report their experience using etomidate, a widely used agent for the induction of anesthesia. Methods: Sixteen consecutive patients requiring IAP to evaluate memory or to lateralize speech underwent the procedure using etomidate. Prior to the procedure a catheter was placed in the internal carotid artery and an angiogram was performed. EEG was recorded and read online by an electroencephalographer. An anesthetist injected the drug, administered by bolus followed by an infusion, which was maintained until each speech measure had been sampled and new memory items had been introduced. The infusion was then stopped and testing continued as in a standard IAP. Results: In all cases (30 hemispheres) contralateral hemiplegia followed injection. EEG slow waves were observed in every injected hemisphere, with some contralateral slowing anteriorly in 18. Global aphasia with preserved attention and cooperation followed dominant-hemisphere injections. These phenomena remained during infusion, and upon its termination returned gradually to baseline over a period of about 4 minutes. Conclusions: Etomidate is a viable alternative to amobarbital, and its administration by bolus followed by infusion offers an improvement over the traditional intracarotid amobarbital procedure. Cognitive tests can be performed during an assured hemianesthesia of the injected hemisphere.

Grey matter heterotopia are commonly associated with refractory epilepsy. Depth electrodes recordings have shown that epileptiform activity can be generated within these lesions, and also at a distance in the neocortex. Heterotopia seem to be part of a more complex circuitry involving also the surrounding and distant cerebral cortex. Blood oxygenation level-dependent (BOLD) changes to interictal spikes using continuous EEG and functional MRI (EEG-fMRI) can help to understand non-invasively the mechanisms of epileptogenicity in these patients. We studied 14 patients with epilepsy and heterotopia using simultaneous recording of EEG-fMRI. EEG was continuously acquired from inside the scanner during 2 h sessions. Epileptic spikes were visually identified in the filtered EEG and each type of spike determined one EEG-fMRI study. We looked at positive (activation) and negative (deactivation) changes in the BOLD signal. Eleven patients had nodular heterotopia and three band heterotopia. Four patients had more than one type of spikes, with a total of 26 EEG-fMRI studies. We excluded three with less than three spikes, and therefore a total of 23 studies (12 with nodular and 11 with band heterotopia) were analysed. Nodular heterotopia: Activation was present in nine studies, with involvement of the heterotopia or surrounding cortex in six, three of which had concomitant distant activation. Deactivation was also observed in nine studies, with involvement of the heterotopia and surrounding cortex in four, three of which had concomitant distant deactivation. Band heterotopia: Activation was present in all 11 studies, and always involved the heterotopia and surrounding cortex, 9 of which had concomitant distant activation. Deactivation was also observed in all 11 studies, with involvement of both the heterotopia and surrounding cortex, in addition to distant deactivation in 5 studies. EEG-fMRI studies reveal, non-invasively, metabolic responses in the heterotopia despite the fact that spikes are generated in the neocortex. The responses, activation or deactivation, had different correlation with the lesion and surrounding or distant cortex, activation reflecting intense neuronal activity, or excitation, and deactivation a possible distant (extra-lesional) inhibition. EEG-fMRI may become a useful tool to understand the epileptogenicity of such malformations.

Objective In the clinical interpretation of intracerebral EEGs, epileptic foci are commonly identified by visually analyzing the amplitude of the potentials. This is potentially misleading since electrodes record activity from several sources, but the nearest ones generate large amplitudes that can overpower distant sources. Our objective was to improve foci detection in intracerebral recordings by applying source localization methods. Methods Data were simulated by placing 3 sources in a semi-infinite medium near 3 intracerebral electrodes. Potentials were generated and contaminated with white and correlated noise. Two inverse problem algorithms, beamforming and RAP-MUSIC, were used to calculate equivalent dipoles. Results Simulations for each noise types showed that the two methods detected the source locations accurately, with RAP-MUSIC reporting lower orientation errors. With correlated noise, beamforming reconstructed original source waveforms poorly. A spatial resolution analysis was performed, in which beamforming adequately distinguished sources separated by 1.2cm, whereas RAP-MUSIC separated sources as close as 0.4–0.6cm. Conclusions Both source localization methods proved useful in detecting the location of dipolar sources based on simulated intracerebral potentials. For all simulations, RAP-MUSIC was more accurate than beamforming. Significance It is possible to use source localization methods traditionally applied to scalp recordings for improving source detection from intracerebral recordings.

Our objective was to evaluate the brain regions showing increased and decreased metabolism in patients at the time of generalized bursts of epileptic discharges in order to understand their mechanism of generation and effect on brain function. By recording the electroencephalogram during the functional MRI, changes in the blood oxygenation level-dependent signal were obtained in response to epileptic discharges observed in the electroencephalogram of 15 patients with idiopathic generalized epilepsy. A group analysis was performed to determine the regions of positive (activation) and negative (deactivation) blood oxygenation level-dependent responses that were common to the patients. Activations were found bilaterally and symmetrically in the thalamus, mesial midfrontal region, insulae, and midline and bilateral cerebellum and on the borders of the lateral ventricles. Deactivations were bilateral and symmetrical in the anterior frontal and parietal regions and in the posterior cingulate gyri and were seen in the left posterior temporal region. Activations in thalamus and midfrontal regions confirm known involvement of these regions in the generation or spread of generalized epileptic discharges. Involvement of the insulae in generalized discharges had not previously been described. Cerebellar activation is not believed to reflect the generation of discharges. Deactivations in frontal and parietal regions remarkably followed the pattern of the default state of brain function. Thalamocortical activation and suspension of the default state may combine to cause the actual state of reduced responsiveness observed in patients during spike-and-wave discharges. This brief lapse of responsiveness may therefore not result only from the epileptic discharge but also from its effect on normal brain function.

Objective A new clinical seizure waning system for intracerebral EEG is proposed. It is aimed at a better performance than existing systems and at user tuneability. Methods The system employs data filtering in multiple bands, spectral feature extraction, Bayes' theorem, and spatio-temporal analysis. The a priori information in Bayes' theorem was provided by 407 h of EEG from 19 patients having 152 seizures. Results The testing data (19 patients, 389 h, 100 seizures, independent of the training data) yielded a sensitivity of 89.4%, a false detection rate of 0.22/h, and median delay time of 17.1 s when tuning was used, and 86%, 0.47/h and 16.2 s without tuning. Missed seizures were of short duration or had subtle seizure activity. False detections were caused by technical artefacts, non-epileptic large amplitude rhythmic bursts or very low amplitude activity. It was established that performance could easily be tuned. Results were also compared to the clinical system of Gotman (1990) . Conclusions The system offers a performance that is acceptable for clinical use. User tuneability allows for reduction in false detection with minimal loss to sensitivity. Significance Epilepsy monitoring generates large amounts of recordings and requires intense observation. Automatic seizure detection and warning systems reduce review time and facilitate observation. We propose a method with high sensitivity and few false alarms.

One of the key features of rapid-eye movement (REM) sleep is the presence of bursts of REMs. Sleep studies routinely use REMs to classify sleep stages. Moreover, REM count or density has been used in studies involving learning and various psychiatric disorders. Most of these studies have been based on the visual identification of REMs, which is generally a very time-consuming task. This and the varying definitions of REMs across scorers have warranted the development of automatic REM detection methodologies. In this paper, we present a new detection scheme that combines many of the intrinsic properties of REMs and requires minimal parameter adjustments. In the proposed method, a single parameter can be used to control the REM detection sensitivity and specificity tradeoff. Manually scored training data are used to develop the method. We assess the performance of the method against manual scoring of individual REM events and present validation results using a separate data set. The ability of the method to discriminate fast horizontal ocular movement in REM sleep from other types of events is highlighted. A key advantage of the presented method is the minimal a priori information requirement. The results of training data (recordings from five subjects) show an overall sensitivity of 78.8% and specificity of 81.6%. The performance on the testing data (recording from five subjects different from the training data) showed overall sensitivity of 67.2% and specificity of 77.5%.

Summary: Purpose: The EEG is often recorded during the intracarotid amobarbital procedure (IAP) to help in the assessment of the spatial extent and the duration of the effect of the drug. In scalp recordings, the EEG is always heavily contaminated with eye movement artifacts as the patient actively performs visual tasks. Methods: Independent component analysis (ICA) is a new technique for blind source separation. In this study, we separated the EEG data recorded during the IAP into independent components using ICA. The EEG signal was reconstructed by excluding the components related to eye movement and eye blinks. Results: EEGs from 10 IAP tests were analyzed. The experimental results indicate that ICA is very efficient at subtracting eye-movement artifacts, while retaining the EEG slow waves and making their interpretation easier. Conclusions: ICA appears to be a generally applicable and effective method for removing ocular artifacts from EEG recordings during IAP, although slow waves and ocular artifacts share similar frequency distributions.

Summary: Purpose: We investigated the accuracy and potential for serious error when representing cortical generators of epileptic spikes with the common single-dipole model. Spike generators were realistically simulated with cortical areas of different extents. Methods: The source was simulated by using a patch that comprised small triangles on the cortical surface, each triangle having an elementary dipole generator with a moment corresponding to real intracerebral fields of spikes. The source-patch covered various clinically important parts of the temporal and frontal lobes, with an area ranging from 6 to 120 cm2. The scalp field was computed for each source-patch by using a realistic head model and was fitted by the single-dipole model to determine the best-fit dipole and the intracerebral distribution of residual variance (RV). Dipole modeling also was performed for the simulated scalp field with additional real EEG background. Results: The RV after fitting a dipole to the scalp field without noise was at most 1.34%. Scalp spikes arising from sources of 6 cm2 were of small amplitude, and the dipoles estimated for these spikes were inconsistent. Extension of the source area was associated with increase of scalp potential amplitude, only very small increase of RV, and increased consistency of the estimated dipoles. When the source was very large, the dipoles clustered at very misleading locations. Conclusions: Pitfalls in dipole source localization are caused by the procedure of fitting the simplistic dipole model to real cortical sources with spatial extent and complex configuration.

The use of combined EEG–fMRI to study interictal epileptiform activity is increasing and has great potential as a clinical tool, but the haemodynamic response to epileptiform activity remains incompletely characterised. To this end, 19 data sets from 14 patients with prolonged bursts of focal or generalised interictal epileptiform activity lasting up to 15 s were analysed. To determine whether the inclusion of the durations of the epileptic events in the general linear model resulted in increased statistical significance of activated regions, statistical maps were generated with and without the event durations. The mean differences when including the durations were a 14.5% increase in peak t value and a 29.5% increase in volume of activation. This suggests that when analysing EEG–fMRI data from patients with prolonged bursts of interictal epileptiform activity, it is better to include the event durations. To determine whether the amplitudes and latencies of the measured responses were consistent with the general linear model, the haemodynamic response functions for bursts of different durations were calculated and compared with the model predictions. The measured amplitude of the response to the shortest duration events was consistently larger than predicted, which is consistent with studies in normal subjects. For the two data sets with the widest range of event durations, the measured amplitudes increased with the durations of the events without evidence of the plateau that was expected from the general linear model. There were no consistent differences between the measured and modelled latencies.

Objective A new method for automatic seizure detection and onset warning is proposed. The system is based on determining the seizure probability of a section of EEG. Operation features a user-tuneable threshold to exploit the trade-off between sensitivity and detection delay and an acceptable false detection rate. Methods The system was designed using 652 h of scalp EEG, including 126 seizures in 28 patients. Wavelet decomposition, feature extraction and data segmentation were employed to compute the a priori probabilities required for the Bayesian formulation used in training, testing and operation. Results Results based on the analysis of separate testing data (360 h of scalp EEG, including 69 seizures in 16 patients) initially show a sensitivity of 77.9%, a false detection rate of 0.86/h and a median detection delay of 9.8 s. Results after use of the tuning mechanism show a sensitivity of 76.0%, a false detection rate of 0.34/h and a median detection delay of 10 s. Missed seizures are characterized mainly by subtle or focal activity, mixed frequencies, short duration or some combination of these traits. False detections are mainly caused by short bursts of rhythmic activity, rapid eye blinking and EMG artifact caused by chewing. Evaluation of the traditional seizure detection method of [Gotman J. Electroencephalogr Clin Neurophysiol 1990;76:317–24] using both data sets shows a sensitivity of 50.1%, a false detection rate of 0.5/h and a median detection delay of 14.3 s. Conclusions The system performed well enough to be considered for use within a clinical setting. In patients having an unacceptable level of false detection, the tuning mechanism provided an important reduction in false detections with minimal loss of detection sensitivity and detection delay. Significance During prolonged EEG monitoring of epileptic patients, the continuous recording may be marked where seizures are likely to have taken place. Several methods of automatic seizure detection exist, but few can operate as an on-line seizure alert system. We propose a seizure detection system that can alert medical staff to the onset of a seizure and hence improve clinical diagnosis.

Objective: To determine whether early tonic limb posturing is reliable in lateralizing or localizing of the seizure generator in 14 patients with pharmacoresistent supplementary sensorimotor area (SSMA) seizures. Methods: All patients underwent high-quality MRI scans and stereo-EEG recordings. Results: The SSMA seizure semiology predicted focal or regional ictal onset in the SSMA in six (43%) patients: Three had a focal SSMA seizure onset, and three had a regional seizure onset with involvement of one SSMA plus adjacent neocortex. The eight remaining patients had diffuse uni- or bilateral seizure onset. Eight of 14 patients underwent a frontal or central cortical resection, but a good outcome was seen in only 3: 2 with no SSMA resection and 1 with an extensive central removal. Conclusions: SSMA semiology is suggestive of early involvement of this region but is by no means a reliable indicator that the primary SSMA contains the seizure focus.

Combined EEG-fMRI has recently been used to explore the BOLD responses to interictal epileptiform discharges. This study examines whether misspecification of the form of the haemodynamic response function (HRF) results in significant fMRI responses being missed in the statistical analysis. EEG-fMRI data from 31 patients with focal epilepsy were analysed with four HRFs peaking from 3 to 9 sec after each interictal event, in addition to a standard HRF that peaked after 5.4 sec. In four patients, fMRI responses were correlated with gadolinium-enhanced MR angiograms and with EEG data from intracranial electrodes. In an attempt to understand the absence of BOLD responses in a significant group of patients, the degree of signal loss occurring as a result of magnetic field inhomogeneities was compared with the detected fMRI responses in ten patients with temporal lobe spikes. Using multiple HRFs resulted in an increased percentage of data sets with significant fMRI activations, from 45% when using the standard HRF alone, to 62.5%. The standard HRF was good at detecting positive BOLD responses, but less appropriate for negative BOLD responses, the majority of which were more accurately modelled by an HRF that peaked later than the standard. Co-registration of statistical maps with gadolinium-enhanced MRIs suggested that the detected fMRI responses were not in general related to large veins. Signal loss in the temporal lobes seemed to be an important factor in 7 of 12 patients who did not show fMRI activations with any of the HRFs.

There are multiple approaches to analyzing the scalp EEG to obtain information on the intracerebral generators of epileptic activity. We review here a few of these methods, as they have been applied in various research projects carried out at the Montreal Neurological Institute. The areas covered include the validation of dipole models of epileptic spikes and of seizure discharges, the EEG microscope of Kobayashi, the analysis of rapid spread of epileptic seizures, and the recently developed method of combining EEG and functional magnetic resonance imaging to obtain activation regions at the time of spikes.

Combining electroencephalogram (EEG) and functional MRI (fMRI) allows localization of brain regions activated as a result of epileptic spikes. The statistical analysis of fMRI data usually includes a standard model of the hemodynamic response function (HRF) but it is not known how well this fits the actual HRF of epileptic spikes. The objective of this exploratory study was to compare the activated areas and t-statistical scores obtained with a standard HRF to those obtained with a patient-specific HRF. Eight patients with focal epilepsy were studied. We obtained an estimate of the patient-specific HRFs for each patient at the local maximum of activation in the standard HRF analysis. The activated areas obtained with the patient-specific HRFs were larger or similar to the originally activated areas. Additional activated areas were seen in five patients, and most were compatible with the EEG and anatomical MRI localization of epileptogenic and lesional regions. Using patient-specific HRFs brings increased sensitivity to the analysis of epileptic spikes by EEG-fMRI.

Summary: Purpose: To evaluate functional magnetic resonance imaging (fMRI) with simultaneous EEG for finding metabolic sources of epileptic spikes. To find the localizing value of activated regions and factors influencing fMRI responses. Methods: Patients with focal epilepsy and frequent spikes were subjected to spike-triggered or continuous fMRI with simultaneous EEG. Results were analyzed in terms of fMRI activation, concordance with the location of EEG spiking and anatomic MRI abnormalities, and other EEG and clinical variables. In four patients, results also were compared with those of intracerebral EEG. Results: Forty-eight studies were performed on 38 patients. Seventeen studies were not analyzed, primarily because no spikes occurred during scanning. Activation was obtained in 39% of 31 studies, with an activation volume of 2.55 ± 4.84 cc. Activated regions were concordant with EEG localization in almost all studies and confirmed by intracerebral EEG in four patients. Forty percent of patients without an MRI lesion showed activation; 37.5% of patients with a lesion had an activation; the activation was near or inside the lesion. Bursts of spikes were more likely to generate an fMRI response than were isolated spikes (76 vs. 11%; p < 0.05). Conclusions: Combining EEG and fMRI in focal epilepsy yields regions of activation that are presumably the source of spiking activity. These regions are highly linked with epileptic foci and epileptogenic lesions in a significant number of patients. Activation also is found in patients with no visible MRI lesion. Intracerebral recordings largely confirm that these activation regions represent epileptogenic areas. It is still unclear why many patients show no activation. Key Words: Functional magnetic resonance imaging—Activation—Spikes.

Objective : We tried to determine the error range of dipole modeling for EEG spikes originating from various clinically important sources by a simulation study employing a realistic head model. The computed error range was also compared with the degree of disturbance of dipole modeling caused by adding background activity to the spike. Methods : The scalp fields generated by temporal, frontal and rolandic epileptic sources with spatial extent were simulated, and the corresponding 3-dimensional maps of residual variance (RV) were built by computing the RV for a single dipole at each point on a fine imaginary grid in the brain. Single dipole modeling was also performed for the simulated scalp fields after adding real background activity. Results : The brain volume corresponding to a small RV was compact for the frontal sources and the lateral and baso-mesial temporal sources, and large for the anterior and baso-lateral temporal sources. The distribution of dipoles estimated for spikes contaminated with background corresponded to that of the volume of small RV and to spike-amplitude. Estimates were improved by employing inferior temporal electrodes. Conclusions : When evaluating dipole models of epileptic spikes, error ranges can be estimated and they vary considerably from region to region. Significance : This study illustrates the variability of the error in dipole modeling of epileptic spikes. This variability is important when considering the clinical interpretation of modeling results.

Background : Automatic seizure detection is often used during long-term monitoring, and is particularly important during intracerebral investigations. Existing methods make many false detections, particularly in intracerebral electroencephalogram (EEG) because of frequent large amplitude rhythmic activity bursts that are non-epileptiform. Objective : To develop a seizure detection method for intracerebral monitoring that is as sensitive as existing methods but has fewer false detections. Methods : To capture the rhythmic nature of seizure discharges, we developed a wavelet-based method, examining how different frequency ranges fluctuate compared to the background. In particular, the system remembers rhythmic bursts occurring commonly in the background to avoid detecting them as seizures. Results : The method was evaluated on test data from 11 patients, including 229 h and 66 seizures, and its performance compared to the method of Gotman (Electroencephalogr clin Neurophysiol 76 (1990) 317). Detection sensitivity was unchanged at close to 90%, but false detections were reduced from 2.4 to 0.3/h. Conclusions : Perfect sensitivity is unlikely because the morphology of seizure discharges is so variable. Nevertheless, the 87% sensitivity obtained in the combined training and testing data is quite high. We reduced the average false alarm rate to one per 3 h of recording, or 6 per 24-h period. Given how rapidly one can decide visually that a detection is erroneous, false detections should not cause any burden to the reviewer. Significance : In intracerebral EEG it is possible to detect seizures automatically with high sensitivity and high specificity.

Summary: Purpose: We studied the relation between quantitative interictal subdural EEG data and visually defined ictal subdural EEG findings in children with intractable neocortical epilepsy, and determined whether interictal EEG data are predictive of ictal EEG onset zones. Methods: Thirteen children (aged 1.2–15.4 years) underwent prolonged intracranial EEG recording, using 48- to 120-channel subdural electrodes. Three distinct 10-min segments of the continuous interictal EEG recording were selected for each patient, and the spike frequency for each channel was determined by using an automatic spike-detection program. Subsequently the average spike frequency of each electrode was compared with ictal assessment (onset, spread, and no early ictal involvement). In addition, 50 distinct interictal spikes were averaged for each patient, and the amplitude and latency after the leading spike (averaged spike showing the earliest peak) were measured for each electrode and analyzed with respect to ictal EEG findings. Results: Reproducibility of the spike-frequency pattern derived from three 10-min segments was high (Kendall's W, 0.85 ± 0.08). Electrodes showing the highest spike frequency, the highest spike amplitude, and the leading spike were found to be a part of the seizure onset in 13 of 13, 12 of 13, and 10 of 13 cases, respectively. There was significant correlation between ictal assessment and spike frequency as well as spike amplitude. A receiver operating characteristics analysis showed that a cutoff threshold at 14% of the maximal spike frequency resulted in a specificity of 0.90 and a sensitivity of 0.77 for the detection of seizure-onset electrodes. Conclusions: Quantitative interictal subdural EEG may predict ictal-onset zones in children with intractable neocortical epilepsy.

Objective : We evaluated the use of an efficient dipole source algorithm to improve performance of automated spike detection by identifying false detections caused by artefacts. Methods : Automated spike detections were acquired from 26 patients undergoing prolonged electroencephalograph (EEG) monitoring. Data from 6 patients were used to develop the method and data from 20 patients were used to test the method. To provide a standard against which to evaluate the results, an electroencephalographer (EEGer) visually categorized all automated detections before the dipole models were calculated for all events. The event categories (as defined by the EEGer) were then combined with properties of the dipole model and features were identified that differentiated spike and artefact detections. The resulting method was then applied to the testing data set. Results : Residual variance and eccentricity of the dipole models differentiated artefact and spike detections. A separate set of rules defining eye blink artefact was also developed. The combined criteria removed a mean of 53.2% of artefact from the testing data set. Some spike detections (4.3%) were also lost. Conclusions : The features of the dipole source of a detected event can be used to differentiate artefacts from spikes. This algorithm is computationally light and could be implemented on-line.

We studied single-event and average BOLD responses to EEG interictal epileptic discharges (IEDs) in four patients with focal epilepsy, using continuous EEG–fMRI during 80-min sessions. The detection of activated areas was performed by comparing the BOLD signal at each voxel to a model of the expected signal. Since little is known about the BOLD response to IEDs, we modeled it with the response to brief auditory events (G. H. 8 , NeuroImage 9, 416–429). For each activated area, we then obtained the time course of the BOLD signal for the complete session and computed the actual average hemodynamic response function (HRF) to IEDs. In two of four patients, we observed clear BOLD responses to single IEDs. The average response was composed of a positive lobe peaking between 6 and 7 s in all patients and a negative undershoot in three patients. There were important variations in amplitude and shape between average HRFs across patients. The average HRF presented a wider positive lobe than the Glover model in three patients and a longer undershoot in two. There was a remarkable similarity in the shape of the HRF across areas for patients presenting multiple activation sites. There was no clear correlation between the amplitude of individual BOLD responses and the amplitude of the corresponding EEG spike. The possibility of a longer HRF could be used to improve statistical detection of activation in simultaneous EEG–fMRI. The variability in average HRFs across patients could reflect in part different pathophysiological mechanisms.

Objectives : We propose a combination of independent component analysis (ICA) and recursively applied and projected multiple signal classification (RAP-MUSIC) as a new approach to dipole source estimation of epileptiform discharges. The method is minimally dependent on subjective decisions. Methods : Ten electroencephalographic (EEG) data matrices were generated by computer, each matrix including real background activity from a normal subject and an array of added simulated spikes. Each spike was a summation of two transients originating from slightly different ‘original dipole sources'. The unaveraged EEG matrices were decomposed by ICA, and source estimation was performed by applying RAP-MUSIC to the spatial information defined by the ICA components showing epileptiform activity in their waveform. For comparison, dipoles were also estimated from the same matrices using two existing methods: RAP-MUSIC based on eigen-decomposition of the covariance matrices of averaged spikes and common spatial pattern decomposition. Results : In every simulated EEG data matrix, two dipoles close to the original sources were estimated by the present method. Their unaveraged activities were also similar to those of the original sources. The two existing methods gave less precise results than the proposed method. Conclusions : RAP-MUSIC based on ICA thus proved promising for source estimation of unaveraged epileptiform discharges.

We designed a combination of independent component analysis (ICA) and recursively applied and projected multiple signal classification (RAP-MUSIC) as a new approach to dipole source estimation of epileptiform discharges. The estimation is minimally influenced by subjective decisions in this analysis. A simulation study was performed by generating 10 EEG data matrices by means of a computer: each matrix included real background activity from a normal subject to which an array of simulated unaveraged spikes was added. Each discharge was a summation of two transients originating from slightly different “original dipole sources”. The simulated spikes in the unaveraged EEG data were extracted from the background by ICA as spatiotemporal components, each component having fixed potential field distribution and maximally independent waveform. The complete separation between spikes and background was objectively proven by reconstruction of the EEG from the decomposed components. RAP-MUSIC was performed based on the spatial information included in the epileptic ICA components. RAP-MUSIC does not involve subjective decisions such as selection of multiple local score peaks, determination of the number of dipoles or initial guesses of dipole parameters. In every simulated EEG data matrix, two dipoles close to the original sources were estimated. Their activities were also similar to those of the original sources. For comparison, the same simulated EEG data were averaged and subjected to RAP-MUSIC using eigen-decomposition of the covariance matrices. Only one dipole was estimated in every simulation by this conventional method and therefore the result of the present method was better. RAP-MUSIC based on ICA, thus, proved promising for source estimation of unaveraged epileptiform discharges.

The authors present a method that can be used to identify exemplar spikes from prolonged EEG recordings. To achieve this they have calculated single dipole source models for each automatically detected spike-like waveform. They used a dipole source algorithm that is computationally light and can be run on-line during EEG acquisition. Although a single dipole source model may not provide anatomically accurate information about the location of generators of all epileptiform abnormalities, it does provide a novel spatial parameter that may be useful in its own right. The authors use this spatial parameter and present the relative spatial density of the dipole locations in the form of three planar projections of the spherical model (a view from above, a view from the right, and a view from behind) and allow users to define the x-, y-, and z-coordinates of points of interest within the spherical model. They then present 10 example waveforms of events that have dipole source model locations that occur close to that seed coordinate. Overall, they found that this method performs very well for frequent events, but does not perform well for rare events or for diffuse EEG abnormalities.

Electroencephalography is a long-established technique and remains a crucial diagnostic tool in epilepsy. The high temporal resolution of EEG allows the detection of transient events, such as spikes, which are often an important feature in patients with epilepsy. However, it has low spatial resolution. Functional magnetic resonance imaging (fMRI), first demonstrated in 1990, with its high spatial resolution and noninvasiveness, has had a substantial impact on functional imaging. Combined EEG and fMRI promises to be a tool with all the desirable features of both individual techniques. EEG recording in the MR environment (per-MRI

Highlights • We analyze the distribution of intracerebral EEG patterns of FCD in relation to sleep. • FCD interictal EEG patterns are present between 45% and 97% of the time analyzed. • Despite almost continuous spiking, sleep is an important modulator of FCD EEG patterns. • This suggests that dysplastic tissue is under thalamocortical control., Summary Objective Focal cortical dysplasia (FCD) is able to generate an intrinsic pathological EEG activity characterized by a continuous or near-continuous spiking. Different patterns of discharge were described. We examined quantitatively the distribution of the intracerebral FCD patterns in relation to sleep in order to investigate whether this activity is independent of thalamocortical influences. Methods We analyzed the first sleep cycle of 5 patients with a diagnosis of FCD type II who underwent combined scalp-intracranial electroencephalography (EEG), and showed an intracranial EEG pattern typical for FCD. Three patterns of FCD intracranial EEG activity were identified in all 5 patients, and visually marked for a maximum of 30 min of each stage (wake, N1, N2, N3, REM): spike or polyspike exceeding 2 Hz (pattern 1), spike or polyspike interrupted by flat periods below 2 Hz (pattern 2) and discharges of >15 Hz low-voltage rhythmic activity with regular morphology (pattern 3). After marking, the percentages of the three patterns across the different stages were calculated. Results The three patterns of FCD were present between 45% and 97% of the total time analyzed. Pattern 1 was the predominant pattern in wakefulness (73–100%), N1 (76–97%) and N2 (58–88.5%) in all patients, and in REM in 4 of 5 patients (91–100%). During N2 and N3, there was an increase in pattern 2 in all patients, becoming the predominant pattern in 3 of the 5 patients during N3 (63–89%). Pattern 3 was rare and only sporadically observed during N2 and N3. Wakefulness and REM sleep showed a similar pattern (pattern 1) with a slight amplitude reduction in REM sleep. Significance Despite the presence of an almost continuous discharge, sleep is an important modulator of the pathological EEG patterns found in FCD type II. This might suggest that dysplastic tissue is influenced by the thalamo-cortical control mechanisms involved in the generation of sleep.

Objective To demonstrate and quantify the occurrence of false High Frequency Oscillations (HFOs) generated by the filtering of sharp events. To distinguish real HFOs from spurious ones using analysis of the raw signal. Method We developed a new method to prevent false HFO detections due to the filtering effect by detecting oscillations in the raw signal at the time of sharp events. We specified temporal features to classify sharp events with and without HFOs using support vector machine in both ripple and fast ripple bands. The traditionally used time–frequency representation served as the gold standard to indicate real and false HFOs. Results 44% of ripples and 43% of FRs concurring with sharp events were found to be false HFOs. Sharp events with HFOs had significantly more oscillations in the raw signal than sharp events without. They could be distinguished from false HFOs with accuracy of 76.6% in the ripple band and 72.6% in the fast ripple band. Conclusion It may be most appropriate to detect HFOs as oscillations not only on the filtered signal but also on the raw signal. The classical time–frequency display used for identifying HFOs should be used with great care due to the possible masking effect of broadband activities. Significance The separation of real HFOs from broadband activities will raise the validity of HFO detection methods and will therefore support future HFO investigations.

We studied with functional magnetic resonance imaging (fMRI) differences in resting-state networks between patients with mesial temporal lobe epilepsy (MTLE) and healthy subjects. To avoid any a priori hypothesis, we use a data-driven analysis assessing differences between groups independently of structures involved. Shared and specific independent component analysis (SSICA) is an exploratory method based on independent component analysis, which performs between-group network comparison. It extracts and classifies components (networks) in those common between groups and those specific to one group. Resting fMRI data were collected from 10 healthy subjects and 10 MTLE patients. SSICA was applied multiple times with altered initializations and different numbers of specific components. This resulted in many components specific to patients and to controls. Spatial clustering identified the reliable resting-state networks among all specific components in each group. For each reliable specific network, power spectrum analysis was performed on reconstructed time-series to estimate connectivity in each group and differences between groups. Two reliable networks, corresponding to statistically significant clusters robustly detected with clustering were labeled as specific to MTLE and one as specific to the control group. The most reliable MTLE network included hippocampus and amygdala bilaterally. The other MTLE network included the postcentral gyri and temporal poles. The control-specific network included bilateral precuneus, anterior cingulate, thalamus, and parahippocampal gyrus. Results indicated that the two MTLE networks show increased connectivity in patients, whereas the control-specific network shows decreased connectivity in patients. Our findings complement results from seed-based connectivity analysis (1). The pattern of changes in connectivity between mesial temporal lobe structures and other areas may help us understand the cognitive impairments often reported in patients with MTLE.

Growing evidence indicates that fast oscillations (> 80 Hz) can be recorded interictally in the scalp EEG of patients with epilepsy, and that they may point to the seizure-onset zone. However, mechanisms underpinning the emergence of scalp fast oscillations, and whether they differ from those of interictal epileptic discharges (IEDs), are yet to be understood. The visibility of cortical electric activity on scalp EEG recordings is dependent on two factors: the characteristics of the cortical generator and the background level. We studied this issue using scalp EEG recordings and detailed simulations, with a finite element model including 8 million elements and 8 different tissues. We observed an almost linear relationship between the amplitude of scalp electric potential and the extent of the generator on the cortex. However, this relationship is subject to substantial variability, with variations in factors greater than 3 occurring simply by changing the location on the cortex of generators of fixed extent. In addition, we showed that the background power in scalp EEG recordings decreases at higher frequency bands, being inversely proportional to a power of 2.5 of the frequency. In the specific case of fast oscillations, they can be detected within the lower noise level of the ripple band (80–200 Hz) even though their median amplitude on scalp EEG recordings is more than 10 times smaller than IEDs and consistent with cortical generators of approximately 1 cm2. In conclusion, the physics governing the propagation of electrical activity from the brain to the scalp are consistent with the hypothesis that scalp fast oscillations and intracranial high-frequency oscillations (HFOs, 80–500 Hz) are expressions of common generators. Given the potential role of HFOs as biomarkers in epilepsy, the possibility to obtain some of the associated information from scalp EEG is of high clinical significance. Fil: Von Ellenrieder, Nicolás. McGill University. Montreal Neurological Institute and Hospital; Canadá. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina Fil: Beltrachini, Leandro. University of Sheffield; Reino Unido Fil: Perucca, Piero. McGill University. Montreal Neurological Institute and Hospital; Canadá Fil: Gotman, Jean. McGill University. Montreal Neurological Institute and Hospital; Canadá

Objective The importance of epileptic high-frequency oscillations (HFOs) in electroencephalogram (EEG) is growing. Action potentials generating some HFOs are observed in the vicinity of neurons in experimental animals. However electrodes that are remote from neurons, as in case of clinical situations, should not record action potentials. We propose to resolve this question by a realistic simulation of epileptic neuronal network. Methods The rat dentate gyrus with sclerosis was simulated in silico . We computed the current dipole moment generated by each granule cell and the field potentials in a measurement area far from neurons. Results The dentate gyrus was stimulated through synaptic input to evoke discharges resembling interictal epileptiform discharges, which had superimposed HFOs⩽295Hz that were recordable with remote electrodes and represented bursts of action potentials of granule cells. The increase in power of HFOs was associated with the progression of sclerosis, the reduction of GABAergic inhibition, and the increase in cell connectivity. Spectral frequency of HFOs had similar tendencies. Conclusions HFOs recorded with electrodes remote from neurons could actually be generated by clusters of action potentials. Significance The phenomenon of action potentials recorded with remote electrodes can possibly extend the clinical meaning of EEG.

The spatial coherence of spontaneous slow fluctuations in the blood-oxygen-level dependent (BOLD) signal at rest is routinely used to characterize the underlying resting-state networks (RSNs). Studies have demonstrated that these patterns are organized in space and highly reproducible from subject to subject. Moreover, RSNs reorganizations have been suggested in pathological conditions. Comparisons of RSNs organization have been performed between groups of subjects but have rarely been applied at the individual level, a step required for clinical application. Defining the notion of modularity as the organization of brain activity in stable networks, we propose DANI - Detection of Abnormal Networks in Individuals - to identify modularity changes at the individual level. The stability of each RSN was estimated using a spatial clustering method: BASC Bootstrap Analysis of Stable Clusters (Bellec et al Neuroimage,51(3),2010). Our contributions consisted in (i) providing functional maps of the most stable cores of each networks and (ii) in detecting “abnormal” individual changes in networks organization when compared to a population of healthy controls. DANI was first evaluated using realistic simulated data, showing that focussing on a conservative core size (50% most stable regions) improved the sensitivity to detect modularity changes. DANI was then applied to resting state fMRI data of six patients with focal epilepsy who underwent multimodal assessment using simultaneous EEG/fMRI acquisition followed by surgery. Only patient with a seizure free outcome were selected and the resected area was identified using a post-operative MRI. DANI automatically detected abnormal changes in 5 out of 6 patients, with excellent sensitivity, showing for each of them at least one “abnormal” lateralized network closely related to the epileptic focus. For each patient, we also detected some distant networks as abnormal, suggesting some remote reorganization in the epileptic brain.