A European questionnaire survey on epilepsy monitoring units' current practice for postoperative psychogenic nonepileptic seizures' detection.

BACKGROUND: In cases undergoing epilepsy surgery, postoperative psychogenic nonepileptic seizures (PNES) may be underdiagnosed complicating the assessment of postsurgical seizures' outcome and the clinical management. We conducted a survey to investigate the current practices in the European epilepsy monitoring units (EMUs) and the data that EMUs could provide to retrospectively detect cases with postoperative PNES and to assess the feasibility of a subsequent postoperative PNES research project for cases with postoperative PNES. METHODS: We developed and distributed a questionnaire survey to 57 EMUs. Questions addressed the number of patients undergoing epilepsy surgery, the performance of systematic preoperative and postoperative psychiatric evaluation, the recording of sexual or other abuse, the follow-up period of patients undergoing epilepsy surgery, the performance of video-electroencephalogram (EEG) and postoperative psychiatric assessment in suspected postoperative cases with PNES, the existence of electronic databases to allow extraction of cases with postoperative PNES, the data that these bases could provide, and EMUs' interest to participate in a retrospective postoperative PNES project. RESULTS: Twenty EMUs completed the questionnaire sheet. The number of patients operated every year/per center is 26.7 ( ±â€¯19.1), and systematic preoperative and postoperative psychiatric evaluation is performed in 75% and 50% of the EMUs accordingly. Sexual or other abuse is systematically recorded in one-third of the centers, and the mean follow-up period after epilepsy surgery is 10.5 ±â€¯7.5 years. In suspected postoperative PNES, video-EEG is performed in 85% and psychiatric assessment in 95% of the centers. An electronic database to allow extraction of patients with PNES after epilepsy surgery is used in 75% of the EMUs, and all EMUs that sent the sheet completed expressed their interest to participate in a retrospective postoperative PNES project. CONCLUSION: Postoperative PNES is an underestimated and not well-studied entity. This is a European survey to assess the type of data that the EMUs surgical cohorts could provide to retrospectively detect postoperative PNES. In cases with suspected PNES, most EMUs perform video-EEG and psychiatric assessment, and most EMUs use an electronic database to allow extraction of patients developing PNES.

Dynamics of convulsive seizure termination and postictal generalized EEG suppression.

It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.

Cortisol fluctuations relate to interictal epileptiform discharges in stress sensitive epilepsy.

People with epilepsy often report seizures precipitated by stress. This is believed to be due to effects of stress hormones, such as cortisol, on neuronal excitability. Cortisol, regardless of stress, is released in hourly pulses, whose effect on epileptic activity is unknown. We tested the relation between cortisol levels and the incidence of epileptiform abnormalities in the electroencephalogram of people with focal epilepsy. Morning cortisol levels were measured in saliva samples obtained every 15 min. Interictal epileptiform discharges were determined in the same time periods. We investigated the relationship between cortisol levels and the epileptiform discharges distinguishing persons with from those without stress-precipitated seizures (linear mixed model), and analysed the contribution of individual, epilepsy and recording characteristics with multivariable analysis. Twenty-nine recordings were performed in 21 individuals. Cortisol was positively related to incidence of epileptiform discharges (ß = 0.26, P = 0.002) in people reporting stress-sensitive seizures, but not those who did not report stress sensitivity (ß = -0.07, P = 0.64). The relationship between cortisol and epileptiform discharges was positively associated only with stress sensitivity of seizures (ß = 0.31, P = 0.005). The relationship between cortisol levels and incidence of interictal epileptiform discharges in people with stress-sensitive seizures suggests that stress hormones influence disease activity in epilepsy, also under basal conditions.

Postictal generalized EEG suppression is not associated with periictal cardiac autonomic instability in people with convulsive seizures.

PURPOSE: Postictal generalized EEG suppression (PGES) seems to be a pathophysiologic hallmark in ictal recordings of sudden unexpected death in epilepsy (SUDEP). It has recently been suggested that presence and duration of PGES might be a predictor of SUDEP risk. Little is known about the etiology of PGES. METHODS: We conducted a retrospective case-control study in 50 people with convulsive seizures (CS) recorded on digital video-electroencephalography (EEG). One CS per individual was reviewed for presence and duration of PGES by two independent observers: Preictal and postictal heart rate (HR) (1 min before seizure onset and 1, 3, 5, 15, and 30 min after seizure end) and frequency domain measures of heart rate variability (HRV), including the ratio of low frequency (LF) versus high frequency (HF) power, were analyzed. The relationship between PGES and periictal autonomic changes was evaluated, as well as its association with several clinical variables. KEY FINDINGS: Thirty-seven individuals (74%) exhibited PGES and 13 (26%) did not. CS resulted in a significant increase of periictal HR and the LF/HF ratio. PGES was associated with neither periictal HR (mean HR difference between PGES+ and PGES- seizures: -2 beats per minute [bpm], 95% confidence interval [CI] -10 to +6 bpm) nor HRV change. There was no association between the duration of PGES and periictal HR change. People with PGES were more likely to be asleep before seizure onset (odds ratio [OR] 4.7, 95% CI 1.2-18.3) and had a higher age of onset of epilepsy (median age 15 vs. 4 years). SIGNIFICANCE: PGES was not associated with substantial changes in measures of cardiac autonomic instability but was more prevalent in CS arising from sleep.

Correction for pulse height variability reduces physiological noise in functional MRI when studying spontaneous brain activity.

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

Stimulation-based anticipation and control of state transitions in the epileptic brain.

We focus on the implications that the underlying neuronal dynamics might have on the possibility of anticipating seizures and designing an effective paradigm for their control. Transitions into seizures can be caused by parameter changes in the dynamic state or by interstate transitions as occur in multi-attractor systems; in the latter case, only a weak statistical prognosis of the seizure risk can be achieved. Nevertheless, we claim that by applying a suitable perturbation to the system, such as electrical stimulation, relevant features of the system's state may be detected and the risk of an impending seizure estimated. Furthermore, if these features are detected early, transitions into seizures may be blocked. On the basis of generic and realistic computer models we explore the concept of acute seizure control through state-dependent feedback stimulation. We show that in some classes of dynamic transitions, this can be achieved with a relatively limited amount of stimulation.

The paroxysmal dyskinesias.

The paroxysmal dyskinesias are a challenging group of movement disorders characterised by painless dystonic and/or choreiform movements. Lack of familiarity with their features and a normal neurological examination between attacks frequently cause diagnostic delays, or even the diagnosis of a non-organic disorder. They are classified by their mode of triggering, and also by the duration and frequency of attacks, effectiveness of medication, and any associated syndromes. Four subtypes are recognised: paroxysmal kinesigenic dyskinesia induced by sudden movement; paroxysmal non-kinesigenic dyskinesia precipitated by for instance alcohol or caffeine; paroxysmal exercise-induced dyskinesia triggered by longer lasting activity; and paroxysmal hypnogenic dyskinesia occurring during sleep. Here we will summarise the characteristics of the subtypes, discuss the differential diagnosis, genetic aspects and pathophysiology, and give practical advice on the diagnostic work-up and treatment.

Virtual localization of the seizure onset zone: Using non-invasive MEG virtual electrodes at stereo-EEG electrode locations in refractory epilepsy patients.

In some patients with medically refractory epilepsy, EEG with intracerebrally placed electrodes (stereo-electroencephalography, SEEG) is needed to locate the seizure onset zone (SOZ) for successful epilepsy surgery. SEEG has limitations and entails risk of complications because of its invasive character. Non-invasive magnetoencephalography virtual electrodes (MEG-VEs) may overcome SEEG limitations and optimize electrode placement making SOZ localization safer. Our purpose was to assess whether interictal activity measured by MEG-VEs and SEEG at identical anatomical locations were comparable, and whether MEG-VEs activity properties could determine the location of a later resected brain area (RA) as an approximation of the SOZ. We analyzed data from nine patients who underwent MEG and SEEG evaluation, and surgery for medically refractory epilepsy. MEG activity was retrospectively reconstructed using beamforming to obtain VEs at the anatomical locations corresponding to those of SEEG electrodes. Spectral, functional connectivity and functional network properties were obtained for both, MEG-VEs and SEEG time series, and their correlation and reliability were established. Based on these properties, the approximation of the SOZ was characterized by the differences between RA and non-RA (NRA). We found significant positive correlation and reliability between MEG-VEs and SEEG spectral measures (particularly in delta [0.5-4 Hz], alpha2 [10-13 Hz], and beta [13-30 Hz] bands) and broadband functional connectivity. Both modalities showed significantly slower activity and a tendency towards increased broadband functional connectivity in the RA compared to the NRA. Our findings show that spectral and functional connectivity properties of non-invasively obtained MEG-VEs match those of invasive SEEG recordings, and can characterize the SOZ. This suggests that MEG-VEs might be used for optimal SEEG planning and fewer depth electrode implantations, making the localization of the SOZ safer and more successful.

Localization of the Epileptogenic Zone Using Interictal MEG and Machine Learning in a Large Cohort of Drug-Resistant Epilepsy Patients.

Objective: Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the epileptogenic zone, thereby enhancing the chance of seizure freedom. Methods: Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results: The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67-60.22%) for SVM and 60.34% (59.98-60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients [43.77% accuracy (42.08-45.45%) for SVM and 49.03% (47.25-50.82%) for random forest]. Significance: All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the epileptogenic zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.

Quantification of unidirectional nonlinear associations between multidimensional signals.

In this paper, we present a rigorous, general definition of the nonlinear association index, known as h2. Proving equivalence between different definitions we show that the index measures the best dynamic range of any nonlinear map between signals. We present also a construction for removing the influence of one signal from another, providing, thus, the basis of an independent component analysis. Our definition applies to arbitrary multidimensional vector-valued signals and depends on an aperture function. In this way, the bin-related classic definition of h2 can be generalized. We show that upon choosing suitable aperture functions the bin-related intuitive definition can be deduced. Special attention is dedicated to the direction of the association index that in general is taken in only one sense. We show that for linearly coupled signals high associations are always bidirectional. As a consequence, high asymmetric nonlinear associations are indicators of nonlinear relations, possibly critical, between the dynamic systems underlying the measured signals. We give a simple simulated example to illustrate this property. As a potential clinical application, we show that unidirectional associations between electroencephalogram (EEG) and electromyogram (EMG) recorded from patient with pharmacologically intractable epilepsy can be used to study the cortical involvement in the generation of motor seizures.

The use of single bipolar scalp derivation for the detection of ictal events during long-term EEG monitoring.

Epilepsy is difficult to diagnose using routine EEG recordings of short duration in patients who have low seizure frequency. Long-term EEG may be useful but is impractical in an out-of-hospital setting. We investigated whether single-channel scalp EEG placed behind the earlobe is suitable for seizure identification during prolonged EEG monitoring. Scalp EEG samples were selected from subjects over 15 years of age, and comprised two segments of either background followed by seizure or background followed by background. Bipolar EEG derivations in three directions (F8-T8, C4-T8 and T8-P8) were evaluated for the presence of a seizure by two experienced reviewers. For each EEG segment containing a seizure, one pair of electrodes was oriented towards the suspected region of seizure onset, while two pairs of electrodes were oriented elsewhere. The EEG data contained five frontally localized seizures, five parietal, five temporal, two occipital, and four primary or secondary generalized seizures. The sensitivity and specificity for recognition of seizures was 86% and 95% for Reviewer 1, and 79% and 99% for Reviewer 2, respectively. When identifying a seizure with the lead orientation towards the region of seizure onset, both reviewers identified 20 out of 21 seizures (95%). When the lead was not oriented towards the region of seizure onset, the reviewers identified 34 and 30 out of 42 ictal records correctly, respectively. These results suggest that it is possible to identify epileptic seizures by bipolar EEG derivation using only two scalp electrodes. Lead orientation towards the suspected region of seizure onset is important for optimal detection sensitivity.

Altered GABAA receptor density and unaltered blood-brain barrier [11C]flumazenil transport in drug-resistant epilepsy patients with mesial temporal sclerosis.

Studies in rodents suggest that flumazenil is a P-glycoprotein substrate at the blood-brain barrier. This study aimed to assess whether [11C]flumazenil is a P-glycoprotein substrate in humans and to what extent increased P-glycoprotein function in epilepsy may confound interpretation of clinical [11C]flumazenil studies used to assess gamma-aminobutyric acid A receptors. Nine drug-resistant patients with epilepsy and mesial temporal sclerosis were scanned twice using [11C]flumazenil before and after partial P-glycoprotein blockade with tariquidar. Volume of distribution, nondisplaceable binding potential, and the ratio of rate constants of [11C]flumazenil transport across the blood-brain barrier (K1/k2) were derived for whole brain and several regions. All parameters were compared between pre- and post-tariquidar scans. Regional results were compared between mesial temporal sclerosis and contralateral sides. Tariquidar significantly increased global K1/k2 (+23%) and volume of distribution (+10%), but not nondisplaceable binding potential. At the mesial temporal sclerosis side volume of distribution and nondisplaceable binding potential were lower in hippocampus (both ∼-19%) and amygdala (both ∼-16%), but K1/k2 did not differ, suggesting that only regional gamma-aminobutyric acid A receptor density is altered in epilepsy. In conclusion, although [11C]flumazenil appears to be a (weak) P-glycoprotein substrate in humans, this does not seem to affect its role as a tracer for assessing gamma-aminobutyric acid A receptor density.

Dynamics of epileptic phenomena determined from statistics of ictal transitions.

In this paper, we investigate the dynamical scenarios of transitions between normal and paroxysmal state in epilepsy. We assume that some epileptic neural network are bistable i.e., they feature two operational states, ictal and interictal that co-exist. The transitions between these two states may occur according to a Poisson process, a random walk process or as a result of deterministic time-dependent mechanisms. We analyze data from animal models of absence epilepsy, human epilepsies and in vitro models. The distributions of durations of ictal and interictal epochs are fitted with a gamma distribution. On the basis of qualitative features of the fits, we identify the dynamical processes that may have generated the underlying data. The analysis showed that the following hold. 1) The dynamics of ictal epochs differ from those of interictal states. 2) Seizure initiation can be accounted for by a random walk process while seizure termination is often mediated by deterministic mechanisms. 3) In certain cases, the transitions between ictal and interictal states can be modeled by a Poisson process operating in a bistable network. These results imply that exact prediction of seizure occurrence is not possible but termination of an ictal state by appropriate counter stimulation might be feasible.

Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse.

Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time-frequency wavelet spectrum to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as "gold standard". We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.

Material-specific recognition memory deficits elicited by unilateral hippocampal electrical stimulation.

Although the medial temporal lobe is thought to be critical for recognition memory (RM), the specific role of the hippocampus in RM remains uncertain. We investigated the effects of transient unilateral hippocampal electrical stimulation (ES), subthreshold for afterdischarge, on delayed item RM in epilepsy patients implanted with bilateral hippocampal depth electrodes. RM was assessed using a novel computer-controlled test paradigm in which ES to left or right hippocampus was either absent (baseline) or synchronized with item presentation. Subsequent yes-no RM performance revealed a double dissociation between material-specific RM and the lateralization of ES. Left hippocampal ES produced word RM deficits, whereas right hippocampal ES produced face RM deficits. Our findings provide the first demonstration in humans that selective unilateral stimulation-induced hippocampal disruption is sufficient to produce impairments on delayed RM tasks and provide support for the material-specific laterality of hippocampal function with respect to RM.

What does an epileptiform spike look like in MEG? Comparison between coincident EEG and MEG spikes.

Recent investigations suggest that there are differences between the characteristics of EEG and MEG epileptiform spikes. The authors performed an objective characterization of the morphology of epileptiform spikes recorded simultaneously in both EEG and MEG to determine whether they present the same morphologic characteristics. Based on a stepwise approach, the authors performed a computer analysis of EEG and MEG of a set of coincident epileptiform transients selected by a senior clinical neurophysiologist in recordings of three patients with drug-resistant epilepsy. A computer-based algorithm was applied to extract parameters that could be used to describe quantitatively the morphology of the transients, followed by a statistical comparison over the extracted metrics of the EEG and MEG waveforms. EEG and MEG coincident events were statistically different with respect to several morphologic characteristics, such as duration, sharpness, and shape. The differences found appear to be a consequence of MEG signals not being influenced by volume propagation through the tissues with different conductivities that surround the brain, compared with EEG, and of the different orientation of the underlying dipolar sources. The results indicate that visual inspection of MEG spikes and automatic spike-detector algorithms should use criteria adapted to the specific characteristics of the MEG, and not simply those used on conventional EEG.

Automated Seizure Onset Zone Approximation Based on Nonharmonic High-Frequency Oscillations in Human Interictal Intracranial EEGs.

A novel automated algorithm is proposed to approximate the seizure onset zone (SOZ), while providing reproducible output. The SOZ, a surrogate marker for the epileptogenic zone (EZ), was approximated from intracranial electroencephalograms (iEEG) of nine people with temporal lobe epilepsy (TLE), using three methods: (1) Total ripple length (TRL): Manually segmented high-frequency oscillations, (2) Rippleness (R): Area under the curve (AUC) of the autocorrelation functions envelope, and (3) Autoregressive model residual variation (ARR, novel algorithm): Time-variation of residuals from autoregressive models of iEEG windows. TRL, R, and ARR results were compared in terms of separability, using Kolmogorov-Smirnov tests, and performance, using receiver operating characteristic (ROC) curves, to the gold standard for SOZ delineation: visual observation of ictal video-iEEGs. TRL, R, and ARR can distinguish signals from iEEG channels located within the SOZ from those outside it (p < 0.01). The ROC AUC was 0.82 for ARR, while it was 0.79 for TRL, and 0.64 for R. ARR outperforms TRL and R, and may be applied to identify channels in the SOZ automatically in interictal iEEGs of people with TLE. ARR, interpreted as evidence for nonharmonicity of high-frequency EEG components, could provide a new way to delineate the EZ, thus contributing to presurgical workup.

Enhancement of phase clustering in the EEG/MEG gamma frequency band anticipates transitions to paroxysmal epileptiform activity in epileptic patients with known visual sensitivity.

A new analytical method for quantifying brain activity from magnetoelectroencephalogram (MEG) and electroencephalogram (EEG) recordings during periodic light stimulation is proposed. It consists in estimating the phase clustering of harmonically related frequency components of a subject's MEG/EEG responses evoked by the light stimulation. The method was developed to test the hypothesis that changes in the dynamics of brain systems in the course of intermittent photic stimulation (IPS) may precede the transition to seizure activity in photosensitive patients. We assumed that such changes would be reflected in the phase of harmonic components of the evoked responses. Thus, we determined the phase clustering for different harmonic components of these MEG/EEG signals. We found that the patients who develop epileptiform discharges during IPS present an enhancement of the phase clustering index at the gamma frequency band, compared with that at the driving frequency. We introduce a quantity--relative phase clustering index (rPCI)--by means of which this enhancement can be quantified. We argue that this quantity reflects the degree of excitability of the underlying dynamical system and it can indicate presence of nonlinear dynamics. rPCI can be applied to detect transitions to epileptic seizure activity in patients with known sensitivity to IPS.

Dynamical diseases of brain systems: different routes to epileptic seizures.

In this overview, we consider epilepsies as dynamical diseases of brain systems since they are manifestations of the property of neuronal networks to display multistable dynamics. To illustrate this concept we may assume that at least two states of the epileptic brain are possible: the interictal state characterized by a normal, apparently random, steady-state electroencephalography (EEG) ongoing activity, and the ictal state, that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called, in neurology, a seizure. The transition between these two states can either occur: 1) as a continuous sequence of phases, like in some cases of mesial temporal lobe epilepsy (MTLE); or 2) as a sudden leap, like in most cases of absence seizures. In the mathematical terminology of nonlinear systems, we can say that in the first case the system's attractor gradually deforms from an interictal to an ictal attractor. The causes for such a deformation can be either endogenous or external. In this type of ictal transition, the seizure possibly may be anticipated in its early, preclinical phases. In the second case, where a sharp critical transition takes place, we can assume that the system has at least two simultaneous interictal and ictal attractors all the time. To which attractor the trajectories converge, depends on the initial conditions and the system's parameters. An essential question in this scenario is how the transition between the normal ongoing and the seizure activity takes place. Such a transition can occur either due to the influence of external or endogenous factors or due to a random perturbation and, thus, it will be unpredictable. These dynamical changes may not be detectable from the analysis of the ongoing EEG, but they may be observable only by measuring the system's response to externally administered stimuli. In the special cases of reflex epilepsy, the leap between the normal ongoing attractor and the ictal attractor is caused by a well-defined external perturbation. Examples from these different scenarios are presented and discussed.

MEG network differences between low- and high-grade glioma related to epilepsy and cognition.

OBJECTIVE: To reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups. METHODS: We constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients. RESULTS: LGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4-8 Hz), similar to NGL patients. HGG patients' networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients. CONCLUSION: Lesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients' networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline.