Automated video-based detection of nocturnal motor seizures in children.

Seizure detection devices can improve epilepsy care, but wearables are not always tolerated. We previously demonstrated good performance of a real-time video-based algorithm for detection of nocturnal convulsive seizures in adults with learning disabilities. The algorithm calculates the relative frequency content based on the group velocity reconstruction from video-sequence optical flow. We aim to validate the video algorithm on nocturnal motor seizures in a pediatric population. We retrospectively analyzed the algorithm performance on a database including 1661 full recorded nights of 22 children (age = 3-17 years) with refractory epilepsy at home or in a residential care setting. The algorithm detected 118 of 125 convulsions (median sensitivity per participant = 100%, overall sensitivity = 94%, 95% confidence interval = 61%-100%) and identified all 135 hyperkinetic seizures. Most children had no false alarms; 81 false alarms occurred in six children (median false alarm rate [FAR] per participant per night = 0 [range = 0-0.47], overall FAR = 0.05 per night). Most false alarms (62%) were behavior-related (eg, awake and playing in bed). Our noncontact detection algorithm reliably detects nocturnal epileptic events with only a limited number of false alarms and is suitable for real-time use.

Phase clustering in transcranial magnetic stimulation-evoked EEG responses in genetic generalized epilepsy and migraine.

BACKGROUND: Epilepsy and migraine are paroxysmal neurological conditions associated with disturbances of cortical excitability. No useful biomarkers to monitor disease activity in these conditions are available. Phase clustering was previously described in electroencephalographic (EEG) responses to photic stimulation and may be a potential epilepsy biomarker. OBJECTIVE: The objective of this study was to investigate EEG phase clustering in response to transcranial magnetic stimulation (TMS), compare it with photic stimulation in controls, and explore its potential as a biomarker of genetic generalized epilepsy or migraine with aura. METHODS: People with (possible) juvenile myoclonic epilepsy (JME), migraine with aura, and healthy controls underwent single-pulse TMS with concomitant EEG recording during the interictal period. We compared phase clustering after TMS with photic stimulation across the groups using permutation-based testing. RESULTS: We included eight people with (possible) JME (five off medication, three on), 10 with migraine with aura, and 37 controls. The TMS and photic phase clustering spectra showed significant differences between those with epilepsy without medication and controls. Two phase clustering-based indices successfully captured these differences between groups. One participant was tested multiple times. In this case, the phase clustering-based indices were inversely correlated with the dose of antiepileptic medication. Phase clustering did not differ between people with migraine and controls. CONCLUSION: We present methods to quantify phase clustering using TMS-EEG and show its potential value as a measure of brain network activity in genetic generalized epilepsy. Our results suggest that the higher propensity to phase clustering is not shared between genetic generalized epilepsy and migraine.

Automated remote fall detection using impact features from video and audio.

Elderly people and people with epilepsy may need assistance after falling, but may be unable to summon help due to injuries or impairment of consciousness. Several wearable fall detection devices have been developed, but these are not used by all people at risk. We present an automated analysis algorithm for remote detection of high impact falls, based on a physical model of a fall, aiming at universality and robustness. Candidate events are automatically detected and event features are used as classifier input. The algorithm uses vertical velocity and acceleration features from optical flow outputs, corrected for distance from the camera using moving object size estimation. A sound amplitude feature is used to increase detector specificity. We tested the performance and robustness of our trained algorithm using acted data from a public database and real life data with falls resulting from epilepsy and with daily life activities. Applying the trained algorithm to the acted dataset resulted in 90% sensitivity for detection of falls, with 92% specificity. In the real life data, six/nine falls were detected with a specificity of 99.7%; there is a plausible explanation for not detecting each of the falls missed. These results reflect the algorithm's robustness and confirms the feasibility of detecting falls using this algorithm.

Automated video-based detection of nocturnal convulsive seizures in a residential care setting.

People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.

Long-interval intracortical inhibition as biomarker for epilepsy: a transcranial magnetic stimulation study.

Cortical excitability, as measured by transcranial magnetic stimulation combined with electromyography, is a potential biomarker for the diagnosis and follow-up of epilepsy. We report on long-interval intracortical inhibition data measured in four different centres in healthy controls (n = 95), subjects with refractory genetic generalized epilepsy (n = 40) and with refractory focal epilepsy (n = 69). Long-interval intracortical inhibition was measured by applying two supra-threshold stimuli with an interstimulus interval of 50, 100, 150, 200 and 250 ms and calculating the ratio between the response to the second (test stimulus) and to the first (conditioning stimulus). In all subjects, the median response ratio showed inhibition at all interstimulus intervals. Using a mixed linear-effects model, we compared the long-interval intracortical inhibition response ratios between the different subject types. We conducted two analyses; one including data from the four centres and one excluding data from Centre 2, as the methods in this centre differed from the others. In the first analysis, we found no differences in long-interval intracortical inhibition between the different subject types. In all subjects, the response ratios at interstimulus intervals 100 and 150 ms showed significantly more inhibition than the response ratios at 50, 200 and 250 ms. Our second analysis showed a significant interaction between interstimulus interval and subject type (P = 0.0003). Post hoc testing showed significant differences between controls and refractory focal epilepsy at interstimulus intervals of 100 ms (P = 0.02) and 200 ms (P = 0.04). There were no significant differences between controls and refractory generalized epilepsy groups or between the refractory generalized and focal epilepsy groups. Our results do not support the body of previous work that suggests that long-interval intracortical inhibition is significantly reduced in refractory focal and genetic generalized epilepsy. Results from the second analysis are even in sharper contrast with previous work, showing inhibition in refractory focal epilepsy at 200 ms instead of facilitation previously reported. Methodological differences, especially shorter intervals between the pulse pairs, may have contributed to our inability to reproduce previous findings. Based on our results, we suggest that long-interval intracortical inhibition as measured by transcranial magnetic stimulation and electromyography is unlikely to have clinical use as a biomarker of epilepsy.

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.

Non-harmonicity in high-frequency components of the intra-operative corticogram to delineate epileptogenic tissue during surgery.

OBJECTIVE: We aimed to test the potential of auto-regressive model residual modulation (ARRm), an artefact-insensitive method based on non-harmonicity of the high-frequency signal, to identify epileptogenic tissue during surgery. METHODS: Intra-operative electrocorticography (ECoG) of 54 patients with refractory focal epilepsy were recorded pre- and post-resection at 2048Hz. The ARRm was calculated in one-minute epochs in which high-frequency oscillations (HFOs; fast ripples, 250-500Hz; ripples, 80-250Hz) and spikes were marked. We investigated the pre-resection fraction of HFOs and spikes explained by the ARRm (h2-index). A general ARRm threshold was set and used to compare the ARRm to surgical outcome in post-resection ECoG (Pearson X2). RESULTS: ARRm was associated strongest with the number of fast ripples in pre-resection ECoG (h2=0.80, P<0.01), but also with ripples and spikes. An ARRm threshold of 0.47 yielded high specificity (95%) with 52% sensitivity for channels with fast ripples. ARRm values >0.47 were associated with poor outcome at channel and patient level (both P<0.01) in post-resection ECoG. CONCLUSIONS: The ARRm algorithm might enable intra-operative delineation of epileptogenic tissue. SIGNIFICANCE: ARRm is the first unsupervised real-time analysis that could provide an intra-operative, 'on demand' interpretation per electrode about the need to remove underlying tissue to optimize the chance of seizure freedom.

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.

Gap Junctions as Common Cause of High-Frequency Oscillations and Epileptic Seizures in a Computational Cascade of Neuronal Mass and Compartmental Modeling.

High frequency oscillations (HFO) appear to be a promising marker for delineating the seizure onset zone (SOZ) in patients with localization related epilepsy. It remains, however, a purely observational phenomenon and no common mechanism has been proposed to relate HFOs and seizure generation. In this work we show that a cascade of two computational models, one on detailed compartmental scale and a second one on neural mass scale can explain both the autonomous generation of HFOs and the presence of epileptic seizures as emergent properties. To this end we introduce axonal-axonal gap junctions on a microscopic level and explore their impact on the higher level neural mass model (NMM). We show that the addition of gap junctions can generate HFOs and simultaneously shift the operational point of the NMM from a steady state network into bistable behavior that can autonomously generate epileptic seizures. The epileptic properties of the system, or the probability to generate epileptic type of activity, increases gradually with the increase of the density of axonal-axonal gap junctions. We further demonstrate that ad hoc HFO detectors used in previous studies are applicable to our simulated data.

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.

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.

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.

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.

A trained spin-glass model for grouping of image primitives.

A method is presented that uses grouping to improve local classification of image primitives. The grouping process is based upon a spin-glass system, where the image primitives are treated as possessing a spin. The system is subject to an energy functional consisting of a local and a bilocal part, allowing interaction between the image primitives. Instead of defining the state of lowest energy as the grouping result, the mean state of the system is taken. In this way, instabilities caused by multiple minima in the energy are being avoided. The means of the spins are taken as the a posteriori probabilities for the grouping result. In the paper, it is shown how the energy functional can be learned from example data. The energy functional is defined in such a way that, in case of no interactions between the elements, the means of the spins equal the a priori local probabilities. The grouping process enables the fusion of the a priori local and bilocal probabilities into the a posteriori probabilities. The method is illustrated both on grouping of line elements in synthetic images and on vessel detection in retinal fundus images.

Physiological changes in chronic epileptic rats are prominent in superficial layers of the medial entorhinal area.

PURPOSE: We investigated whether the functional network properties of the medial entorhinal area (MEA) of the entorhinal cortex were altered in a rat model of chronic epilepsy that is characterized by extensive cell loss in MEA layer III. METHODS: Responses were evoked in the entorhinal cortex by electrical stimulation of the subiculum in anesthetized chronic epileptic rats, 2-4 months after status epilepticus, induced by systemic kainate (KA) injections. Laminar field potentials were measured using a 16-channel silicon probe that covered all six layers of the MEA; an estimate of the local transmembrane currents was made using current source density analysis. RESULTS: Double-pulse stimulation of the subiculum evoked responses in deep and superficial layers of the MEA in control and KA rats. A current sink in layer I and at the border of layer I and II that was induced by antidromic activation of MEA-II, was much more prominent in KA rats with extensive neuronal loss in MEA-III than in control rats or KA rats with minor MEA-III loss. Furthermore, KA rats that displayed MEA-III loss presented a series of oscillations induced by subicular stimulation in the beta/gamma-frequency range (20-100 Hz), which were confined to superficial layers of MEA. These oscillations were never observed in control rats or KA rats with minor MEA-III loss. CONCLUSIONS: These results indicate that the observed alterations in the superficial MEA responses to subiculum stimulation and the occurrence of beta/gamma-oscillations are related phenomena, which are a consequence of altered and impaired inhibition within these MEA layers in chronic epileptic rats.

Photosensitivity and visually induced seizures.

PURPOSE OF REVIEW: Interest in visually induced seizures has increased in recent years as a result of the increasing number of precipitants in our modern environment. This review addresses new developments in this field with special attention given to the emergence of new diagnostic, therapeutic and preventive approaches; it also emphasizes the importance of this condition as a public health issue. RECENT FINDINGS: Current evidence indicates the presence of two different mechanisms of photosensitivity, one dependent on luminance changes and the other on wavelength. Both mechanisms may be active in the same patient, although one may be dominant. Magnetoencephalography studies revealed an enhancement in gamma frequency preceding the development of a paroxysmal response as well as underlying uncomfortable visual illusions, suggesting that a loss of control over high-frequency oscillatory processes may be involved in the genesis of both types of phenomenon. The genetics underlying this trait remain to be determined. More precise definition of different phenotypes should help in this search. Recognition of the risks posed by the audiovisual environment for induction of seizures in photosensitive individuals, who may not even be aware of their condition, will prompt further development of guidelines and devices designed to prevent the occurrence of seizures triggered by dangerous video sequences. SUMMARY: Photosensitive epilepsy constitutes a unique benchmark model in which to address important issues in human epileptogenesis. The scope of the health risks posed by the modern audiovisual environment is increasingly being recognized, and further development of guidelines and regulations to control exposure to provocative materials are warranted.

Magnetoencephalography: an investigational tool or a routine clinical technique?

Magnetoencephalography (MEG) is a relatively novel noninvasive technique, with a much shorter history than EEG, that conveys neurophysiological information complementary to that provided by EEG, with high temporal and spatial resolution. Despite its a priori, highly competitive profile, the role of MEG in the clinical setting is still controversial. We briefly review the major obstacles MEG faces in becoming a routine clinical test and the different strategies needed to bypass them. The high cost and complexity associated with MEG equipment are powerful hindrances to wide acceptance of this relatively new technique in clinical practice. The most straightforward advantage is based on the relative facility of MEG recordings in the process of source localization, which also carries some degree of uncertainty, thus partly explaining why the development of clinical applications of MEG has been so slow. Obviously, a decrease in the cost and the elaboration of semiautomatic protocols that could reduce the complexity of the studies and favor the development of consensual strategies, as well as a major effort on the part of clinicians to identify clinical issues where MEG could be decisive, would be most welcome.

Epilepsies as dynamical diseases of brain systems: basic models of the transition between normal and epileptic activity.

PURPOSE: The occurrence of abnormal dynamics in a physiological system can become manifest as a sudden qualitative change in the behavior of characteristic physiologic variables. We assume that this is what happens in the brain with regard to epilepsy. We consider that neuronal networks involved in epilepsy possess multistable dynamics (i.e., they may display several dynamic states). To illustrate this concept, we may assume, for simplicity, that at least two states are possible: an interictal one characterized by a normal, apparently random, steady-state of ongoing activity, and another one that is characterized by the paroxysmal occurrence of a synchronous oscillations (seizure). METHODS: By using the terminology of the mathematics of nonlinear systems, we can say that such a bistable system has two attractors, to which the trajectories describing the system's output converge, depending on initial conditions and on the system's parameters. In phase-space, the basins of attraction corresponding to the two states are separated by what is called a "separatrix." We propose, schematically, that the transition between the normal ongoing and the seizure activity can take place according to three basic models: Model I: In certain epileptic brains (e.g., in absence seizures of idiopathic primary generalized epilepsies), the distance between "normal steady-state" and "paroxysmal" attractors is very small in contrast to that of a normal brain (possibly due to genetic and/or developmental factors). In the former, discrete random fluctuations of some variables can be sufficient for the occurrence of a transition to the paroxysmal state. In this case, such seizures are not predictable. Model II and model III: In other kinds of epileptic brains (e.g., limbic cortex epilepsies), the distance between "normal steady-state" and "paroxysmal" attractors is, in general, rather large, such that random fluctuations, of themselves, are commonly not capable of triggering a seizure. However, in these brains, neuronal networks have abnormal features characterized by unstable parameters that are very vulnerable to the influence of endogenous (model II) and/or exogenous (model III) factors. In these cases, these critical parameters may gradually change with time, in such a way that the attractor can deform either gradually or suddenly, with the consequence that the distance between the basin of attraction of the normal state and the separatrix tends to zero. This can lead, eventually, to a transition to a seizure. RESULTS: The changes of the system's dynamics preceding a seizure in these models either may be detectable in the EEG and thus the route to the seizure may be predictable, or may be unobservable by using only measurements of the dynamical state. It is thinkable, however, that in some cases, changes in the excitability state of the underlying networks may be uncovered by using appropriate stimuli configurations before changes in the dynamics of the ongoing EEG activity are evident. A typical example of model III that we discuss here is photosensitive epilepsy. CONCLUSIONS: We present an overview of these basic models, based on neurophysiologic recordings combined with signal analysis and on simulations performed by using computational models of neuronal networks. We pay especial attention to recent model studies and to novel experimental results obtained while analyzing EEG features preceding limbic seizures and during intermittent photic stimulation that precedes the transition to paroxysmal epileptic activity.

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.