Temporo-spatial patterns of pre-ictal spike activity in human temporal lobe epilepsy

Epileptic seizure clustering and accumulation at transition from activity to rest in GAERS rats

Knowing when seizures occur may help patients and can also provide insight into epileptogenesis mechanisms. We recorded seizures over periods of several days in the Genetic Absence Epileptic Rat from Strasbourg (GAERS) model of absence epilepsy, while we monitored behavioral activity with a combined head accelerometer (ACCEL), neck electromyogram (EMG), and electrooculogram (EOG). The three markers consistently discriminated between states of behavioral activity and rest. Both GAERS and control Wistar rats spent more time in rest (55-66%) than in activity (34-45%), yet GAERS showed prolonged continuous episodes of activity (23 vs. 18 min) and rest (34 vs. 30 min). On average, seizures lasted 13 s and were separated by 3.2 min. Isolated seizures were associated with a decrease in the power of the activity markers from steep for ACCEL to moderate for EMG and weak for EOG, with ACCEL and EMG power changes starting before seizure onset. Seizures tended to occur in bursts, with the probability of seizing significantly increasing around a seizure in a window of ±4 min. Furthermore, the seizure rate was strongly increased for several minutes when transitioning from activity to rest. These results point to mechanisms that control behavioral states as determining factors of seizure occurrence.

Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study

BACKGROUND

Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices.

METHOD

Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed.

FINDINGS

The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance.

INTERPRETATION

The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications.

FUNDING

This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy

Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.

Interictal-period-activated neuronal ensemble in piriform cortex retards further seizure development

Epileptic networks are characterized as having two states, seizures or more prolonged interictal periods. However, cellular mechanisms underlying the contribution of interictal periods to ictal events remain unclear. Here, we use an activity-dependent labeling technique combined with genetically encoded effectors to characterize and manipulate neuronal ensembles recruited by focal seizures (FS-Ens) and interictal periods (IP-Ens) in piriform cortex, a region that plays a key role in seizure generation. Ca²⁺ activities and histological evidence reveal a disjointed correlation between the two ensembles during FS dynamics. Optogenetic activation of FS-Ens promotes further seizure development, while IP-Ens protects against it. Interestingly, both ensembles are functionally involved in generalized seizures (GS) due to circuit rearrangement. IP-Ens bidirectionally modulates FS but not GS by controlling coherence with hippocampus. This study indicates that the interictal state may represent a seizure-preventing environment, and the interictal-activated ensemble may serve as a potential therapeutic target for epilepsy.

High-frequency oscillations in the ripple bands and amplitude information coding: Toward a biomarker of maximum entropy in the preictal signals

Intracranial electroencephalography (iEEG) can directly record local field potentials (LFPs) from a large set of neurons in the vicinity of the electrode. To search for possible epileptic biomarkers and to determine the epileptogenic zone that gives rise to seizures, we investigated the dynamics of basal and preictal signals. For this purpose, we explored the dynamics of the recorded time series for different frequency bands considering high-frequency oscillations (HFO) up to 240 Hz. We apply a Hilbert transform to study the amplitude and phase of the signals. The dynamics of the different frequency bands in the time causal entropy-complexity plane, H × C, is characterized by comparing the dynamical evolution of the basal and preictal time series. As the preictal states evolve closer to the time in which the epileptic seizure starts, the, H × C, dynamics changes for the higher frequency bands. The complexity evolves to very low values and the entropy becomes nearer to its maximal value. These quasi-stable states converge to equiprobable states when the entropy is maximal, and the complexity is zero. We could, therefore, speculate that in this case, it corresponds to the minimization of Gibbs free energy. In this case, the maximum entropy is equivalent to the principle of minimum consumption of resources in the system. We can interpret this as the nature of the system evolving temporally in the preictal state in such a way that the consumption of resources by the system is minimal for the amplitude in frequencies between 220-230 and 230-240 Hz.

Theta and gamma hippocampal-neocortical oscillations during the episodic-like memory test: Impairment in epileptogenic rats

Cortical oscillations in different frequency bands have been shown to be intimately involved in exploration of environment and cognition. Here, the local field potentials in the hippocampus, the medial prefrontal cortex (mPFC), and the medial entorhinal cortex (mEC) were recorded simultaneously in rats during the execution of the episodic-like memory task. The power of theta (~4-10 Hz), slow gamma (~25-50 Hz), and fast gamma oscillations (~55-100 Hz) was analyzed in all structures examined. Particular attention was paid to the theta coherence between three mentioned structures. The modulation of the power of gamma rhythms by the phase of theta cycle during the execution of the episodic-like memory test by rats was also closely studied. Healthy rats and rats one month after kainate-induced status epilepticus (SE) were examined. Paroxysmal activity in the hippocampus (high amplitude interictal spikes), excessive excitability of animals, and the death of hippocampal and dentate granular cells in rats with kainate-evoked SE were observed, which indicated the development of seizure focus in the hippocampus (epileptogenesis). One month after SE, the rats exhibited a specific impairment of episodic memory for the what-where-when triad: unlike healthy rats, epileptogenic SE animals did not identify the objects during the test. This impairment was associated with the changes in the characteristics of theta and gamma rhythms and specific violation of theta coherence and theta/gamma coupling in these structures in comparison with the healthy animals. We believe that these disturbances in the cortical areas play a role in episodic memory dysfunction in kainate-treated animals. These findings can shed light on the mechanisms of cognitive deficit during epileptogenesis.

Online Prediction of Lead Seizures from iEEG Data

We describe a novel system for online prediction of lead seizures from long-term intracranial electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This study adopts new specification of lead seizures, reflecting strong clustering of seizures in observed data. This clustering results in fewer lead seizures (~7 lead seizures per dog), and hence new challenges for online seizure prediction, that are addressed in the proposed system. In particular, the machine learning part of the system is implemented using the group learning method suitable for modeling sparse and noisy seizure data. In addition, several modifications for the proposed system are introduced to cope with the non-stationarity of a noisy iEEG signal. They include: (1) periodic retraining of the SVM classifier using most recent training data; (2) removing samples with noisy labels from training data; and (3) introducing a new adaptive post-processing technique for combining many predictions made for 20 s windows into a single prediction for a 4 h segment. Application of the proposed system requires only two lead seizures for training the initial model, and results in high prediction performance for all four dogs (with mean 0.84 sensitivity, 0.27 time-in-warning, and 0.78 false-positive rate per day). The proposed system achieves accurate prediction of lead seizures during long-term test periods, 3–16 lead seizures during a 169–364 day test period, whereas earlier studies did not differentiate between lead vs. non-lead seizures and used much shorter test periods (~few days long).

An Overview of EEG-based Machine Learning Methods in Seizure Prediction and Opportunities for Neurologists in this Field

The unpredictability of epileptic seizures is one of the most problematic aspects of the field of epilepsy. Methods or devices capable of detecting seizures minutes before they occur may help prevent injury or even death and significantly improve the quality of life. Machine learning (ML) is an emerging technology that can markedly enhance algorithm performance by interpreting data. ML has gained increasing attention from medical researchers in recent years. Its epilepsy applications range from the localization of the epileptic region, predicting the medical or surgical outcome of epilepsy, and automated electroencephalography (EEG) analysis to seizure prediction. While ML has good prospects with regard to detecting epileptic seizures via EEG signals, many clinicians are still unfamiliar with this field. This work briefly summarizes the history and recent significant progress made in this field and clarifies the essential components of the automatic seizure detection system using ML methodologies for clinicians. This review also proposes how neurologists can actively contribute to ensure improvements in seizure prediction using EEG-based ML.

Seizure Forecasting Using Long-Term Electroencephalography and Electrocardiogram Data

Electroencephalography (EEG) has been used to forecast seizures with varying success. There is an increasing interest to use electrocardiogram (ECG) to help with seizure forecasting. The neural and cardiovascular systems may exhibit critical slowing, which is measured by an increase in variance and autocorrelation of the system, when change from a normal state to an ictal state. To forecast seizures, the variance and autocorrelation of long-term continuous EEG and ECG data from 16 patients were used for analysis. The average period of recordings was 161.9 h, with an average of 9 electrographic seizures in an individual patient. The relationship between seizure onset times and phases of variance and autocorrelation in EEG and ECG data was investigated. The results of forecasting models using critical slowing features, seizure circadian features, and combined critical slowing and circadian features were compared using the receiver-operating characteristic curve. The results demonstrated that the best forecaster was patient-specific and the average area under the curve (AUC) of the best forecaster across patients was 0.68. In 50% of patients, circadian forecasters had the best performance. Critical slowing forecaster performed best in 19% of patients. Combined forecaster achieved the best performance in 31% of patients. The results of this study may help to advance the field of seizure forecasting and lead to the improved quality of life of people who suffer from epilepsy.

Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.

Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states

Epileptic seizures are known for their unpredictable nature. However, recent research provides that the transition to seizure event is not random but the result of evidence accumulations. Therefore, a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes, and the amplitude. In this study, spike rate is used as the indicator to anticipate seizures in electroencephalogram (EEG) signal. Spikes detection step is used in EEG signal during interictal, preictal, and ictal periods followed by a mean filter to smooth the spike number. The maximum spike rate in interictal periods is used as an indicator to predict seizures. When the spike number in the preictal period exceeds the threshold, an alarm is triggered. Using the CHB-MIT database, the proposed approach has ensured 92% accuracy in seizure prediction for all patients.

Seizure prediction - ready for a new era

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

Automatic detection of the epileptogenic zone: An application of the fingerprint of epilepsy

BACKGROUND

The successful delineation of the epileptogenic zone in epilepsy monitoring is crucial for achieving seizure freedom after epilepsy surgery.

NEW METHOD

We aim to improve epileptogenic zone localization by utilizing a computer-assisted tool for the automated grading of the seizure activity recorded in various locations for 20 patients undergoing stereo electroencephalography. Their epileptic seizures were processed to extract two potential biomarkers. The concentration of these biomarkers from within each patient's implantation were then graded to identify their epileptogenic zone and were compared to the clinical assessment.

RESULTS

Our technique was capable of ranking the clinically defined epileptogenic zone with high accuracy, above 95%, with a true to false positive ratio of 1:1.52, and was effective with both temporal and extra-temporal onset epilepsies.

COMPARISON WITH EXISTING METHOD

We compared our method to two other groups performing localization using similar biomarkers. Our classification metrics, sensitivity and precision together were comparable to both groups and our overall accuracy from a larger population was also higher then both.

CONCLUSIONS

Our method is highly accurate, automated and non-parametric providing clinicians another tool that can be used to help identify the epileptogenic zone in patients undergoing the stereo electroencephalography procedure for epilepsy monitoring.

Interictal spike connectivity in human epileptic neocortex

OBJECTIVE

Interictal spikes are a biomarker of epilepsy, yet their precise roles are poorly understood. Using long-term neocortical recordings from epileptic patients, we investigated the spatial-temporal propagation patterns of interictal spiking.

METHODS

Interictal spikes were detected in 10 epileptic patients. Short time direct directed transfer function was used to map the spatial-temporal patterns of interictal spike onset and propagation across different cortical topographies.

RESULTS

Each patient had unique interictal spike propagation pattern that was highly consistent across times, regardless of the frequency band. High spiking brain regions were often not spike onset regions. We observed frequent spike propagations to shorter distances and that the central sulcus forms a strong barrier to spike propagation. Spike onset and seizure onset seemed to be distinct networks in most cases.

CONCLUSIONS

Patients in epilepsy have distinct and unique network of causal propagation pattern which are very consistent revealing the underlying epileptic network. Although spike are epileptic biomarkers, spike origin and seizure onset seems to be distinct in most cases.

SIGNIFICANCE

Understanding patterns of interictal spike propagation could lead to the identification patient-specific epileptic networks amenable to surgical or other treatments.

Topological Data Analysis of Single-Trial Electroencephalographic Signals

Epilepsy is a neurological disorder that can negatively affect the visual, audial and motor functions of the human brain. Statistical analysis of neurophysiological recordings, such as electroencephalogram (EEG), facilitates the understanding and diagnosis of epileptic seizures. Standard statistical methods, however, do not account for topological features embedded in EEG signals. In the current study, we propose a persistent homology (PH) procedure to analyze single-trial EEG signals. The procedure denoises signals with a weighted Fourier series (WFS), and tests for topological difference between the denoised signals with a permutation test based on their PH features persistence landscapes (PL). Simulation studies show that the test effectively identifies topological difference and invariance between two signals. In an application to a single-trial multichannel seizure EEG dataset, our proposed PH procedure was able to identify the left temporal region to consistently show topological invariance, suggesting that the PH features of the Fourier decomposition during seizure is similar to the process before seizure. This finding is important because it could not be identified from a mere visual inspection of the EEG data and was in fact missed by earlier analyses of the same dataset.

Phase-amplitude coupling and epileptogenesis in an animal model of mesial temporal lobe epilepsy

Polyrhythmic coupling of oscillatory components in electrophysiological signals results from the interactions between neuronal sub-populations within and between cell assemblies. Since the mechanisms underlying epileptic disorders should affect such interactions, abnormal level of cross-frequency coupling is expected to provide a signal marker of epileptogenesis. We measured phase-amplitude coupling (PAC), a form of cross-frequency coupling between neural oscillations, in a rodent model of mesial temporal lobe epilepsy. Sprague-Dawley rats (n = 4, 250-300 g) were injected with pilocarpine (380 mg/kg, i.p) to induce a status epilepticus (SE) that was stopped after 1 h with diazepam (5 mg/kg, s.c.) and ketamine (50 mg/kg, s.c.). Control animals (n = 6) did not receive any injection or treatment. Three days after SE, all animals were implanted with bipolar electrodes in the hippocampal CA3 subfield, entorhinal cortex, dentate gyrus and subiculum. Continuous video/EEG recordings were performed 24/7 at a sampling rate of 2 kHz, over 15 consecutive days. Pilocarpine-treated animals showed interictal spikes (5.25 (±2.5) per minute) and seizures (n = 32) that appeared 7 (±0.8) days after SE. We found that CA3 was the seizure onset zone in most epileptic animals, with stronger ongoing PAC coupling between seizures than in controls (Kruskal-Wallis test: chi2 (1,36) = 46.3, Bonferroni corrected, p < 0.001). Strong PAC in CA3 occurred between the phase of slow-wave oscillations (<1 Hz) and the amplitude of faster rhythms (50-180 Hz), with the strongest bouts of high-frequency activity occurring preferentially on the ascending phase of the slow wave. We also identified that cross-frequency coupling in CA3 (rho = 0.44, p < 0.001) and subiculum (rho = 0.41, p < 0.001) was positively correlated with the daily number of seizures. Overall, our study demonstrates that cross-frequency coupling may represent a signal marker in epilepsy and suggests that this methodology could be transferred to clinical scalp MEG and EEG recordings.

Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG

Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature — diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.

Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features

The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction involves the classification of the preictal and interictal stages. This study aimed to develop an efficient, automatic, quantitative, and individualized approach for preictal/interictal stage identification. Five epileptic children, who had experienced at least 2 episodes of seizures during a 24-hour video EEG recording, were included. Artifact-free preictal and interictal EEG epochs were acquired, respectively, and characterized with 216 global feature descriptors. The best subset of 5 discriminative descriptors was identified. The best subsets showed differences among the patients. Statistical analysis revealed most of the 5 descriptors in each subset were significantly different between the preictal and interictal stages for each patient. The proposed approach yielded weighted averages of 97.50% correctness, 96.92% sensitivity, 97.78% specificity, and 95.45% precision on classifying test epochs. Although the case number was limited, this study successfully integrated a new EEG analytical method to classify preictal and interictal EEG segments and might be used further in predicting the occurrence of seizures.

Effects of antiepileptic drugs on interictal epileptiform discharges in focal epilepsies: an update on current evidence

Interictal epileptiform discharges (IEDs), occurring in the electroencephalograms (EEG) of patients with focal epilepsy, are crucial for diagnosis, while their relationship with seizure severity and recurrence is controversial. The effects of antiepileptic drugs (AEDs) on IEDs are even more debated. In general, it is currently believed by experts in the field that most of the classical AEDs do not significantly affect IEDs occurrence in these patients, and that monitoring their EEG effects during treatment is useless. In this review, we update the existing literature on the effects of classical and newer AEDs on focal IEDs, emphasizing the scarcity of data concerning the latter. We also discuss potential limits of available clinical and experimental data and future perspectives.

Initiation, Propagation, and Termination of Partial (Focal) Seizures

The neurophysiological patterns that correlate with partial (focal) seizures are well defined in humans by standard electroencephalogram (EEG) and presurgical depth electrode recordings. Seizure patterns with similar features are reproduced in animal models of partial seizures and epilepsy. However, the network determinants that support interictal spikes, as well as the initiation, progression, and termination of seizures, are still elusive. Recent findings show that inhibitory networks are prominently involved at the onset of these seizures, and that extracellular changes in potassium contribute to initiate and sustain seizure progression. The end of a partial seizure correlates with an increase in network synchronization, which possibly involves both excitatory and inhibitory mechanisms.

Role of multiple-scale modeling of epilepsy in seizure forecasting

Over the past three decades, a number of seizure prediction, or forecasting, methods have been developed. Although major achievements were accomplished regarding the statistical evaluation of proposed algorithms, it is recognized that further progress is still necessary for clinical application in patients. The lack of physiological motivation can partly explain this limitation. Therefore, a natural question is raised: can computational models of epilepsy be used to improve these methods? Here, we review the literature on the multiple-scale neural modeling of epilepsy and the use of such models to infer physiologic changes underlying epilepsy and epileptic seizures. The authors argue how these methods can be applied to advance the state-of-the-art in seizure forecasting.

Sleep affects cortical source modularity in temporal lobe epilepsy: A high-density EEG study

OBJECTIVE

Interictal epileptiform discharges (IEDs) constitute a perturbation of ongoing cerebral rhythms, usually more frequent during sleep. The aim of the study was to determine whether sleep influences the spread of IEDs over the scalp and whether their distribution depends on vigilance-related modifications in cortical interactions.

METHODS

Wake and sleep 256-channel electroencephalography (EEG) data were recorded in 12 subjects with right temporal lobe epilepsy (TLE) differentiated by whether they had mesial or neocortical TLE. Spikes were selected during wake and sleep. The averaged waking signal was subtracted from the sleep signal and projected on a bidimensional scalp map; sleep and wake spike distributions were compared by using a t-test. The superimposed signal of sleep and wake traces was obtained; the rising phase of the spike, the peak, and the deflections following the spike were identified, and their cortical generator was calculated using low-resolution brain electromagnetic tomography (LORETA) for each group.

RESULTS

A mean of 21 IEDs in wake and 39 in sleep per subject were selected. As compared to wake, a larger IED scalp projection was detected during sleep in both mesial and neocortical TLE (p<0.05). A series of EEG deflections followed the spike, the cortical sources of which displayed alternating activations of different cortical areas in wake, substituted by isolated, stationary activations in sleep in mesial TLE and a silencing in neocortical TLE.

CONCLUSION

During sleep, the IED scalp region increases, while cortical interaction decreases.

SIGNIFICANCE

The interaction of cortical modules in sleep and wake in TLE may influence the appearance of IEDs on scalp EEG; in addition, IEDs could be proxies for cerebral oscillation perturbation.

Dynamic preictal relations in FCD type II: potential for early seizure detection in focal epilepsy

In focal epilepsy, power imbalance between spike-related high frequency oscillations (HFOs) with 80-200 Hz and post-spike slow waves (PSS) in the spike and slow waves selectively occurs within the seizure onset zone (SOZ) before seizure onset. The aim of this study was to elucidate when this preictal power imbalance could occur in the SOZ. We analyzed intracranial EEG data from 6 patients with focal cortical dysplasia. During preictal 3-min period, which was divided into three intervals: 0-1 min, 1-2 min 2-3 min before seizure onset, we performed correlation (Spearman's coefficient) and simple linear regression analyses comparing power of spike-related HFOs and PSS. We analyzed 719 ± 57 (mean ± SD) spike and slow waves per patient, which were obtained from three seizures. In the SOZ, the positive correlation between spike-related HFO and PSS power was drastically reduced during preictal 3-min period, and the slope of regression line (ΔPSS power/ΔHFO power) decreased significantly during 0-1 min before seizure onset (p < 0.05, Steel-Dwass test). The present results indicate that the preictal dynamics of HFO and PSS power in the SOZ may have utility for early seizure detection.

Effect of subthalamic nucleus stimulation on penicillin induced focal motor seizures in primate

BACKGROUND

Drug-resistant motor epilepsies are particularly incapacitating for the patients. In a primate model of focal motor seizures induced by intracortical injection of penicillin, we recently showed that seizures propagated from the motor cortex towards the basal ganglia.

OBJECTIVE

Using the same animal model here, we hypothesized that disruption of subthalamic nucleus (STN) activity by chronic high frequency stimulation (HFS) could modify pathological excessive cortical synchronisation occurring during focal motor seizures, and therefore could reduce seizure activity.

METHODS

Two monkeys were chronically implanted with one electrode positioned into the STN. In each experiment, seizures were induced during 6 hours by injecting penicillin into the motor cortex. During stimulation sessions, HFS-STN was applied at the beginning of penicillin injection.

RESULTS

Our results indicate that HFS-STN improved focal motor seizures by delaying the occurrence of the first seizure, by decreasing the number of seizures by 47% and therefore the total time spent seizing by 53% compared to control. These results argue for a therapeutic use of HFS-STN in motor seizures because they were obtained in a very severe primate model of motor status similar to that seen in human. Furthermore, HFS-STN was much more efficient than direct cortical HFS of the epileptic focus, which we already tested in the same primate model.

CONCLUSIONS

The present study suggests that HFS-STN could be used as an experimental therapy when other therapeutic strategies are not possible or have failed in humans suffering from motor epilepsy but the present study still warrants controlled studies in humans.

Mechanisms of epileptiform synchronization in cortical neuronal networks

Neuronal synchronization supports different physiological states such as cognitive functions and sleep, and it is mirrored by identifiable EEG patterns ranging from gamma to delta oscillations. However, excessive neuronal synchronization is often the hallmark of epileptic activity in both generalized and partial epileptic disorders. Here, I will review the synchronizing mechanisms involved in generating epileptiform activity in the limbic system, which is closely involved in the pathophysiogenesis of temporal lobe epilepsy (TLE). TLE is often associated to a typical pattern of brain damage known as mesial temporal sclerosis, and it is one of the most refractory adult form of partial epilepsy. This epileptic disorder can be reproduced in animals by topical or systemic injection of pilocarpine or kainic acid, or by repetitive electrical stimulation; these procedures induce an initial status epilepticus and cause 1-4 weeks later a chronic condition of recurrent limbic seizures. Remarkably, a similar, seizure-free, latent period can be identified in TLE patients who suffered an initial insult in childhood and develop partial seizures in adolescence or early adulthood. Specifically, I will focus here on the neuronal mechanisms underlying three abnormal types of neuronal synchronization seen in both TLE patients and animal models mimicking this disorder: (i) interictal spikes; (ii) high frequency oscillations (80-500 Hz); and (iii) ictal (i.e., seizure) discharges. In addition, I will discuss the relationship between interictal spikes and ictal activity as well as recent evidence suggesting that specific seizure onsets in the pilocarpine model of TLE are characterized by distinctive patterns of spiking (also termed preictal) and high frequency oscillations.

Preictal surrender of post-spike slow waves to spike-related high-frequency oscillations (80-200 Hz) is associated with seizure initiation

OBJECTIVE

Spike and slow waves consist of a "spike" including high-frequency oscillations (HFOs), which are linked to epileptogenicity and a "post-spike slow wave (PSS)" related to inhibitory activity. The aim of this study was to elucidate the spatiotemporal relationship between spike-related HFOs and PSS in patients with focal cortical dysplasia (FCD) type II.

METHODS

We studied 10 pediatric patients with FCD type II, who underwent extraoperative video-electroencephalography (EEG). We selected spike and slow waves, which included HFOs (80-200 Hz), and performed spike peak-locked averaging 10 times during both 30 s interictal (>1 h apart from seizures) and 30 s preictal periods. We calculated the power of spike-related HFOs and PSS during both periods for the following three areas: (1) inside the seizure-onset zone (SOZ), (2) inside the resection area (RA) but outside SOZ (RA-SOZ), and (3) outside the RA. Between the interictal and preictal periods we performed correlation (Spearman's coefficient) and simple linear regression analyses comparing HFO and PSS power within each area.

RESULTS

A total of 1,614 averaged spike and slow waves were analyzed during both periods. During the interictal periods, there were significant positive correlations between HFO and PSS power in all areas (inside SOZ, r = 0.568; RA-SOZ, r = 0.700; outside RA, r = 0.320). During the preictal periods, the correlation became weaker inside SOZ (r = 0.149) and remained unchanged both inside the RA-SOZ (r = 0.704) and outside RA (r = 0.346). From the interictal to preictal period, the slope (ΔPSS power/ΔHFO power) of the simple regression line decreased inside SOZ (0.349 to 0.051) but increased in RA-SOZ (0.534 to 0.734) and outside RA (0.267 to 0.435).

SIGNIFICANCE

Relative power reduction of PSS to spike-related HFOs in SOZ is relevant for seizure initiation. Our analysis will contribute to future studies of seizure prediction and distinction between pathologic and physiologic HFOs. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.

Functional brain connectivity from EEG in epilepsy: seizure prediction and epileptogenic focus localization

Today, neuroimaging techniques are frequently used to investigate the integration of functionally specialized brain regions in a network. Functional connectivity, which quantifies the statistical dependencies among the dynamics of simultaneously recorded signals, allows to infer the dynamical interactions of segregated brain regions. In this review we discuss how the functional connectivity patterns obtained from intracranial and scalp electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict upcoming seizures and to localize the seizure onset zone. The added value of extracting information that is not visibly identifiable in the EEG data using functional connectivity analysis is stressed. Despite the fact that many studies have showed promising results, we must conclude that functional connectivity analysis has not made its way into clinical practice yet.

A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy

The relation between epileptic spikes and seizures is an important but still unresolved question in epilepsy research. Preclinical and clinical studies have produced inconclusive results on the causality or even on the existence of such a relation. We set to investigate this relation taking in consideration seizure severity and spatial extent of spike rate. We developed a novel automated spike detection algorithm based on morphological filtering techniques and then tested the hypothesis that there is a pre-ictal increase and post-ictal decrease of the spatial extent of spike rate. Peri-ictal (around seizures) spikes were detected from intracranial EEG recordings in 5 patients with temporal lobe epilepsy. The 94 recorded seizures were classified into two classes, based on the percentage of brain sites having higher or lower rate of spikes in the pre-ictal compared to post-ictal periods, with a classification accuracy of 87.4%. This seizure classification showed that seizures with increased pre-ictal spike rate and spatial extent compared to the post-ictal period were mostly (83%) clinical seizures, whereas no such statistically significant (α = 0.05) increase was observed peri-ictally in 93% of sub-clinical seizures. These consistent across patients results show the existence of a causal relation between spikes and clinical seizures, and imply resetting of the preceding spiking process by clinical seizures.

Intracranially recorded interictal spikes: relation to seizure onset area and effect of medication and time of day

OBJECTIVE

The relationship between seizures and interictal spikes remains undetermined. We analyzed intracranial EEG (icEEG) recordings to examine the relationship between the seizure onset area and interictal spikes.

METHODS

80 unselected patients were placed into 5 temporal, 4 extratemporal, and one unlocalized groups based on the location of the seizure onset area. We studied 4-h icEEG epochs, removed from seizures, from day-time and night-time during both on- and off-medication periods. Spikes were detected automatically from electrode contacts sampling the hemisphere ipsilateral to the seizure onset area.

RESULTS

There was a widespread occurrence of spikes over the hemisphere ipsilateral to the seizure onset area. The spatial distributions of spike rates for the different patient groups were different (p<0.0001, chi-square test). The area with the highest spike rate coincided with the seizure onset area only in half of the patients.

CONCLUSION

The spatial distribution of spike rates is strongly associated with the location of the seizure onset area, suggesting the presence of a distributed spike generation network, which is related to the seizure onset area.

SIGNIFICANCE

The spatial distribution of spike rates, but not the area with the highest spike rate, may hold value for the localization of the seizure onset area.

Does interictal synchronization influence ictogenesis?

The EEG recorded from epileptic patients presents with interictal discharges that are not associated with detectable clinical symptoms but are valuable for diagnostic purposes. Experimental studies have shown that interictal discharges and ictal events (i.e., seizures) are characterized intracellularly by similar (but for duration) neuronal depolarizations leading to sustained action potential firing, thus indicating that they may share similar cellular and pharmacological mechanisms. It has also been proposed that interictal discharges may herald the onset of electrographic seizures, but other studies have demonstrated that interictal events interfere with the occurrence of ictal activity. The relationship between interictal and ictal activity thus remains ambiguous. Here we will review this issue in animal models of limbic seizures that are electrographically close to those seen in TLE patients. In particular we will: (i) focus on the electrophysiological and pharmacological characteristics of, at least, two types of interictal discharge; (ii) propose that they play opposite roles in leading to ictogenesis; and (iii) discuss the possibility that mimicking one of these two types of interictal activity by low frequency repetitive stimulation can control ictogenesis. Finally, we will also review evidence indicating that specific types of interictal discharge may play a role in epileptogenesis. This article is part of the Special Issue entitled 'New Targets and Approaches to the Treatment of Epilepsy'.

Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG

OBJECTIVE

Identification of consistent distinguishing features between preictal and interictal periods in the EEG is an essential step towards performing seizure prediction. We propose a novel method to separate preictal and interictal states based on the analysis of the high frequency activity of intracerebral EEGs in patients with mesial temporal lobe epilepsy.

METHODS

Wavelet energy and entropy were computed in sliding window fashion from preictal and interictal epochs. A comparison of their organization in a 2 dimensional space was carried out using three features quantifying the similarities between their underlying states and a reference state. A discriminant analysis was then used in the features space to classify epochs. Performance was assessed based on sensitivity and false positive rates and validation was performed using a bootstrapping approach.

RESULTS

Preictal and interictal epochs were discriminable in most patients on a subset of channels that were found to be close or within the seizure onset zone.

CONCLUSIONS

Preictal and interictal states were separable using measures of similarity with the reference state. Discriminability varies with frequency bands.

SIGNIFICANCE

This method is useful to discriminate preictal from interictal states in intracerebral EEGs and could be useful for seizure prediction.

Electrical, molecular and behavioral effects of interictal spiking in the rat

OBJECTIVE

Epilepsy is a disease characterized by chronic seizures, but is associated with significant comorbidities between seizures including cognitive impairments, hyperactivity, and depression. To study this interictal state, we characterized the electrical, molecular, and behavior effects of chronic, neocortical interictal spiking in rats.

METHODS

A single injection of tetanus toxin into somatosensory cortex generated chronic interictal spiking measured by long-term video EEG monitoring and was correlated with motor activity. The cortical pattern of biomarker activation and the effects of blocking MAPK signaling on interictal spiking and behavior were determined.

RESULTS

Interictal spiking in this model increases in frequency, size, and becomes repetitive over time, but is rarely associated with seizures. Interictal spiking was sufficient to produce the same molecular and cellular pattern of layer 2/3-specific CREB activation and plasticity gene induction as is seen in the human interictal state. Increasing spike frequency was associated with hyperactivity, demonstrated by increased ambulatory activity and preferential circling toward the spiking hemisphere. Loud noises induced epileptic discharges, identical to spontaneous discharges. Treatment with a selective MAPK inhibitor prevented layer 2/3 CREB activation, reduced the frequency of epileptic discharges, and normalized behavioral abnormalities, but had no effect on seizures induced by electrical kindling.

INTERPRETATION

These results provide insights into the development of interictal epileptic spiking, their relationship to behavior, and suggest that interictal and ictal activities utilize distinct molecular pathways. This model, that parallels recent observations in humans, will be useful to develop therapeutics against interictal spiking and its behavioral comorbidities.

Seizure prediction and its applications

Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity that may remain localized and/or spread and severely disrupt the brain's normal multitask and multiprocessing function. Epileptic seizures are the hallmarks of such activity. The ability to issue warnings in real time of impending seizures may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a warning to the patient to avert seizure-associated injuries, to automatic timely administration of an appropriate stimulus. Seizure prediction could become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems.

Preictal changes in cerebral haemodynamics: review of findings and insights from intracerebral EEG

The possibility of recording changes in brain signals occurring before epileptic seizures is of considerable interest, both as markers for seizure anticipation and as a window into the mechanisms of seizure generation. Several studies have reported preictal changes on electrophysiological traces. More recently, observations have been made of changes occurring on haemodynamic signals before interictal events or before seizures, often without concurrent changes observed on electrophysiology. We present here a critical review of these findings, in optical imaging, SPECT and fMRI, followed by a discussion based on data from intracerebral EEG.

The value of intraoperative electrocorticography in surgical decision making for temporal lobe epilepsy with normal MRI

PURPOSE

We hypothesized that acute intraoperative electrocorticography (ECoG) might identify a subset of patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE) who could proceed directly to standard anteromesial resection (SAMR), obviating the need for chronic electrode implantation to guide resection.

METHODS

Patients with TLE and a normal MRI who underwent acute ECoG prior to chronic electrode recording of ictal onsets were evaluated. Intraoperative interictal spikes were classified as mesial (M), lateral (L), or mesial/lateral (ML). Results of the acute ECoG were correlated with the ictal-onset zone following chronic ECoG. Onsets were also classified as "M,""L," or "ML." Positron emission tomography (PET), scalp-EEG (electroencephalography), and Wada were evaluated as adjuncts.

KEY FINDINGS

Sixteen patients fit criteria for inclusion. Outcomes were Engel class I in nine patients, Engel II in two, Engel III in four, and Engel IV in one. Mean postoperative follow-up was 45.2 months. Scalp EEG and PET correlated with ictal onsets in 69% and 64% of patients, respectively. Wada correlated with onsets in 47% of patients. Acute intraoperative ECoG correlated with seizure onsets on chronic ECoG in all 16 patients. All eight patients with "M" pattern ECoG underwent SAMR, and six (75%) experienced Engel class I outcomes. Three of eight patients with "L" or "ML" onsets (38%) had Engel class I outcomes.

SIGNIFICANCE

Intraoperative ECoG may be useful in identifying a subset of patients with MRI-negative TLE who will benefit from SAMR without chronic implantation of electrodes. These patients have uniquely mesial interictal spikes and can go on to have improved postoperative seizure-free outcomes.

Seizure prediction

There is mounting evidence that seizures are preceded by characteristic changes in the EEG that are detectable minutes before seizure onset. Using novel signal analysis techniques, researchers are beginning to characterize the transition from the interictal to the ictal state in quantitative terms. This research has led to the development of automated seizure prediction algorithms. Active debate persists regarding the interpretation of research results, methods of signal analysis, as well as experimental and statistical methods for testing seizure prediction algorithms. Developments in this field have led to new theories on the mechanism of seizure development and resolution. The ability to predict seizures could lead the way to novel diagnostic and therapeutic methods for the treatment of patients with epilepsy.

Do interictal spikes sustain seizures and epileptogenesis?

Interictal spiking is seen in the EEG of epileptic patients between seizures. To date, the roles played by interictal events in seizure occurrence and in epileptogenesis remain elusive. While interictal spikes may herald the onset of electrographic seizures, experimental data indicate that hippocampus-driven interictal events prevent seizure precipitation. Even less clear than the role of interictal events in seizure occurrence is whether and how interictal spikes contribute to epileptogenesis. Thus, while plastic changes within limbic neuronal networks may result from ongoing interictal activity, experimental evidence supports the view that epileptogenesis is accompanied by a decrease in hippocampus-driven interictal activity.