Termination patterns of complex partial seizures: An intracranial EEG study

Tracking Multisite Seizure Propagation Using Ictal High-Gamma Activity

PURPOSE

Spatial patterns of long-range seizure propagation in epileptic networks have not been well characterized. Here, we use ictal high-gamma activity (HGA) as a proxy of intense neuronal population firing to map the spatial evolution of seizure recruitment.

METHODS

Ictal HGA (80-150 Hz) was analyzed in 13 patients with 72 seizures recorded by stereotactic depth electrodes, using previously validated methods. Distinct spatial clusters of channels with the ictal high-gamma signature were identified, and seizure hubs were defined as stereotypically recruited nonoverlapping clusters. Clusters correlated with asynchronous seizure terminations to provide supportive evidence for independent seizure activity at these sites. The spatial overlap between seizure hubs and interictal ripples was compared.

RESULTS

Ictal HGA was detected in 71% of seizures and 10% of implanted contacts, enabling tracking of contiguous and noncontiguous seizure recruitment. Multiple seizure hubs were identified in 54% of cases, including 43% of patients thought preoperatively to have unifocal epilepsy. Noncontiguous recruitment was associated with asynchronous seizure termination (odds ratio = 19.7; p = 0.029). Interictal ripples demonstrated greater spatial overlap with ictal HGA in cases with single seizure hubs compared with those with multiple hubs (100% vs. 66% per patient; p = 0.03).

CONCLUSIONS

Ictal HGA may serve as a useful adjunctive biomarker to distinguish contiguous seizure spread from propagation to remote seizure sites. High-gamma sites were found to cluster in stereotyped seizure hubs rather than being broadly distributed. Multiple hubs were common even in cases that were considered unifocal.

Classification of Seizure Termination Patterns using Deep Learning on intracranial EEG

Seizure termination has received significantly less attention than initiation and propagation and consequently, remains a poorly understood phase of seizure evolution. Yet, its study may have a significant impact on the development of efficient interventional approaches, i.e., it may be critical for the design of treatments that induce or reproduce termination mechanisms that are triggered in self-terminating seizures. In this work, we aim to study temporal and spectral features of intracranial EEG (iEEG) during epileptic seizures to find time-frequency signatures that can predict the termination patterns. We propose a deep learning model for classification of multi channel iEEG epileptic seizure termination pattern into burst suppression and continuous bursting. We decompose the raw time series seizure data into time-frequency maps using Morlet Wavelet Transform. A Convolution Neural Network (CNN) is then trained on cross-patient time-frequency maps to classify the seizure termination patterns. For evaluation of classification performance, we compared the proposed method with k-Nearest Neighbour (k-NN). The CNN is shown to achieve an accuracy of 90 % and precision of 92 % as compared to 70% and 72% accuracy and precision achieved with the k-NN respectively. The proposed model is thus able to capture the temporal and spatial patterns which results in high performance of the classifier. This method of classification can be used to predict how a particular seizure will end and can potentially inform seizure management and treatment. Clinical relevance- This method establishes a model that can be used to classify seizure termination patterns with an accuracy of 90 % which can assist in better treatment of epilepsy patients.

Quantifying seizure termination patterns reveals limited pathways to seizure end

OBJECTIVE

Despite their possible importance in the design of novel neuromodulatory approaches and in understanding status epilepticus, the dynamics and mechanisms of seizure termination are not well studied. We examined intracranial recordings from patients with epilepsy to differentiate seizure termination patterns and investigated whether these patterns are indicative of different underlying mechanisms.

METHODS

Seizures were classified into one of two termination patterns: (a) those that end simultaneously across the brain (synchronous), and (b) those whose termination is piecemeal across the cortex (asynchronous). Both types ended with either a burst suppression pattern, or continuous seizure activity. These patterns were quantified and compared using burst suppression ratio, absolute energy, and network connectivity.

RESULTS

Seizures with electrographic generalization showed burst suppression patterns in 90% of cases, compared with only 60% of seizures which remained focal. Interestingly, we found similar absolute energy and burst suppression ratios in seizures with synchronous and asynchronous termination, while seizures with continuous seizure activity were found to be different from seizures with burst suppression, showing lower energy during seizure and lower burst suppression ratio at the start and end of seizure. Finally, network density was observed to increase with seizure progression, with significantly lower densities in seizures with continuous seizure activity compared to seizures with burst suppression.

SIGNIFICANCE

Based on this spatiotemporal classification scheme, we suggest that there are a limited number of seizure termination patterns and dynamics. If this bears out, it would imply that the number of mechanisms underlying seizure termination is also constrained. Seizures with different termination patterns exhibit different dynamics even before their start. This may provide useful clues about how seizures may be managed, which in turn may lead to more targeted modes of therapy for seizure control.

Multiple mechanisms shape the relationship between pathway and duration of focal seizures

A seizure's electrographic dynamics are characterized by its spatiotemporal evolution, also termed dynamical 'pathway', and the time it takes to complete that pathway, which results in the seizure's duration. Both seizure pathways and durations have been shown to vary within the same patient. However, it is unclear whether seizures following the same pathway will have the same duration or if these features can vary independently. We compared within-subject variability in these seizure features using (i) epilepsy monitoring unit intracranial EEG (iEEG) recordings of 31 patients (mean: 6.7 days, 16.5 seizures/subject), (ii) NeuroVista chronic iEEG recordings of 10 patients (mean: 521.2 days, 252.6 seizures/subject) and (iii) chronic iEEG recordings of three dogs with focal-onset seizures (mean: 324.4 days, 62.3 seizures/subject). While the strength of the relationship between seizure pathways and durations was highly subject-specific, in most subjects, changes in seizure pathways were only weakly to moderately associated with differences in seizure durations. The relationship between seizure pathways and durations was strengthened by seizures that were 'truncated' versions, both in pathway and duration, of other seizures. However, the relationship was weakened by seizures that had a common pathway, but different durations ('elasticity'), or had similar durations, but followed different pathways ('semblance'). Even in subjects with distinct populations of short and long seizures, seizure durations were not a reliable indicator of different seizure pathways. These findings suggest that seizure pathways and durations are modulated by multiple different mechanisms. Uncovering such mechanisms may reveal novel therapeutic targets for reducing seizure duration and severity.

Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study

Objective: Our aim was to investigate the dynamics of functional connectivity (FC) networks during seizure termination in patients with childhood absence epilepsy (CAE) using magnetoencephalography (MEG) and graph theory (GT) analysis.

Methods: MEG data were recorded from 22 drug-naïve patients diagnosed with CAE. FC analysis was performed to evaluate the FC networks in seven frequency bands of the MEG data. GT analysis was used to assess the topological properties of FC networks in different frequency bands.

Results: The patterns of FC networks involving the frontal cortex were altered significantly during seizure termination compared with those during the ictal period. Changes in the topological parameters of FC networks were observed in specific frequency bands during seizure termination compared with those in the ictal period. In addition, the connectivity strength at 250–500 Hz during the ictal period was negatively correlated with seizure frequency.

Conclusions: FC networks associated with the frontal cortex were involved in the termination of absence seizures. The topological properties of FC networks in different frequency bands could be used as new biomarkers to characterize the dynamics of FC networks related to seizure termination.

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.

Postictal clinical and electroencephalographic activity following intracranially recorded bilateral tonic-clonic seizures

OBJECTIVE

The dynamics of the postictal period, which may demonstrate such dramatic clinical phenomena as focal neurological deficits, prolonged coma and immobility, and even sudden death, are poorly understood. We sought to classify and characterize postictal phases of bilateral tonic-clonic seizures based on electroencephalographic (EEG) criteria and associated clinical features.

METHODS

We performed a detailed electroclinical evaluation of the postictal period in a series of 31 bilateral tonic-clonic seizures in 16 patients undergoing epilepsy surgery evaluations for focal pharmacoresistant epilepsy with intracranial electrodes and time-locked video.

RESULTS

The postictal EEG demonstrated three clearly differentiated phases as follows: attenuation, a burst-attenuation pattern, and a return to continuous background, with abrupt, synchronized transitions between phases. Postictal attenuation was common, occurring in 84% of seizures in 94% of patients in this study. There was increased power in gamma frequencies (>25 Hz) during postictal attenuation periods relative to preictal baseline in 88% of seizures demonstrating the attenuation pattern (n = 25 seizures, P < 0.002). Such increases were seen in >90% of channels in 13 seizures (52%) and <10% of channels in three seizures (12%). Postictal immobility was seen in 87% of seizures, with either a flaccid (58%) or rigid/dystonic (29%) appearance. Clinical motor manifestations, including focal dystonic posturing, automatisms, head and eye deviation, and myoclonic jerking, continued or emerged within the first minute following seizure termination in 48% of seizures, regardless of EEG appearance.

SIGNIFICANCE

Intracranial postictal attenuation, which may be diffuse or focal, is so common that it should be regarded as a ubiquitous feature of bilateral tonic-clonic seizures, rather than an unusual event. The prominence of high-frequency activity coupled with emerging clinical features, including rigid immobility and semiologies such as automatisms, during the postictal period supports the presence of ongoing seizure-related neuronal activity in unrecorded brain regions.

Phase Synchronization Dynamics of Neural Network during Seizures

Epilepsy has been considered as a network-level disorder characterized by recurrent seizures, which result from network reorganization with evolution of synchronization. In this study, the brain networks were established by calculating phase synchronization based on electrocorticogram (ECoG) signals from eleven refractory epilepsy patients. Results showed that there was a significant increase of synchronization prior to seizure termination and no significant difference of the transitions of network states among the preseizure, seizure, and postseizure periods. Those results indicated that synchronization might participate in termination of seizures, and the network states transitions might not dominate the seizure evolution.

SK channels participate in the formation of after burst hyperpolarization and partly inhibit the burst strength of epileptic ictal discharges

Epilepsy is a common disease of the central nervous system. Tetanic spasms and convulsions are the key symptoms exhibited during epileptic seizures. However, the majority of patients have a significant post-seizure silence following a serious seizure; the underlying molecular neural mechanisms in this burst interval are unclear. The aim of the present study was to reveal the effect and role of calcium-activated potassium channels during this seizure interval silence period. Cyclothiazide (CTZ) was used to establish the seizure model in rat hippocampal cultured neurons, then the after-burst hyperpolarization (ABH) activities were recorded using the patch clamp technique. By comparing the amplitude and duration of hyperpolarizations, the present study analyzed the association between epileptiform bursts and ABHs when treated with different concentrations of CTZ. In addition, apamin and iberiotoxin were used for pharmacological tests. An intracranial electroencephalogram (EEG) recording was also performed when the CTZ experiments were repeated on animals. The experimental results revealed that treatment with high levels of CTZ induced larger ABHs and was associated with stronger burst activities, which suggested a positive correlation between ABH and epileptiform burst. Apamin, an antagonist of small conductance calcium-activated potassium (SK) channels, decreased the amplitude of ABH; however, reduced ABH was associated with enhanced burst activity, in burst probability and burst strength. These results revealed an important role of SK channels in the formation of ABH and in the inhibition of burst activity. Iberiotoxin, an antagonist of big conductance calcium-activated potassium (BK) channels, had no significant effect on ABH and burst activity. In addition, a positive correlation was identified between burst duration and ABH parameters. An intracellular calcium chelator impaired the amplitude of ABH; however, it did not affect the burst parameters. The rat cortical EEG recordings also exhibited a similar positive correlation between the duration of epileptic burst and after burst depression. Collectively, the results indicate that ABH may serve in the physiological feedback system to reduce the strength of epileptic hyperexcitation, a process in which SK channels are important.

Toward a Mechanistic Understanding of Epileptic Networks

Focal epileptic seizures have long been considered to arise from a small susceptible brain area and spread through uninvolved regions. In the past decade, the idea that focal seizures instead arise from coordinated activity across large-scale epileptic networks has become widely accepted. Understanding the network model's applicability is critical, due to its increasing influence on clinical research and surgical treatment paradigms. In this review, we examine the origins of the concept of epileptic networks as the nidus for recurring seizures. We summarize analytical and methodological elements of epileptic network studies and discuss findings from recent detailed electrophysiological investigations. Our review highlights the strengths and limitations of the epileptic network theory as a metaphor for the complex interactions that occur during seizures. We present lines of investigation that may usefully probe these interactions and thus serve to advance our understanding of the long-range effects of epileptiform activity.

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

OBJECTIVE

To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone.

METHODS

We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal. Statistically significant differences between the distributions of the nodes' number of input and output connections are used to detect time-irreversible iEEG signals.

RESULTS

In 31 of 32 seizure recordings we found time-irreversible iEEG signals. The maximally time-irreversible signals always occurred during seizures, with highest probability in the middle of the first seizure half. These signals spanned a large range of frequencies and amplitudes but were all characterized by saw-tooth like shaped components. Brain regions removed from patients who became post-surgically seizure-free generated significantly larger time-irreversibilities than regions removed from patients who still had seizures after surgery.

CONCLUSIONS

Our results corroborate that ictal time-irreversible iEEG signals can indeed serve as markers of the epileptogenic zone and can be efficiently detected and quantified in a time-resolved manner by dHVG based methods.

SIGNIFICANCE

Ictal time-irreversible EEG signals can help to improve pre-surgical evaluation in patients suffering from pharmaco-resistant epilepsies.