The postictal EEG

Highly local activation of inhibition at the seizure wavefront in vivo

The propagation of a seizure wavefront in the cortex divides an intensely firing seizure core from a low-firing seizure penumbra. Seizure propagation is currently thought to generate strong activation of inhibition in the seizure penumbra that leads to its decreased neuronal firing. However, the direct measurement of neuronal excitability during seizures has been difficult to perform in vivo. We used simultaneous optogenetics and calcium imaging (all-optical interrogation) to characterize real-time neuronal excitability in an acute mouse model of seizure propagation. We find that single-neuron excitability is decreased in close proximity to the seizure wavefront but becomes increased distal to the seizure wavefront. This suggests that inhibitory neurons of the seizure wavefront create a proximal circumference of hypoexcitability but do not influence neuronal excitability in the penumbra.

Postictal psychiatric symptoms: A neurophysiological study

OBJECTIVE

Postictal psychiatric symptoms (PPS) are a relatively common but understudied phenomenon in epilepsy. The mechanisms by which seizures contribute to worsening in psychiatric symptoms are unclear. We aimed to identify PPS prospectively during and after admission to the epilepsy monitoring unit (EMU) in order to characterize the postictal physiologic changes leading to PPS.

METHODS

We prospectively enrolled patients admitted to the EMU and administered repeat psychometric questionnaires during and after their hospital stay in order to assess for postictal exacerbations in four symptom complexes: anger/hostility, anxiety, depression, and paranoia. Electroclinical and electrographic seizures were identified from the EEG recordings, and seizure durations were measured. The severity of postictal slowing was calculated as the proportion of postictal theta/delta activity in the postictal EEG relative to the preictal EEG using the Hilbert transform.

RESULTS

Among 33 participants, 8 demonstrated significant increases in at least one of the four symptoms (the PPS+ group) within three days following the first seizure. The most common PPS was anger/hostility, experienced by 7/8 participants with PPS. Among the 8 PPS+ participants, four experienced more than one PPS. As compared to those without PPS (the PPS- group), the PPS+ group demonstrated a greater degree of postictal EEG slowing at 10 min (p = 0.022) and 20 min (p = 0.05) following seizure termination. They also experienced significantly more seizures during the study period (p = 0.005). There was no difference in seizure duration between groups.

SIGNIFICANCE

Postictal psychiatric symptoms including anger/hostility, anxiety, depression, and paranoia may be more common than recognized. In particular, postictal increases in anger and irritability may be particularly common. We provide physiological evidence of a biological mechanism as well as a demonstration of the use of quantitative electroencephalography toward a better understanding of postictal neurophysiology.

Restoration of postictal cortical activity after electroconvulsive therapy relates to recovery of orientation in person, place, and time

BACKGROUND

Most patients show temporary impairments in clinical orientation after electroconvulsive therapy (ECT)-induced seizures. It is unclear how postictal reorientation relates to electroencephalography (EEG) restoration. This relationship may provide additional measures to quantify postictal recovery and shed light on neurophysiological aspects of reorientation after ECT.

METHODS

We analyzed prospectively collected clinical and continuous ictal and postictal EEG data from ECT patients. Postictal EEG restoration up to 1 h was estimated by the evolution of the normalized alpha-delta ratio (ADR). Times to reorientation in the cognitive domains of person, place, and time were assessed postictally. In each cognitive domain, a linear mixed model was fitted to investigate the relationships between time to reorientation and postictal EEG restoration.

RESULTS

In total, 272 pairs of ictal-postictal EEG and reorientation times of 32 patients were included. In all domains, longer time to reorientation was associated with slower postictal EEG recovery. Longer seizure duration and postictal administration of midazolam were related to longer time to reorientation in all domains. At 1-hour post-seizure, most patients were clinically reoriented, while their EEG had only partly restored.

CONCLUSIONS

We show a relationship between postictal EEG restoration and clinical reorientation after ECT-induced seizures. EEG was more sensitive than reorientation time in all domains to detect postictal recovery beyond 1-hour post-seizure. Our findings indicate that clinical reorientation probably depends on gradual cortical synaptic recovery, with longer seizure duration leading to longer postsynaptic suppression after ECT seizures.

Astrocytes as critical players of the fine balance between inhibition and excitation in the brain: spreading depolarization as a mechanism to curb epileptic activity

Spreading depolarizations (SD) are slow waves of complete depolarization of brain tissue followed by neuronal silencing that may play a role in seizure termination. Even though SD was first discovered in the context of epilepsy research, the link between SD and epileptic activity remains understudied. Both seizures and SD share fundamental pathophysiological features, and recent evidence highlights the frequent occurrence of SD in experimental seizure models. Human data on co-occurring seizures and SD are limited but suggestive. This mini-review addresses possible roles of SD during epileptiform activity, shedding light on SD as a potential mechanism for terminating epileptiform activity. A common denominator for many forms of epilepsy is reactive astrogliosis, a process characterized by morphological and functional changes to astrocytes. Data suggest that SD mechanisms are potentially perturbed in reactive astrogliosis and we propose that this may affect seizure pathophysiology.

Seizure-induced neutrophil adhesion in brain capillaries leads to a decrease in postictal cerebral blood flow

Cerebral hypoperfusion has been proposed as a potential cause of postictal neurological dysfunction in epilepsy, but its underlying mechanism is still unclear. We show that a 30% reduction in postictal cerebral blood flow (CBF) has two contributing factors: the early hypoperfusion up to ∼30 min post-seizure was mainly induced by arteriolar constriction, while the hypoperfusion that persisted for over an hour was due to increased capillary stalling induced by neutrophil adhesion to brain capillaries, decreased red blood cell (RBC) flow accompanied by constriction of capillaries and venules, and elevated intercellular adhesion molecule-1 (ICAM-1) expression. Administration of antibodies against the neutrophil marker Ly6G and against LFA-1, which mediates adhesive interactions with ICAM-1, prevented neutrophil adhesion and recovered the prolonged CBF reductions to control levels. Our findings provide evidence that seizure-induced neutrophil adhesion to cerebral microvessels via ICAM-1 leads to prolonged postictal hypoperfusion, which may underlie neurological dysfunction in epilepsy.

Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy

OBJECTIVE

Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ).

METHODS

We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates.

RESULTS

We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve = .83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep.

SIGNIFICANCE

The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.

Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy

Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until 2 h before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9 ± 21.0 min) and starting time before seizure onset (47.6 ± 27.3 min). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-specific preictal signatures for some patients and some seizures within the same patient.

Tardive Seizures After Electroconvulsive Therapy

OBJECTIVES

Seizures that occur spontaneously after termination of an electroconvulsive therapy (ECT) seizure are termed tardive seizures. They are thought to be a rare complication of ECT, influenced by risk factors that affect seizure threshold. However, there has been limited review of tardive seizures with modified ECT. We aimed to review the literature to provide clinical guidance for the use of ECT after tardive seizures.

METHODS

PubMed, EMBASE, PsycInfo, and CINAHL databases were searched from inception to May 2021 to identify cases of modified ECT, with evidence of a seizure occurring within 7 days of a terminated ECT seizure. Data for demographic, medical, pharmacological, anesthetic, and ECT variables as well as management strategies were collected.

RESULTS

There have been 39 episodes of modified ECT-related tardive seizures published over a period of 40 years. In 97.4% of cases, there was at least 1 identified potential risk factor for seizures, including use of a seizure-lowering medication and/or preexisting neurological injury. Major complications were uncommon (<15% of cases); however, 1 fetal death and 1 subsequent suicide were reported. No case was diagnosed with epilepsy, although around 20% continued on antiepileptic medications. More than half of the included patients were retrialed on ECT, with only 15% developing further tardive seizures.

CONCLUSIONS

Seizures that occurred spontaneously after the termination of an ECT seizure are a rare complication of modified ECT. Recommencing ECT after a tardive seizure may occur after review of modifiable seizure risk factors and with consideration of antiepileptic medication and extended post-ECT monitoring.

Robust chronic convulsive seizures, high frequency oscillations, and human seizure onset patterns in an intrahippocampal kainic acid model in mice

Intrahippocampal kainic acid (IHKA) has been widely implemented to simulate temporal lobe epilepsy (TLE), but evidence of robust seizures is usually limited. To resolve this problem, we slightly modified previous methods and show robust seizures are common and frequent in both male and female mice. We employed continuous wideband video-EEG monitoring from 4 recording sites to best demonstrate the seizures. We found many more convulsive seizures than most studies have reported. Mortality was low. Analysis of convulsive seizures at 2-4 and 10-12 wks post-IHKA showed a robust frequency (2-4 per day on average) and duration (typically 20-30 s) at each time. Comparison of the two timepoints showed that seizure burden became more severe in approximately 50% of the animals. We show that almost all convulsive seizures could be characterized as either low-voltage fast or hypersynchronous onset seizures, which has not been reported in a mouse model of epilepsy and is important because these seizure types are found in humans. In addition, we report that high frequency oscillations (>250 Hz) occur, resembling findings from IHKA in rats and TLE patients. Pathology in the hippocampus at the site of IHKA injection was similar to mesial temporal lobe sclerosis and reduced contralaterally. In summary, our methods produce a model of TLE in mice with robust convulsive seizures, and there is variable progression. HFOs are robust also, and seizures have onset patterns and pathology like human TLE. SIGNIFICANCE: Although the IHKA model has been widely used in mice for epilepsy research, there is variation in outcomes, with many studies showing few robust seizures long-term, especially convulsive seizures. We present an implementation of the IHKA model with frequent convulsive seizures that are robust, meaning they are >10 s and associated with complex high frequency rhythmic activity recorded from 2 hippocampal and 2 cortical sites. Seizure onset patterns usually matched the low-voltage fast and hypersynchronous seizures in TLE. Importantly, there is low mortality, and both sexes can be used. We believe our results will advance the ability to use the IHKA model of TLE in mice. The results also have important implications for our understanding of HFOs, progression, and other topics of broad interest to the epilepsy research community. Finally, the results have implications for preclinical drug screening because seizure frequency increased in approximately half of the mice after a 6 wk interval, suggesting that the typical 2 wk period for monitoring seizure frequency is insufficient.

Headache in people with epilepsy

Epidemiological estimates indicate that individuals with epilepsy are more likely to experience headaches, including migraine, than individuals without epilepsy. Headaches can be temporally unrelated to seizures, or can occur before, during or after an episode; seizures and migraine attacks are mostly not temporally linked. The pathophysiological links between headaches (including migraine) and epilepsy are complex and have not yet been fully elucidated. Correct diagnoses and appropriate treatment of headaches in individuals with epilepsy is essential, as headaches can contribute substantially to disease burden. Here, we review the insights that have been made into the associations between headache and epilepsy over the past 5 years, including information on the pathophysiological mechanisms and genetic variants that link the two disorders. We also discuss the current best practice for the management of headaches co-occurring with epilepsy and highlight future challenges for this area of research.

Longitudinal impact of cannabidiol on EEG measures in subjects with treatment-resistant epilepsy

OBJECTIVE

To assess the longitudinal impact of highly purified cannabidiol (CBD) on the electroencephalogram (EEG) of children and adults.

METHODS

Participants received an EEG prior to starting CBD, after approximately 12 weeks of CBD (FU1) and after approximately one year of CBD therapy (FU2). Longitudinal changes in five EEG measures (background frequency, focal slowing, reactivity, frequency of interictal, and ictal discharges) were examined following CBD exposure. Data were compared between pediatric and adult groups at two follow-up time points and within groups over time. Population-averaged models with generalized estimation equations or linear mixed effects models were used to analyze data where appropriate. Correlation analysis was used to assess any association between changes in seizure frequency and changes in EEG interictal discharge (IED) frequency. An alpha level of 5% was used to assess statistical significance.

RESULTS

At FU1, the adult group showed significant decrease in IED/minute (IDR 0.07, 95% CI [0.04, 0.14], P < 0.001); a nonsignificant decrease was observed among children (IDR 0.87, 95% CI [0.47, 0.64], P = 0.67). The difference in changes over time between participant groups was significant after adjusting for last CBD dose (IDR 11.8, 95% CI [4.86, 28.65], P < 0.0001). At FU2 both groups showed significant reduction from baseline after controlling for last CBD dose. This decrease was more pronounced in children (IDR 15.38, 95% CI [4.93, 47.99], P < 0.001). There was no significant correlation between changes in seizure frequency and EEG IED frequency at each timepoint (P = 0.542, 0.917 and 0.989 from baseline to FU1, FU1 to FU2 and baseline to FU2, respectively).

SIGNIFICANCE

This longitudinal EEG study shows that highly-purified plant-derived CBD has positive effects on interictal epileptiform discharge frequency but no effects on other EEG measures. The effect of CBD does not appear to be dose or treatment-duration dependent.

[A case of epileptic seizure that required differentiation from hyper-acute ischemic stroke: usefulness of comparing DWI and FLAIR]

A 60-year-old man developed aphasia and transient right upper limb paresis in the presence of chronic subdural hematoma and was transferred to our hospital at an early stage. Cranial MRI within an hour after onset showed diffusion-weighted image (DWI) hyperintensity in the left parietal, temporal, and insular cortex and the pulvinar, and decreased apparent diffusion coefficient (ADC) in the left parietal cortex and pulvinar, suggesting a differential diagnosis of hyper-acute ischemic stroke. However, the distribution and timing of the MRI abnormalities were considered to be atypical for hyper-acute ischemic stroke. The area with both DWI hyperintensity and decreased ADC included the cerebral cortex adjacent to the hematoma and the ipsilateral pulvinar, and fluid-attenuated inversion recovery (FLAIR) hyperintensity co-existed with DWI hyperintensity within only an hour from onset. Furthermore, FLAIR images showed infiltration of the hematoma content into the subarachnoid space, which might have triggered the attack. These findings collectively led us to diagnose an epileptic seizure. The present case suggests that the distribution and timing of MRI abnormalities are essential to differentiate epileptic seizures from hyper-acute ischemic stroke.

Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

Previous research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

Post-ictal Generalized EEG Suppression is reduced by Enhancing Dorsal Raphe Serotonergic Neurotransmission

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. A proposed risk marker for SUDEP is the duration of post-ictal generalized EEG suppression (PGES). The mechanisms underlying PGES are unknown. Serotonin (5-HT) has been implicated in SUDEP pathophysiology. Seizures suppress activity of 5-HT neurons in the dorsal raphe nucleus (DRN). We hypothesized that suppression of DRN 5-HT neuron activity contributes to PGES and increasing 5-HT neurotransmission or stimulating the DRN before a seizure would decrease PGES duration. Adult C57BL/6J and Pet1-Cre mice received EEG/EMG electrodes, a bipolar stimulating/recording electrode in the right basolateral amygdala, and either a microdialysis guide cannula or an injection of adeno-associated virus (AAV) allowing expression of channelrhodopsin2 plus an optic fiber into the DRN. Systemic application of the selective 5-HT reuptake inhibitor citalopram (20 mg/kg) decreased PGES duration from seizures induced during wake (n = 23) and non-rapid eye movement (NREM) sleep (n = 13) whereas fluoxetine (10 mg/kg) pretreatment decreased PGES duration following seizures induced from wake (n = 11), but not NREM sleep (n = 9). Focal chemical (n = 6) or optogenetic (n = 8) stimulation of the DRN reduced PGES duration following seizures in kindled mice induced during wake. During PGES, animals exhibited immobility and suppression of EEG activity that was reduced by citalopram pretreatment. These results suggest 5-HT and the DRN may regulate PGES.

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

OBJECTIVE

Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head.

METHODS

Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way.

RESULTS

Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future.

SIGNIFICANCE

Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization.

In vivo assessment of mechanisms underlying the neurovascular basis of postictal amnesia

Long-lasting confusion and memory difficulties during the postictal state remain a major unmet problem in epilepsy that lacks pathophysiological explanation and treatment. We previously identified that long-lasting periods of severe postictal hypoperfusion/hypoxia, not seizures per se, are associated with memory impairment after temporal lobe seizures. While this observation suggests a key pathophysiological role for insufficient energy delivery, it is unclear how the networks that underlie episodic memory respond to vascular constraints that ultimately give rise to amnesia. Here, we focused on cellular/network level analyses in the CA1 of hippocampus in vivo to determine if neural activity, network oscillations, synaptic transmission, and/or synaptic plasticity are impaired following kindled seizures. Importantly, the induction of severe postictal hypoperfusion/hypoxia was prevented in animals treated by a COX-2 inhibitor, which experimentally separated seizures from their vascular consequences. We observed complete activation of CA1 pyramidal neurons during brief seizures, followed by a short period of reduced activity and flattening of the local field potential that resolved within minutes. During the postictal state, constituting tens of minutes to hours, we observed no changes in neural activity, network oscillations, and synaptic transmission. However, long-term potentiation of the temporoammonic pathway to CA1 was impaired in the postictal period, but only when severe local hypoxia occurred. Lastly, we tested the ability of rats to perform object-context discrimination, which has been proposed to require temporoammonic input to differentiate between sensory experience and the stored representation of the expected object-context pairing. Deficits in this task following seizures were reversed by COX-2 inhibition, which prevented severe postictal hypoxia. These results support a key role for hypoperfusion/hypoxia in postictal memory impairments and identify that many aspects of hippocampal network function are resilient during severe hypoxia except for long-term synaptic plasticity.

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter- and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter- and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram.

Spatial distribution of interictal spikes fluctuates over time and localizes seizure onset

The location of interictal spikes is used to aid surgical planning in patients with medically refractory epilepsy; however, their spatial and temporal dynamics are poorly understood. In this study, we analysed the spatial distribution of interictal spikes over time in 20 adult and paediatric patients (12 females, mean age = 34.5 years, range = 5-58) who underwent intracranial EEG evaluation for epilepsy surgery. Interictal spikes were detected in the 24 h surrounding each seizure and spikes were clustered based on spatial location. The temporal dynamics of spike spatial distribution were calculated for each patient and the effects of sleep and seizures on these dynamics were evaluated. Finally, spike location was assessed in relation to seizure onset location. We found that spike spatial distribution fluctuated significantly over time in 14/20 patients (with a significant aggregate effect across patients, Fisher's method: P < 0.001). A median of 12 sequential hours were required to capture 80% of the variability in spike spatial distribution. Sleep and postictal state affected the spike spatial distribution in 8/20 and 4/20 patients, respectively, with a significant aggregate effect (Fisher's method: P < 0.001 for each). There was no evidence of pre-ictal change in the spike spatial distribution for any patient or in aggregate (Fisher's method: P = 0.99). The electrode with the highest spike frequency and the electrode with the largest area of downstream spike propagation both localized the seizure onset zone better than predicted by chance (Wilcoxon signed-rank test: P = 0.005 and P = 0.002, respectively). In conclusion, spikes localize seizure onset. However, temporal fluctuations in spike spatial distribution, particularly in relation to sleep and post-ictal state, can confound localization. An adequate duration of intracranial recording-ideally at least 12 sequential hours-capturing both sleep and wakefulness should be obtained to sufficiently sample the interictal network.

The postictal state - What do we know?

This narrative review provides a broad and comprehensive overview of the most important discoveries on the postictal state over the past decades as well as recent developments. After a description and definition of the postictal state, we discuss postictal sypmtoms, their clinical manifestations, and related findings. Moreover, pathophysiological advances are reviewed, followed by current treatment options.

No evidence for critical slowing down prior to human epileptic seizures

There is an ongoing debate whether generic early warning signals for critical transitions exist that can be applied across diverse systems. The human epileptic brain is often considered as a prototypical system, given the devastating and, at times, even life-threatening nature of the extreme event epileptic seizure. More than three decades of international effort has successfully identified predictors of imminent seizures. However, the suitability of typically applied early warning indicators for critical slowing down, namely, variance and lag-1 autocorrelation, for indexing seizure susceptibility is still controversially discussed. Here, we investigated long-term, multichannel recordings of brain dynamics from 28 subjects with epilepsy. Using a surrogate-based evaluation procedure of sensitivity and specificity of time-resolved estimates of early warning indicators, we found no evidence for critical slowing down prior to 105 epileptic seizures.

Characterizing the role of the structural connectome in seizure dynamics

How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.

Traceability and dynamical resistance of precursor of extreme events

Extreme events occur in a variety of natural, technical, and societal systems and often have catastrophic consequences. Their low-probability, high-impact nature has recently triggered research into improving our understanding of generating mechanisms, providing early warnings as well as developing control strategies. For the latter to be effective, knowledge about dynamical resistance of a system prior to an extreme event is of utmost importance. Here we introduce a novel time-series-based and non-perturbative approach to efficiently monitor dynamical resistance and apply it to high-resolution observations of brain activities from 43 subjects with uncontrollable epileptic seizures. We gain surprising insights into pre-seizure dynamical resistance of brains that also provide important clues for success or failure of measures for seizure prevention. The novel resistance monitoring perspective advances our understanding of precursor dynamics in complex spatio-temporal systems with potential applications in refining control strategies.

Spatiotemporal evolution of focal epileptiform activity from surface and laminar field recordings in cat neocortex

New devices that use targeted electrical stimulation to treat refractory localization-related epilepsy have shown great promise, although it is not well known which targets most effectively prevent the initiation and spread of seizures. To better understand how the brain transitions from healthy to seizing on a local scale, we induced focal epileptiform activity in the visual cortex of five anesthetized cats with local application of the GABA_A blocker picrotoxin while simultaneously recording local field potentials on a high-resolution electrocorticography array and laminar depth probes. Epileptiform activity appeared in the form of isolated events, revealing a consistent temporal pattern of ictogenesis across animals with interictal events consistently preceding the appearance of seizures. Based on the number of spikes per event, there was a natural separation between seizures and shorter interictal events. Two distinct spatial regions were seen: an epileptic focus that grew in size as activity progressed, and an inhibitory surround that exhibited a distinct relationship with the focus both on the surface and in the depth of the cortex. Epileptiform activity in the cortical laminae was seen concomitant with activity on the surface. Focus spikes appeared earlier on electrodes deeper in the cortex, suggesting that deep cortical layers may be integral to recruiting healthy tissue into the epileptic network and could be a promising target for interventional devices. Our study may inform more effective therapies to prevent seizure generation and spread in localization-related epilepsies. NEW & NOTEWORTHY We induced local epileptiform activity and recorded continuous, high-resolution local field potentials from the surface and depth of the visual cortex in anesthetized cats. Our results reveal a consistent pattern of ictogenesis, characterize the spatial spread of the epileptic focus and its relationship with the inhibitory surround, and show that focus activity within events appears earliest in deeper cortical layers. These findings have potential implications for the monitoring and treatment of refractory epilepsy.

Cognitive and Neurophysiological Recovery Following Electroconvulsive Therapy: A Study Protocol

Electroconvulsive therapy (ECT) employs the elective induction of generalizes seizures as a potent treatment for severe psychiatric illness. As such, ECT provides an opportunity to rigorously study the recovery of consciousness, reconstitution of cognition, and electroencephalographic (EEG) activity following seizures. Fifteen patients with major depressive disorder refractory to pharmacologic therapy will be enrolled (Clinicaltrials.gov, NCT02761330). Adequate seizure duration will be confirmed following right unilateral ECT under etomidate anesthesia. Patients will then undergo randomization for the order in which they will receive three sequential treatments: etomidate + ECT, ketamine + ECT, and ketamine + sham ECT. Sessions will be repeated in the same sequence for a total of six treatments. Before each session, sensorimotor speed, working memory, and executive function will be assessed through a standardized cognitive test battery. After each treatment, the return of purposeful responsiveness to verbal command will be determined. At this point, serial cognitive assessments will begin using the same standardized test battery. The presence of delirium and changes in depression severity will also be ascertained. Sixty-four channel EEG will be acquired throughout baseline, ictal, and postictal epochs. Mixed-effects models will correlate the trajectories of cognitive recovery, clinical outcomes, and EEG metrics over time. This innovative research design will answer whether: (1) time to return of responsiveness will be prolonged with ketamine + ECT compared with ketamine + sham ECT; (2) time of restoration to baseline function in each cognitive domain will take longer after ketamine + ECT than after ketamine + sham ECT; (3) postictal delirium is associated with delayed restoration of baseline function in all cognitive domains; and (4) the sequence of reconstitution of cognitive domains following the three treatments in this study is similar to that occurring after an isoflurane general anesthetic (NCT01911195). Sub-studies will assess the relationships of cognitive recovery to the EEG preceding, concurrent, and following individual ECT sessions. Overall, this study will lead the development of biomarkers for tailoring the cogno-affective recovery of patients undergoing ECT.

Pathophysiological mechanisms of migraine and epilepsy: Similarities and differences

Migraine and epilepsy are episodic disorders with distinct features, but they have some clinical and pathophysiological overlaps. We review here clinical overlaps between seizures and migraine attacks, activities of neuronal networks observed during seizures and migraine attacks, and molecular and cellular mechanisms of migraine identified in genetic forms, focusing on genetic variants identified in hemiplegic migraine and their functional effects. Epilepsy and migraine can be generated by dysfunctions of the same neuronal networks, but these dysfunctions can be disease-specific, even if pathogenic mutations target the same protein. Studies of rare monogenic forms have allowed the identification of some molecular/cellular dysfunctions that provide a window on pathological mechanisms: we have begun to disclose the tip of the iceberg.

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.

Seizure Forecasting and the Preictal State in Canine Epilepsy

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

Predictability of uncontrollable multifocal seizures - towards new treatment options

Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

Postictal generalized EEG suppression and SUDEP: a review

SUMMARY

Sudden unexpected death in epilepsy (SUDEP) remains a leading cause of epilepsy-related death, and yet, its pathogenic mechanisms remain ill-defined. Although epidemiological studies of SUDEP in heterogenous populations have established a number of clinical associations, evaluation and stratification of individual risk remains difficult. Thus, potential markers as predictors of risk of SUDEP are important not only clinically but also for research on SUDEP prevention. Recordings from rare monitored cases of SUDEP demonstrate postictal generalized EEG suppression after terminal seizures, raising expectations that postictal generalized EEG suppression may identify individuals at higher risk. In this review, we consider the literature on postictal generalized EEG suppression and evaluate its relevance and utility as a possible marker of SUDEP.

Electroencephalography in the pediatric emergency department: when is it most useful?

This study aimed to identify the indications in which electroencephalography in the pediatric emergency department is most useful. We retrospectively reviewed the influence that the results of the emergent electroencephalogram had on the eventual disposition of patients at our pediatric emergency department. Sixty-eight children (mean age, 7.3 years; 32 males) underwent 70 emergent electroencephalograms. Fifty-seven emergent electroencephalograms were performed for the suspicion of ongoing seizures or status epilepticus. Thirteen of the 22 children (59.1%) discharged from the emergency department were sent home mainly based on the results of the emergent electroencephalogram, which prevented an admission. In particular, 11 of 38 children with frequent and recurrent paroxysmal events concerning for seizures and 2 of 19 children with suspected ongoing status epilepticus were discharged after excluding an epileptic disturbance. The emergent electroencephalogram provided meaningful clinical information that influenced disposition, especially in patients with ongoing events in which the clinical picture was clarified by a rapidly acquired electroencephalogram.

Seizures, Cerebral Shutdown, and SUDEP

Several potential pathophysiologic phenomena, including "cerebral shutdown," are postulated to be responsible for SUDEP. Since the evidence for a seizure-related mechanism is strong, a poor understanding of the physiology of human seizure termination is a major handicap. However, rather than a failure of a single homeostatic mechanism, such as postictal arousal, it may be a "perfect storm" created by the lining up of a several factors that lead to death.

A machine-learning algorithm for detecting seizure termination in scalp EEG

Efforts to develop algorithms that can robustly detect the cessation of seizure activity within scalp EEGs are now underway. Such algorithms can facilitate novel clinical applications such as the estimation of a seizure's duration; the delivery of therapies designed to mitigate postictal period symptoms; or detection of the presence of status epilepticus. In this article, we present and evaluate a novel, machine learning-based method for detecting the termination of electrographic seizure activity. When tested on 133 seizures from a public database, our method successfully detected the end of 132 seizures within 10.3 ± 5.5 seconds of the time determined by an electroencephalographer to represent the electrographic end of seizure. Furthermore, by pairing our seizure end detector with a previously published seizure onset detector, we could automatically estimate the duration of 85% of test electrographic seizures within a 15-second error margin compared with electroencephalographer determinations. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Identification of preseizure States in epilepsy: a data-driven approach for multichannel EEG recordings

The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure – so-called measure profiles – for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating process.

Postictal generalized electroencephalographic suppression is associated with generalized seizures

Postictal generalized electroencephalographic suppression (PGES) may be involved in sudden unexpected death in epilepsy (SUDEP). We examined whether the occurrence of PGES depends on seizure type and whether PGES occurs more frequently in people with epilepsy who died suddenly. EEG recordings of people with pharmacoresistant focal epilepsies who died from SUDEP after presurgical video/EEG telemetry were compared with recordings of living controls. To test if PGES depends on seizure type, EEG recordings of people with temporal lobe epilepsy who had complex partial seizures (CPS) and secondarily generalized tonic-clonic seizures (GTCS) were reviewed. A total of 122 seizures in 57 individuals have been included. PGES was observed in 15% of all seizures in 26% of all individuals. Secondarily GTCS were significantly associated with PGES. Neither presence nor duration of PGES differed between the SUDEP and control groups. In conclusion, PGES is facilitated by secondarily GTCS, but does not seem to be an independent risk factor for SUDEP.

An algorithm for detecting seizure termination in scalp EEG

Little effort has been devoted to developing algorithms that can detect the cessation of seizure activity in scalp EEG. Such algorithms could facilitate clinical applications such as the estimation of seizure duration or the delivery of therapies designed to mitigate postictal period symptoms. In this paper, we present a method for detecting the termination of seizure activity. When tested on 133 seizures from a public database, our method detected the end of 132 seizures with a mean absolute error of 10.3 ± 5.5 seconds of the time marked by an electroencephalographer. Furthermore, by pairing our seizure end detector with a previously published seizure onset detector, we could automatically estimate the duration of 85% of test seizures within a 15 second error margin.