Cycles in epilepsy

Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research

Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.

Chronobiology of epilepsy and sudden unexpected death in epilepsy

Epilepsy is a neurological disease characterized by spontaneous, unprovoked seizures. Various insults render the brain hyperexcitable and susceptible to seizure. Despite there being dozens of preventative anti-seizure medications available, these drugs fail to control seizures in nearly 1 in 3 patients with epilepsy. Over the last century, a large body of evidence has demonstrated that internal and external rhythms can modify seizure phenotypes. Physiologically relevant rhythms with shorter periodic rhythms, such as endogenous circadian rhythms and sleep-state, as well as rhythms with longer periodicity, including multidien rhythms and menses, influence the timing of seizures through poorly understood mechanisms. The purpose of this review is to discuss the findings from both human and animal studies that consider the effect of such biologically relevant rhythms on epilepsy and seizure-associated death. Patients with medically refractory epilepsy are at increased risk of sudden unexpected death in epilepsy (SUDEP). The role that some of these rhythms play in the nocturnal susceptibility to SUDEP will also be discussed. While the involvement of some of these rhythms in epilepsy has been known for over a century, applying the rhythmic nature of such phenomenon to epilepsy management, particularly in mitigating the risk of SUDEP, has been underutilized. As our understanding of the physiological influence on such rhythmic phenomenon improves, and as technology for chronic intracranial epileptiform monitoring becomes more widespread, smaller and less invasive, novel seizure-prediction technologies and time-dependent chronotherapeutic seizure management strategies can be realized.

Discovery of triazenyl triazoles as Nav1.1 channel blockers for treatment of epilepsy

The voltage-gated sodium (Na_v) channel is one of most important targets for treatment of epilepsy, and rufinamide is an approved third-generation anti-seizure drug as Na_v1.1 channel blocker. Herein, by triazenylation of rufinamide, we reported the triazenyl triazoles as new Na_v1.1 channel blocker for treatment of epilepsy. Through the electrophysiological activity assay, compound 6a and 6e were found to modulate the inactivation voltage of Na_v 1.1 channel with shift of -10.07 mv and -11.28 mV, respectively. In the pentylenetetrazole (PTZ) mouse model, 6a and 6e reduced the seizure level, prolonged seizure latency and improved the survival rate of epileptic mice at an intragastric administration of 50 mg/kg dosage. In addition, 6a also exhibited promising effectiveness in the maximal electroshock (MES) mouse model and possessed moderate pharmacokinetic profiles. These results demonstrated that 6a was a novel Na_v1.1 channel blocker for treatment of epilepsy.

Weak self-supervised learning for seizure forecasting: a feasibility study

This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.

Seizure Forecasting by High-Frequency Activity (80-170 Hz) in Long-term Continuous Intracranial EEG Recordings

BACKGROUND AND OBJECTIVES

Reliable seizure forecasting has important implications in epilepsy treatment and improving the quality of lives for people with epilepsy. High-frequency activity (HFA) is a biomarker that has received significant attention over the past 2 decades, but its predictive value in seizure forecasting remains uncertain. This work aimed to determine the utility of HFA in seizure forecasting.

METHODS

We used seizure data and HFA (80-170 Hz) data obtained from long-term, continuous intracranial EEG recordings of patients with drug-resistant epilepsy. Instantaneous rates and phases of HFA cycles were used as features for seizure forecasting. Seizure forecasts based on each individual HFA feature, and with the use of a combined approach, were generated pseudo-prospectively (causally). To compute the instantaneous phases for pseudo-prospective forecasting, real-time phase estimation based on an autoregressive model was used. Features were combined with a weighted average approach. The performance of seizure forecasting was primarily evaluated by the area under the curve (AUC).

RESULTS

Of 15 studied patients (median recording duration 557 days, median seizures 151), 12 patients with >10 seizures after 100 recording days were included in the pseudo-prospective analysis. The presented real-time phase estimation is feasible and can causally estimate the instantaneous phases of HFA cycles with high accuracy. Pseudo-prospective seizure forecasting based on HFA rates and phases performed significantly better than chance in 11 of 12 patients, although there were patient-specific differences. Combining rate and phase information improved forecasting performance compared to using either feature alone. The combined forecast using the best-performing channel yielded a median AUC of 0.70, a median sensitivity of 0.57, and a median specificity of 0.77.

DISCUSSION

These findings show that HFA could be useful for seizure forecasting and represent proof of concept for using prior information of patient-specific relationships between HFA and seizures in pseudo-prospective forecasting. Future seizure forecasting algorithms might benefit from the inclusion of HFA, and the real-time phase estimation approach can be extended to other biomarkers.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that HFA (80-170 Hz) in long-term continuous intracranial EEG can be useful to forecast seizures in patients with refractory epilepsy.

Sub-scalp electroencephalography: A next-generation technique to study human neurophysiology

Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEG^TM SubQ, Neuroview^TM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.

Future Opportunities for Research in Rescue Treatments

Clinical studies of rescue medications for seizure clusters are limited and are designed to satisfy regulatory requirements, which may not fully consider the needs of the diverse patient population that experiences seizure clusters or utilize rescue medication. The purpose of this narrative review is to examine the factors that contribute to, or may influence the quality of, seizure cluster research with a goal of improving clinical practice. We address five areas of unmet needs and provide advice for how they could enhance future trials of seizure cluster treatments. The topics addressed in this article are: (1) unaddressed end points to pursue in future studies, (2) roles for devices to enhance rescue medication clinical development programs, (3) tools to study seizure cluster prediction and prevention, (4) the value of other designs for seizure cluster studies, and (5) unique challenges of future trial paradigms for seizure clusters. By focusing on novel end points and technologies with value to patients, caregivers, and clinicians, data obtained from future studies can benefit the diverse patient population that experiences seizure clusters, providing more effective, appropriate care as well as alleviating demands on health care resources.

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.

Neurostimulation as a Method of Treatment and a Preventive Measure in Canine Drug-Resistant Epilepsy: Current State and Future Prospects

Modulation of neuronal activity for seizure control using various methods of neurostimulation is a rapidly developing field in epileptology, especially in treatment of refractory epilepsy. Promising results in human clinical practice, such as diminished seizure burden, reduced incidence of sudden unexplained death in epilepsy, and improved quality of life has brought neurostimulation into the focus of veterinary medicine as a therapeutic option. This article provides a comprehensive review of available neurostimulation methods for seizure management in drug-resistant epilepsy in canine patients. Recent progress in non-invasive modalities, such as repetitive transcranial magnetic stimulation and transcutaneous vagus nerve stimulation is highlighted. We further discuss potential future advances and their plausible application as means for preventing epileptogenesis in dogs.

Treatment of pilocarpine-induced epileptic seizures in adult male mice

Epilepsy is one of the most common neurological disorders affecting most social, economic and biological aspects of human life. Most patients with epilepsy have uncontrolled seizures and drug side effects despite the medications. Patients with epilepsy often have problems with attention, memory, and information processing speed, which may be due to seizures, underlying causes, or anticonvulsants. Therefore, improving seizure control and reducing or changing the anti-epileptic drugs can solve these problems, but these problems will not be solved in most cases. In this work, we looked at the effects of pioglitazone, a Peroxisome Proliferator-Activated Receptor agonist used to treat type 2 diabetes, on pilocarpine-induced seizures in mice. The Racine scale was used to classify pilocarpine-induced convulsions. After that, all of the animals were beheaded, and the brain and hippocampus were dissected. Finally, biochemical techniques were used to determine the levels of Malondialdehyde and Catalase activity, as well as Superoxide Dismutase and Glutathione Reductase in the hippocampus. The results of this investigation suggest that pioglitazone's antioxidant action may play a key role in its neuroprotective properties against pilocarpine-induced seizure neuronal damage.

Louis EK, Gregg NM, Lundstrom BN, Crowe CM, Attia TP, Crepeau D, Balzekas I, Marks VS, Wheeler LP, Cimbalnik J, Cook M, Janca R, Sturges BK, Leyde K, Miller KJ, Van Gompel JJ, Denison T, Worrell GA, Kremen V. Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

Life-span characterization of epilepsy and comorbidities in Dravet syndrome mice carrying a targeted deletion of exon 1 of the Scn1a gene

OBJECTIVE

Dravet Syndrome (DS) is a catastrophic form of paediatric epilepsy associated with multiple comorbidities mainly caused by mutations in the SCN1A gene. DS progresses in three different phases termed febrile, worsening and stabilization stage. Mice that are haploinsufficient for Scn1a faithfully model each stage of DS, although various aspects have not been fully described, including the temporal appearance and sex differences of the epilepsy and comorbidities. The aim of the present study was to investigate the epilepsy landscape according to the progression of DS and the long-term co-morbidities in the Scn1a(+/-)^tm1Kea DS mouse line that are not fully understood yet.

METHODS

Male and female F1.Scn1a(+/+) and F1.Scn1a(+/-)^tm1Kea mice were assessed in the hyperthermia model or monitored by video electroencephalogram (vEEG) and wireless video-EEG according to the respective stage of DS. Long-term comorbidities were investigated through a battery of behaviour assessments in ~6 month-old mice.

RESULTS

At P18, F1.Scn1a(+/-)^tm1Kea mice showed the expected sensitivity to hyperthermia-induced seizures. Between P21 and P28, EEG recordings in F1.Scn1a(+/-)^tm1Kea mice combined with video monitoring revealed a high frequency of SRS and SUDEP. Power spectral analyses of background EEG activity also revealed that low EEG power in multiple frequency bands was associated with SUDEP risk in F1.Scn1a(+/-)^tm1Kea mice during the worsening stage of DS. Later, SRS and SUDEP rates stabilized and then declined in F1.Scn1a(+/-)^tm1kea mice. Incidence of SRS ending with death in F1.Scn1a(+/-)^tm1kea mice displayed variations with the time of day and sex, with female mice displaying higher numbers of severe seizures resulting in greater SUDEP risk. F1.Scn1a(+/-)^tm1kea mice ~6 month-old displayed fewer behavioural impairments than expected including hyperactivity, impaired exploratory behaviour and poor nest building performance.

SIGNIFICANCE

These results reveal new features of this model that will optimize use and selection of phenotype assays for future studies on the mechanisms, diagnosis, and treatment of DS.

Catch the rhythm!

Thalamic Deep Brain Stimulation Modulates Cycles of Seizure Risk in Epilepsy

Gregg NM, Sladky V, Nejedly P, et al. Sci Rep. 2021;11:24250. doi:10.1101/2021.08.25.21262616.

Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and multiday cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and multiday IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and multiday (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS.

Sleep and Temporal Lobe Epilepsy – Associations, Mechanisms and Treatment Implications

In this systematic review, we aim to describe the association between temporal lobe epilepsy (TLE) and sleep, with bidirectional links in mechanisms and therapeutic aspects. Sleep stages may variably impact seizure occurrence, secondary generalization and the development, frequency and distribution of interictal epileptiform discharges. Conversely, epilepsy affects sleep micro- and macroarchitecture. TLE, the most frequent form of drug resistant epilepsy (DRE), shares an enduring relationship with sleep, with some intriguing potential mechanisms specific to anatomic localization, linking the two. Sleep characteristics of TLE may also inform localizing properties in persons with DRE, since seizures arising from the temporal lobe seem to be more common during wakefulness, compared to seizures of extratemporal origin. Polysomnographic studies indicate that persons with TLE may experience excessive daytime somnolence, disrupted sleep architecture, increased wake after sleep onset, frequent shifts in sleep stages, lower sleep efficiency, decreased rapid eye movement (REM) sleep, and possibly, increased incidence of sleep apnea. Limited literature suggests that effective epilepsy surgery may remedy many of these objective and subjective sleep-related concerns, via multipronged effects, apart from reduced seizure frequency. Additionally, sleep abnormalities also seem to influence memory, language and cognitive-executive function in both medically controlled and refractory TLE. Another aspect of the relationship pertains to anti-seizure medications (ASMs), which may contribute significantly to sleep characteristics and abnormalities in persons with TLE. Literature focused on specific aspects of TLE and sleep is limited, and heterogeneous. Future investigations are essential to understand the pathogenetic mechanisms linking sleep abnormalities on epilepsy outcomes in the important sub-population of TLE.

SleepSEEG: Automatic sleep scoring using intracranial EEG recordings only

OBJECTIVE

To perform automatic sleep scoring based only on intracranial EEG, without the need for scalp electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction.

APPROACH

Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch.

MAIN RESULTS

An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%.

SIGNIFICANCE

The automatic algorithm can perform sleep scoring of long term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.

Responsive Neurostimulation Targeting the Anterior, Centromedian and Pulvinar Thalamic Nuclei and the Detection of Electrographic Seizures in Pediatric and Young Adult Patients

Background

Responsive neurostimulation (RNS System) has been utilized as a treatment for intractable epilepsy. The RNS System delivers stimulation in response to detected abnormal activity, via leads covering the seizure foci, in response to detections of predefined epileptiform activity with the goal of decreasing seizure frequency and severity. While thalamic leads are often implanted in combination with cortical strip leads, implantation and stimulation with bilateral thalamic leads alone is less common, and the ability to detect electrographic seizures using RNS System thalamic leads is uncertain.

Objective

The present study retrospectively evaluated fourteen patients with RNS System depth leads implanted in the thalamus, with or without concomitant implantation of cortical strip leads, to determine the ability to detect electrographic seizures in the thalamus. Detailed patient presentations and lead trajectories were reviewed alongside electroencephalographic (ECoG) analyses.

Results

Anterior nucleus thalamic (ANT) leads, whether bilateral or unilateral and combined with a cortical strip lead, successfully detected and terminated epileptiform activity, as demonstrated by Cases 2 and 3. Similarly, bilateral centromedian thalamic (CMT) leads or a combination of one centromedian thalamic alongside a cortical strip lead also demonstrated the ability to detect electrographic seizures as seen in Cases 6 and 9. Bilateral pulvinar leads likewise produced reliable seizure detection in Patient 14. Detections of electrographic seizures in thalamic nuclei did not appear to be affected by whether the patient was pediatric or adult at the time of RNS System implantation. Sole thalamic leads paralleled the combination of thalamic and cortical strip leads in terms of preventing the propagation of electrographic seizures.

Conclusion

Thalamic nuclei present a promising target for detection and stimulation via the RNS System for seizures with multifocal or generalized onsets. These areas provide a modifiable, reversible therapeutic option for patients who are not candidates for surgical resection or ablation.

Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction

This paper presents a new analog front-end classification system that serves as a wake-up engine for digital back-ends, targeting embedded devices for epileptic seizure prediction. Predicting epileptic seizures is of major importance for the patient’s quality of life as they can lead to paralyzation or even prove fatal. Existing solutions rely on power hungry embedded digital inference engines that typically consume several μW or even mW. To increase the embedded device’s autonomy, a new approach is presented combining an analog feature extractor with an analog Gaussian mixture model-based binary classifier. The proposed classification system provides an initial, power-efficient prediction with high sensitivity to switch on the digital engine for the accurate evaluation. The classifier’s circuit is chip-area efficient, operating with minimal power consumption (180 nW) at low supply voltage (0.6 V), allowing long-term continuous operation. Based on a real-world dataset, the proposed system achieves 100% sensitivity to guarantee that all seizures are predicted and good specificity (69%), resulting in significant power reduction of the digital engine and therefore the total system. The proposed classifier was designed and simulated in a TSMC 90 nm CMOS process, using the Cadence IC suite.

Evaluation of Sleep Disturbances in Patients with Nocturnal Epileptic Seizures in a Romanian Cross-Sectional Study

(1) Background: Based on the premise that epilepsy is frequently associated with hypnopathies, in this study we aim to analyze the prevalence of sleep disturbances among patients with epilepsy, with exclusively or predominantly nocturnal seizures, in relation to demographic factors as well as clinical and electroencephalography (EEG) aspects. (2) Methods: 69 patients with nocturnal epilepsy were included in our study. Sleep disturbances were measured with the Pittsburgh Sleep Quality Index (PSQI) questionnaire, followed by a long-term video-EEG monitoring during sleep. We analyzed the PSQI results in relation to patients' gender and age and determined the correlations between the PSQI scores and the modifications on video-EEG recordings, in comparison to a control group of 25 patients with epilepsy but without nocturnal seizures. (3) Results: We found a statistically significant difference between the PSQI of patients with nocturnal seizures compared to those without nocturnal epileptic manifestations. In the experimental group, the mean PSQI score was 7.36 ± 3.91 versus 5.04 ± 2.56 in controls. In women, the average PSQI score was 8.26, whilst in men it only reached 6.41, highlighting a statistically significant difference between genders (p ˂ 0.01). By examining the relationships between the PSQI scores and certain sleep-related factors, evidenced on the nocturnal video-EEG, we found a statistically significant difference between PSQI values of patients who reached the N2 stage, and those who reached the N3 stage of nonrapid eye movement (NREM) sleep, highlighting that those with a more superficial nocturnal sleep also had higher PSQI scores. There were no statistically significant differences regarding the PSQI scores between patients with or without interictal epileptiform discharges, and also in the few patients with nocturnal seizures where we captured ictal activity. (4) Conclusions: we evidenced in this study a poor quality of sleep in patients with nocturnal epilepsy, mostly in women, independent of age. We observed that sleep disturbances were due to superficial and fragmented sleep with frequent microarousals, not necessarily caused by the electrical epileptiform activity.

Epileptic seizures and link to memory processes

Epileptogenesis is a complex and not well understood phenomenon. Here, we explore the hypothesis that epileptogenesis could be "hijacking" normal memory processes, and how this hypothesis may provide new directions for epilepsy treatment. First, we review similarities between the hypersynchronous circuits observed in epilepsy and memory consolidation processes involved in strengthening neuronal connections. Next, we describe the kindling model of seizures and its relation to long-term potentiation model of synaptic plasticity. We also examine how the strengthening of epileptic circuits is facilitated during the physiological slow wave sleep, similarly as episodic memories. Furthermore, we present studies showing that specific memories can directly trigger reflex seizures. The neuronal hypersynchrony in early stages of Alzheimer's disease, and the use of anti-epileptic drugs to improve the cognitive symptoms in this disease also suggests a connection between memory systems and epilepsy. Given the commonalities between memory processes and epilepsy, we propose that therapies for memory disorders might provide new avenues for treatment of epileptic patients.

Circannual incidence of seizure evacuations from the Canadian Arctic

OBJECTIVES

Emerging evidence suggests that circadian rhythms affect seizure propensity in addition to, and possibly independent of, sleep-wake states. Subject to extreme seasonal changes in light and dark, the northerly Arctic can serve as a "natural experiment" to assess the real-life impact of environmental influences on seizure severity. Therefore, we evaluated the timing of seizure evacuations over 11.25 years in a well-defined region of the Canadian Arctic.

METHODS

Retrospective review of EEG database and patient records at the single "bottleneck" hospital to which all patients from the Kivalliq Region in Nunavut, Canada are evacuated for seizure emergencies. We calculated the mean resultant length (MRL) of circular data for circannual analysis, and conducted Rayleigh's test to assess for a statistical departure from circular uniformity.

RESULTS

Screening 40,392 EEGs, we found 117 medical evacuations from 99 distinct individuals from September 2009 to November 2020. Most evacuations occurred month-wise in May (19%); week-wise within a 7-day period in February (5%), June (5%), or November (5%); and day-wise within a 24-hour period in June (3%) or November (3%). Maximal MRL clustering occurred in April no matter if analyzed by day (0.16333, p = 0.04), week (0.16296, p = 0.04), or month (0.1736, p = 0.03).

CONCLUSIONS

A relative circannual increase in seizure evacuations between the winter and summer solstices may be related to increasing sleep loss when day length grows. Fewer evacuations between the summer and winter solstices may be related to decreased daylight and "catching up" on sleep when night length grows. Additional factors likely also play a role in circannual variation of seizure evacuations in the Arctic, which warrants further research.

Bilateral temporal lobe epilepsy: How many seizures are required in chronic ambulatory electrocorticography to estimate the laterality ratio?

OBJECTIVE

This study was undertaken to measure the duration of chronic electrocorticography (ECoG) needed to attain stable estimates of the seizure laterality ratio in patients with drug-resistant bilateral temporal lobe epilepsy (BTLE).

METHODS

We studied 13 patients with drug-resistant BTLE who were implanted for at least 1 year with a responsive neurostimulation device (RNS System) that provides chronic ambulatory ECoG. Bootstrap analysis and nonlinear regression were applied to model the relationship between chronic ECoG duration and the probability of capturing at least one seizure. Laterality of electrographic seizures in chronic ECoG was compared with the seizure laterality ratio from Phase 1 scalp video-electroencephalographic (vEEG) monitoring. The Kaplan-Meier estimator was used to evaluate time to seizure laterality ratio convergence.

RESULTS

Seizure laterality ratios from Phase 1 scalp vEEG monitoring correlated poorly with those from RNS chronic ECoG (r = .31, p = .30). Across the 13 patients, average electrographic seizure frequencies ranged from 1.4 seizures/month to 5.1 seizures/day. A 50% probability of recording at least one electrographic seizure required 9.1 days of chronic ECoG, and 90% probability required 44.3 days of chronic ECoG. A median recording duration of 150.9 days (5 months), corresponding to a median of 16 seizures, was needed before confidence intervals for the seizure laterality ratio reliably contained the long-term value. The median recording duration before the point estimate of the seizure laterality ratio converged to a stationary value was 236.8 days (7.9 months).

SIGNIFICANCE

RNS chronic ECoG overcomes temporal sampling limitations intrinsic to inpatient Phase 1 vEEG evaluations. In patients with drug-resistant BTLE, approximately 8 months of chronic RNS ECoG are needed to precisely estimate the seizure laterality ratio, with 75% of people with BTLE achieving convergence after 1 year of RNS recording. For individuals who are candidates for unilateral resection based on seizure laterality, optimized recording duration may help avert morbidity associated with delay to definitive treatment.

Spatio-temporal spike dynamics predict surgical outcome in adult focal epilepsy

OBJECTIVE

We hypothesized that spatio-temporal dynamics of interictal spikes reflect the extent and stability of epileptic sources and determine surgical outcome.

METHODS

We studied 30 consecutive patients (14 good outcome). Spikes were detected in prolonged stereo-electroencephalography recordings. We quantified the spatio-temporal dynamics of spikes using the variance of the spike rate, line length and skewness of the spike distribution, and related these features to outcome. We built a logistic regression model, and compared its performance to traditional markers.

RESULTS

Good outcome patients had more dominant and stable sources than poor outcome patients as expressed by a higher variance of spike rates, a lower variance of line length, and a lower variance of positive skewness (ps < 0.05). The outcome was correctly predicted in 80% of patients. This was better or non-inferior to predictions based on a focal lesion (p = 0.016), focal seizure-onset zone, or complete resection (ps > 0.05). In the five patients where traditional markers failed, spike distribution predicted the outcome correctly. The best results were achieved by 18-h periods or longer.

CONCLUSIONS

Analysis of spike dynamics shows that surgery outcome depends on strong, single and stable sources.

SIGNIFICANCE

Our quantitative method has the potential to be a reliable predictor of surgical outcome.

Clustering of Spontaneous Recurrent Seizures in a Mouse Model of Extended Hippocampal Kindling

Acute repetitive seizures or seizure clusters are common in epileptic patients. Seizure clusters are associated with a high risk of developing status epilepticus and increased morbidity and mortality. Seizure clusters are also recognizable in spontaneous recurrent seizures (SRS) that occur in animal models of epilepsy. The electrical kindling of a limbic structure is a commonly used model of temporal lobe epilepsy. Although classic kindling over the course of a few weeks does not generally induce SRS, extended kindling over the course of a few months can induce SRS in several animal species. SRS in kindled cats often occur in clusters, but the existence of seizure clusters in rodent models of extended kindling remains to be demonstrated. We explored the existence of seizure clusters in mice following extended hippocampal kindling. Adult male mice (C57BL/6) experienced twice daily hippocampal stimulations and underwent continuous 24-hour electroencephalogram (EEG)-video monitoring after ≥80 stimulations. SRS events were recognized by EEG discharges and associated motor seizures. Seizure clusters, defined as ≥4 seizures per cluster and intra-cluster inter-seizure intervals ≤ 120 min, were observed in 19 of the 20 kindled mice. Individual mice showed variable seizure clusters in terms of cluster incidence and circadian-like expression patterns. For clusters consisting of 4-7 seizures and intra-seizure intervals ≤ 20 min, no consistent changes in inter-seizure intervals, EEG discharge duration, or motor seizure severity scores were observed approaching cluster termination. These results suggested that seizure clustering represents a prominent feature of SRS in hippocampal kindled mice. We speculate that, despite experimental limitations and confounding factors, systemic homeostatic mechanisms that have yet to be explored may play an important role in governing the occurrence and termination of seizure clusters.

Circadian Rhythms, Disease and Chronotherapy

Circadian clocks are biological timing mechanisms that generate 24-h rhythms of physiology and behavior, exemplified by cycles of sleep/wake, hormone release, and metabolism. The adaptive value of clocks is evident when internal body clocks and daily environmental cycles are mismatched, such as in the case of shift work and jet lag or even mistimed eating, all of which are associated with physiological disruption and disease. Studies with animal and human models have also unraveled an important role of functional circadian clocks in modulating cellular and organismal responses to physiological cues (ex., food intake, exercise), pathological insults (e.g. virus and parasite infections), and medical interventions (e.g. medication). With growing knowledge of the molecular and cellular mechanisms underlying circadian physiology and pathophysiology, it is becoming possible to target circadian rhythms for disease prevention and treatment. In this review, we discuss recent advances in circadian research and the potential for therapeutic applications that take patient circadian rhythms into account in treating disease.

Wearable electroencephalography for ultra-long-term seizure monitoring: a systematic review and future prospects

INTRODUCTION

: Wearable electroencephalography (EEG) for objective seizure counting might transform the clinical management of epilepsy. Non-EEG modalities have been validated for the detection of convulsive seizures, but there is still an unmet need for the detection of non-convulsive seizures.

AREAS COVERED

: The main objective of this systematic review was to explore the current status on wearable surface- and subcutaneous EEG for long-term seizure monitoring in epilepsy. We included 17 studies and evaluated the progress on the field, including device specifications, intended populations, and main results on the published studies including diagnostic accuracy measures. Furthermore, we examine the hurdles for widespread clinical implementation. This systematic review and expert opinion both consults the PRISMA guidelines and reflects on the future perspectives of this emerging field.

EXPERT OPINION

: Wearable EEG for long-term seizure monitoring is an emerging field, with plenty of proposed devices and proof-of-concept clinical validation studies. The possible implications of these devices are immense including objective seizure counting and possibly forecasting. However, the true clinical value of the devices, including effects on patient important outcomes and clinical decision making is yet to be unveiled and large-scale clinical validation trials are called for.

Chronic electroencephalography in epilepsy with a responsive neurostimulation device: current status and future prospects

INTRODUCTION

Implanted neurostimulation devices are gaining traction as therapeutic options for people with certain forms of drug-resistant focal epilepsy. Some of these devices enable chronic electroencephalography (cEEG), which offers views of the dynamics of brain activity in epilepsy over unprecedented time horizons.

AREAS COVERED

This review focuses on clinical insights and basic neuroscience discoveries enabled by analyses of cEEG from an exemplar device, the NeuroPace RNS® System. Applications of RNS cEEG covered here include counting and lateralizing seizures, quantifying medication response, characterizing spells, forecasting seizures, and exploring mechanisms of cognition. Limitations of the RNS System are discussed in the context of next-generation devices in development.

EXPERT OPINION

The wide temporal lens of cEEG helps capture the dynamism of epilepsy, revealing phenomena that cannot be appreciated with short duration recordings. The RNS System is a vanguard device whose diagnostic utility rivals its therapeutic benefits, but emerging minimally invasive devices, including those with subscalp recording electrodes, promise to be more applicable within a broad population of people with epilepsy. Epileptology is on the precipice of a paradigm shift in which cEEG is a standard part of diagnostic evaluations and clinical management is predicated on quantitative observations integrated over long timescales.

Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study

Background

Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link has previously been considered in epilepsy research, with potential implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy).

Methods

We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase.

Findings

Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles.

Interpretation

Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.

Funding

This research received funding from the Australian Government National Health and Medical Research Council (investigator grant 1178220), the Australian Government BioMedTech Horizons program, and the Epilepsy Foundation of America's ‘My Seizure Gauge’ grant.

Anterior nucleus of the thalamus seizure detection in ambulatory humans

There is a paucity of data to guide anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) with brain sensing. The clinical Medtronic Percept DBS device provides constrained brain sensing power within a frequency band (power-in-band [PIB]), recorded in 10-min averaged increments. Here, four patients with temporal lobe epilepsy were implanted with an investigational device providing full bandwidth chronic intracranial electroencephalogram (cEEG) from bilateral ANT and hippocampus (Hc). ANT PIB-based seizure detection was assessed. Detection parameters were cEEG PIB center frequency, bandwidth, and epoch duration. Performance was evaluated against epileptologist-confirmed Hc seizures, and assessed by area under the precision-recall curve (PR-AUC). Data included 99 days of cEEG, and 20, 278, 3, and 18 Hc seizures for Subjects 1-4. The best detector had 7-Hz center frequency, 5-Hz band width, and 10-s epoch duration (group PR-AUC = .90), with 75% sensitivity and .38 false alarms per day for Subject 1, and 100% and .0 for Subjects 3 and 4. Hc seizures in Subject 2 did not propagate to ANT. The relative change of ANT PIB was maximal ipsilateral to seizure onset for all detected seizures. Chronic ANT and Hc recordings provide direct guidance for ANT DBS with brain sensing.

Relationship Between Epilepsy and Dreaming: Current Knowledge, Hypotheses, and Perspectives

The interactions between epilepsy and sleep are numerous and the impact of epilepsy on cognition is well documented. Epilepsy is therefore likely to influence dreaming as one sleep-related cognitive activity. The frequency of dream recall is indeed decreased in patients with epilepsy, especially in those with primary generalized seizures. The content of dreams is also disturbed in epilepsy patients, being more negative and with more familiar settings. While several confounding factors (anti-seizure medications, depression and anxiety disorders, cognitive impairment) may partly account for these changes, some observations suggest an effect of seizures themselves on dreams. Indeed, the incorporation of seizure symptoms in dream content has been described, concomitant or not with a focal epileptic discharge during sleep, suggesting that epilepsy might directly or indirectly interfere with dreaming. These observations, together with current knowledge on dream neurophysiology and the links between epilepsy and sleep, suggest that epilepsy may impact not only wake- but also sleep-related cognition.

The pilocarpine model of mesial temporal lobe epilepsy: Over one decade later, with more rodent species and new investigative approaches

Fundamental work on the mechanisms leading to focal epileptic discharges in mesial temporal lobe epilepsy (MTLE) often rests on the use of rodent models in which an initial status epilepticus (SE) is induced by kainic acid or pilocarpine. In 2008 we reviewed how, following systemic injection of pilocarpine, the main subsequent events are the initial SE, the latent period, and the chronic epileptic state. Up to a decade ago, rats were most often employed and they were frequently analysed only behaviorally. However, the use of transgenic mice has revealed novel information regarding this animal model. Here, we review recent findings showing the existence of specific neuronal events during both latent and chronic states, and how optogenetic activation of specific cell populations modulate spontaneous seizures. We also address neuronal damage induced by pilocarpine treatment, the role of neuroinflammation, and the influence of circadian and estrous cycles. Updating these findings leads us to propose that the rodent pilocarpine model continues to represent a valuable tool for identifying the basic pathophysiology of MTLE.

Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.

Forecasting Seizure Likelihood With Wearable Technology

The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study tracked participants' (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using smartphone seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset. Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 min in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles. Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure and epileptic activity cycles are utilized.

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic

It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.

Growing role of S100B protein as a putative therapeutic target for neurological- and nonneurological-disorders

S100B is a calcium-binding protein mainly expressed by astrocytes, but also localized in other definite neural and extra-neural cell types. While its presence in biological fluids is widely recognized as a reliable biomarker of active injury, growing evidence now indicates that high levels of S100B are suggestive of pathogenic processes in different neural, but also extra-neural, disorders. Indeed, modulation of S100B levels correlates with the occurrence of clinical and/or toxic parameters in experimental models of diseases such as Alzheimer's and Parkinson's diseases, amyotrophic lateral sclerosis, muscular dystrophy, multiple sclerosis, acute neural injury, inflammatory bowel disease, uveal and retinal disorders, obesity, diabetes and cancer, thus directly linking the levels of S100B to pathogenic mechanisms. In general, deletion/inactivation of the protein causes the improvement of the disease, whereas its over-expression/administration induces a worse clinical presentation. This scenario reasonably proposes S100B as a common therapeutic target for several different disorders, also offering new clues to individuate possible unexpected connections among these diseases.