Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal electroencephalography investigation

Neural mass modeling reveals that hyperexcitability underpins slow-wave sleep changes in children with epilepsy

OBJECTIVE

The relationship between sleep and epilepsy is important but imperfectly understood. We sought to understand the mechanisms that explain the differences in sleep homeostasis observed in children with epilepsy.

METHODS

We used a neural mass model to replicate sleep electroencephalography (EEG) recorded from 15 children with focal lesional epilepsies and 16 healthy age-matched controls. Different parameter sets were recovered in the model for each subject.

RESULTS

The model revealed that sleep EEG differences are driven by enhanced firing rates in the neuronal populations of patients, which arise predominantly due to enhanced excitatory synaptic currents. These differences were more marked in patients who had seizures within 72 h after the sleep recording. Furthermore, model parameters inferred from patients resided closer to model parameters inferred from a typical seizure rhythm.

SIGNIFICANCE

These results demonstrate that brain mechanisms relating to epilepsy manifest in the interictal EEG in slow-wave sleep, and that EEG recorded from patients can be mapped to synaptic deficits that may explain their predisposition to seizures. Neural mass models inferred from sleep EEG data have the potential to generate new biomarkers to predict seizure occurrence and inform treatment decisions.

Sleep Deprivation Increases Mortality Risk Among Older Adults with Epilepsy

Introduction: Among U.S. adults, over 3 million report a history of epilepsy, accounting for nearly 1.2% of the population. Sleep deprivation is a well-known risk factor for increased likelihood, intensity, and length of seizures. However, the long-term impact of sleep deprivation on people with epilepsy is not well explored. The purpose of this study was to assess mortality risk among individuals with epilepsy based on sleep duration. Methods: Data from the 2008-2018 National Health Interview Survey (NHIS) were linked with mortality data from the National Death Index (NDI) for US adults aged 65 years and older. Survival curves showed the combined effect of sleep deprivation and epilepsy, using the Kaplan-Meier product-limit method to estimate the percent survival of the subject at each point in time. Results: For all-cause mortality, the unadjusted hazard ratio (HR) for sleep deprivation to no sleep deprivation among people with epilepsy (PWE) was HR = 1.92. The adjusted HR was elevated, HR = 1.94, among individuals who had epilepsy and sleep deprivation but close to 1.0 among individuals who had a history of sleep deprivation without epilepsy after adjusting for demographic and health variables. Conclusions: From a nationally representative sample, this first-of-its-kind study in the U.S. found that sleep deprivation and epilepsy combined have worse outcomes than sleep deprivation alone. Clinicians should screen and manage sleep disorders to improve their long-term prognosis of people with epilepsy.

Mechanistic insights into the interaction between epilepsy and sleep

Epidemiological evidence has demonstrated associations between sleep and epilepsy, but we lack a mechanistic understanding of these associations. If sleep affects the pathophysiology of epilepsy and the risk of seizures, as suggested by correlative evidence, then understanding these effects could provide crucial insight into the basic mechanisms that underlie the development of epilepsy and the generation of seizures. In this Review, we provide in-depth discussion of the associations between epilepsy and sleep at the cellular, network and system levels and consider the mechanistic underpinnings of these associations. We also discuss the clinical relevance of these associations, highlighting how they could contribute to improvements in the management of epilepsy. A better understanding of the mechanisms that govern the interactions between epilepsy and sleep could guide further research and the development of novel approaches to the management of epilepsy.

Association of baseline sleep duration and sleep quality with seizure recurrence in newly treated patients with epilepsy

OBJECTIVE

Although sleep duration and sleep quality are considered to be significant factors associated with epilepsy and seizure risk, findings are inconsistent, and their joint association remains uncertain. This study aimed to determine independent and joint associations of these two modifiable sleep features with seizure recurrence risk in newly treated patients with epilepsy (PWE).

METHODS

This is a prospective cohort study of newly treated PWE at a comprehensive epilepsy center in northeast China between June 2020 and December 2023. Self-reported sleep duration and sleep quality were collected at baseline. All patients were followed for 12 months for recurrent seizures. Cox proportional hazard regression models were used to estimate the hazard ratios (HRs) of seizure recurrence. Models fitted with restricted cubic spline were conducted to test for linear and nonlinear shapes of each association.

RESULTS

A total of 209 patients were included, and 103 experienced seizure recurrence during follow-up. Baseline short sleep was significantly associated with greater risk of seizure recurrence (adjusted HR = 2.282, 95% confidence interval [CI] = 1.436-3.628, p < .001). Sleep duration (h/day) and recurrent seizure risk showed a significant nonlinear U-shaped association, with a nadir at 8 h/day. Baseline poor sleep quality was significantly associated with greater risk of seizure recurrence (adjusted HR = 1.985, 95% CI = 1.321-2.984, p < .001). Pittsburgh Sleep Quality Index score and seizure recurrence risk exhibited a positive linear association. Participants with a combination of poor quality-short sleep showed the highest risk of seizure recurrence (adjusted HR = 3.13, 95% CI = 1.779-5.507, p < .001) compared to the referent good quality-intermediate sleep group.

SIGNIFICANCE

Baseline sleep duration and sleep quality were independently and jointly associated with risk of seizure recurrence in newly treated PWE. Our results point to an important potential role of baseline sleep duration and sleep quality in shaping seizure risk.

Timing Mechanisms for Circadian Seizures

For centuries, epileptic seizures have been noticed to recur with temporal regularity, suggesting that an underlying biological rhythm may play a crucial role in their timing. In this review, we propose to adopt the framework of chronobiology to study the circadian timing of seizures. We first review observations made on seizure timing in patients with epilepsy and animal models of the disorder. We then present the existing chronobiology paradigm to disentangle intertwined circadian and sleep-wake timing mechanisms. In the light of this framework, we review the existing evidence for specific timing mechanisms in specific epilepsy syndromes and highlight that current knowledge is far from sufficient. We propose that individual seizure chronotypes may result from an interplay between independent timing mechanisms. We conclude with a research agenda to help solve the urgency of ticking seizures.

Automated algorithms for seizure forecast: a systematic review and meta-analysis

This study aims to review the proposed methodologies and reported performances of automated algorithms for seizure forecast. A systematic review was conducted on studies reported up to May 10, 2024. Four databases and registers were searched, and studies were included when they proposed an original algorithm for automatic human epileptic seizure forecast that was patient specific, based on intraindividual cyclic distribution of events and/or surrogate measures of the preictal state and provided an evaluation of the performance. Two meta-analyses were performed, one evaluating area under the ROC curve (AUC) and another Brier Skill Score (BSS). Eighteen studies met the eligibility criteria, totaling 43 included algorithms. A total of 419 patients participated in the studies, and 19442 seizures were reported across studies. Of the analyzed algorithms, 23 were eligible for the meta-analysis with AUC and 12 with BSS. The overall mean AUC was 0.71, which was similar between the studies that relied solely on surrogate measures of the preictal state, on cyclic distributions of events, and on a combination of these. BSS was also similar for the three types of input data, with an overall mean BSS of 0.13. This study provides a characterization of the state of the art in seizure forecast algorithms along with their performances, setting a benchmark for future developments. It identified a considerable lack of standardization across study design and evaluation, leading to the proposal of guidelines for the design of seizure forecast solutions.

Preoptic area controls sleep-related seizure onset in a genetic epilepsy mouse model

In genetic and refractory epileptic patients, seizure activity exhibits sleep-related modulation/regulation and sleep and seizure are intermingled. In this study, by using one het Gabrg2 Q390X KI mice as a genetic epilepsy model and optogenetic method in vivo, we found that subcortical POA neurons were active within epileptic network from the het Gabrg2 Q390X KI mice and the POA activity preceded epileptic (poly)spike-wave discharges(SWD/PSDs) in the het Gabrg2 Q390X KI mice. Meanwhile, as expected, the manipulating of the POA activity relatively altered NREM sleep and wake periods in both wt and the het Gabrg2 Q390X KI mice. Most importantly, the short activation of epileptic cortical neurons alone did not effectively trigger seizure activity in the het Gabrg2 Q390X KI mice. In contrast, compared to the wt mice, combined the POA nucleus activation and short activation of the epileptic cortical neurons effectively triggered or suppressed epileptic activity in the het Gabrg2 Q390X KI mice, indicating that the POA activity can control the brain state to trigger seizure incidence in the het Gabrg2 Q390X KI mice in vivo. In addition, the suppression of POA nucleus activity decreased myoclonic jerks in the Gabrg2 Q390X KI mice. Overall, this study discloses an operational mechanism for sleep-dependent seizure incidence in the genetic epilepsy model with the implications for refractory epilepsy. This operational mechanism also underlies myoclonic jerk generation, further with translational implications in seizure treatment for genetic/refractory epileptic patients and with contribution to memory/cognitive deficits in epileptic patients.

Clinical Utility of Sleep Recordings During Presurgical Epilepsy Evaluation With Stereo-Electroencephalography: A Systematic Review

SUMMARY

Although the role of sleep in modulating epileptic activity is well established, many epileptologists overlook the significance of considering sleep during presurgical epilepsy evaluations in cases of drug-resistant epilepsy. Here, we conducted a comprehensive literature review from January 2000 to May 2023 using the PubMed electronic database and compiled evidence to highlight the need to revise the current clinical approach. All articles were assessed for eligibility by two independent reviewers. Our aim was to shed light on the clinical value of incorporating sleep monitoring into presurgical evaluations with stereo-electroencephalography. We present the latest developments on the important bidirectional interactions between sleep and various forms of epileptic activity observed in stereo-electroencephalography recordings. Specifically, epileptic activity is modulated by different sleep stages, peaking in non-rapid eye movement sleep, while being suppressed in rapid eye movement sleep. However, this modulation can vary across different brain regions, underlining the need to account for sleep to accurately pinpoint the epileptogenic zone during presurgical assessments. Finally, we offer practical solutions, such as automated sleep scoring algorithms using stereo-electroencephalography data alone, to seamlessly integrate sleep monitoring into routine clinical practice. It is hoped that this review will provide clinicians with a readily accessible roadmap to the latest evidence concerning the clinical utility of sleep monitoring in the context of stereo-electroencephalography and aid the development of therapeutic and diagnostic strategies to improve patient surgical outcomes.

Effects of exercise on sleep in patients with epilepsy: A systematic review

Exercise interventions in epilepsy have been shown to improve seizure frequency, physical capacity, quality of life, mood, and cognitive functioning. However, the effectiveness of exercise in improving sleep in epilepsy is less clear. The purpose of this report is to identify the published literature regarding exercise interventions in people with epilepsy to determine 1) what proportion of published clinical trials assess sleep as an outcome, and 2) what benefits of exercise interventions on sleep have been observed. We searched the PubMed, PsycINFO, and SCOPUS electronic databases using the search terms "epilepsy AND [exercise OR physical activity]" and identified 23 articles reporting on 18 unique clinical trials. Nine studies were conducted in adults, five in children, and four in adults and children with active seizures, controlled seizures, or both. Exercise modalities included aerobic exercise, strength training, walking, and yoga, among others, and some also included educational and motivational components. Exercise effects on sleep were tested in four studies, two of which only included indirect measures of sleep- and rest-related fatigue, with mixed results. Of the two reports assessing sleep directly, one reported marginal non-significant improvements in subjective sleep quality and no improvements in objective sleep quality in children after twelve weeks of walking, and the other reported no benefits in subjective sleep quality after twelve weeks of combined aerobic, strength, and flexibility training in adults. Given the health benefits of sleep and detrimental effects of sleep deprivation in epilepsy, epilepsy researchers need to assess the effects of exercise interventions on sleep.

Phenotypic overlap between rare disease patients and variant carriers in a large population cohort informs biological mechanisms

The biological mechanisms giving rise to the extreme symptoms exhibited by rare disease patients are complex, heterogenous, and difficult to discern. Understanding these mechanisms is critical for developing treatments that address the underlying causes of diseases rather than merely the presenting symptoms. Moreover, the same dysfunctional biological mechanisms implicated in rare recessive diseases may also lead to milder and potentially preventable symptoms in carriers in the general population. Seizures are a common, extreme phenotype that can result from diverse and often elusive biological pathways in patients with ultrarare or undiagnosed disorders. In this pilot study, we present an approach to understand the biological pathways leading to seizures in patients from the Undiagnosed Diseases Network (UDN) by analyzing aggregated genotype and phenotype data from the UK Biobank (UKB). Specifically, we look for enriched phenotypes across UKB participants who harbor rare variants in the same gene known or suspected to be causally implicated in a UDN patient's recessively manifesting disorder. Analyzing these milder but related associated phenotypes in UKB participants can provide insight into the disease-causing molecular mechanisms at play in the rare disease UDN patient. We present six vignettes of undiagnosed patients experiencing seizures as part of their recessive genetic condition, and we discuss the potential mechanisms underlying the spectrum of symptoms associated with UKB participants to the severe presentations exhibited by UDN patients. We find that in our set of rare disease patients, seizures may result from diverse, multi-step pathways that involve multiple body systems. Analyses of large-scale population cohorts such as the UKB can be a critical tool to further our understanding of rare diseases in general.

Sleep physiology in patients with epilepsy: Influence of seizures on rapid eye movement (REM) latency and REM duration

OBJECTIVE

A well-established bidirectional relationship exists between sleep and epilepsy. Patients with epilepsy tend to have less efficient sleep and shorter rapid eye movement (REM) sleep. Seizures are far more likely to arise from sleep transitions and non-REM sleep compared to REM sleep. Delay in REM onset or reduction in REM duration may have reciprocal interactions with seizure occurrence. Greater insight into the relationship between REM sleep and seizure occurrence is essential to our understanding of circadian patterns and predictability of seizure activity. We assessed a cohort of adults undergoing evaluation of drug-resistant epilepsy to examine whether REM sleep prior to or following seizures is delayed in latency or reduced in quantity.

METHODS

We used a spectrogram-guided approach to review the video-electroencephalograms of patients' epilepsy monitoring unit admissions for sleep scoring to determine sleep variables.

RESULTS

In our cohort of patients, we found group- and individual-level delay of REM latency and reduced REM duration when patients experienced a seizure before the primary sleep period (PSP) of interest or during the PSP of interest. A significant increase in REM latency and decrease in REM quantity were observed on nights where a seizure occurred within 4 h of sleep onset. No change in REM variables was found when investigating seizures that occurred the day after the PSP of interest. Our study is the first to provide insight about a perisleep period, which we defined as 4-h periods before and after the PSP.

SIGNIFICANCE

Our results demonstrate a significant relationship between seizures occurring prior to the PSP, during the PSP, and in the 4-h perisleep period and a delay in REM latency. These findings have implications for developing a biomarker of seizure detection as well as longer term seizure risk monitoring.

A polysomnographic study of slow-wave sleep loss in elderly patients with epilepsy

Objective

The primary objective is to explore what causes slow-wave sleep loss in elderly patients with epilepsy. The secondary objective is to identify the PSG characteristics in elderly patients with epilepsy. The clinical demographics, sleep architecture, sleep-related events, and interictal epileptiform discharges are to be evaluated in the objectives.

Methods

The video electroencephalography (VEEG) and polysomnogram (PSG) data from 44 elderly patients with epilepsy and 52 elderly patients with sleep disorders but without definite central nervous system diseases were analysed. This was a case-control study. The differences in the PSG sleep architecture parameters (total sleep time (TST), sleep efficiency, wake after sleep onset, etc.) and sleep-related events (apnea hypopnea index, oxygen desaturation index (ODI), periodic limb movement index, etc.) between the epilepsy and control groups. As Additionally, these parameters were assessed within the elderly patients with epilepsy, comparing the slow-wave sleep existence and slow-wave sleep loss groups, using VEEG and PSG.

Results

The epileptic group exhibited significantly lower TST (343.477 ± 96.3046min vs 389.115 ± 61.5727min, p < 0.05), rapid eye movement (%) (13.011 ± 7.5384 vs 16.992 ± 6.7025, p < 0.05), non-rapid eye movement stage 3 (%) (1.35[0,7.225] vs 3.65[0.425,13.75], p < 0.05), and sleep efficiency (%) (69.482 ± 14.1771% vs 77.242 ± 10.6171%, p < 0.05). Conversely, the ODI (25.6[9.825,51.775] events/hour vs 16.85[5.3,30.425] events/hour, p < 0.05) and spontaneous arousal index (4.0455[2.1805,6.9609] events/hour vs 2.9709[1.4747,5.0554] events/hour, p < 0.05) were significantly higher in elderly patients with epilepsy. The prevalence of obstructive sleep apnea-hypopnea syndrome (OSAHS) was significantly higher in the slow-wave sleep loss group than in the slow-wave sleep existence group (100% vs 77.8%, p < 0.05). The incidence of slow-wave sleep loss was lower in patients with epilepsy aged between 75 and 85 years compared to those aged between 65 and 75 years.

Conclusion

Elderly patients with epilepsy exhibit higher levels of ODI and spontaneous arousal index. Our findings indicate that OSAHS could be a contributing factor to slow-wave sleep loss in this population. The incidence of slow-wave sleep loss was lower in patients aged above 75 years among elderly patients with epilepsy.

Seizure forecasting: Bifurcations in the long and winding road

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.

The impact of epilepsy and antiseizure medications on sleep: Findings from a large European survey in adults with epilepsy and matched controls

OBJECTIVE

To assess the impact of epilepsy and antiseizure medications (ASMs) on sleep quality in people with epilepsy (PWE).

METHODS

An online survey was conducted in France, Germany, Italy, Spain and the UK among PWE taking >1 ASM and matched controls. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Associations between sleep quality (global PSQI) and overall quality of life (QoL; assessed using the 12-Item Short Form Survey [SF-12]) and sleep quality and depressive symptoms (assessed using the Neurological Disorders Depression Inventory for Epilepsy [NDDI-E]) were also evaluated.

RESULTS

Overall, 500 PWE and 500 matched controls were included. PWE had significantly greater mean global PSQI scores than controls (9.32 vs 7.56; p < 0.0001), with 80% reporting a score >5 versus 66% of controls (p < 0.001). PWE experienced significantly more problems with most PSQI components than controls. Mean global PSQI scores in PWE receiving 2 versus ≥3 ASMs were 9.03 and 10.18, respectively (p < 0.004); global PSQI scores >5 were reported in 76% versus 90%, respectively (p = 0.001). Regimens containing lamotrigine or phenobarbital were associated with poorer sleep quality than those without these ASMs. In PWE, negative correlations were identified between global PSQI scores and both the SF-12 physical and mental components (Pearson's correlation coefficient [PCC], -0.61 and -0.40, respectively); NDDI-E and global PSQI scores were positively correlated (PCC, 0.6).

CONCLUSIONS

PWE experience significantly worse sleep quality than people without epilepsy, with some ASMs contributing to poorer sleep. QoL and physical and mental health were all affected by sleep quality in PWE.

Sleep and epilepsy: A clinical and pathophysiological overview

The interaction between sleep and epilepsy is complex. A better understanding of the mechanisms linking sleep and epilepsy appears increasingly important as it may improve diagnosis and therapeutic strategies in patients with epilepsy. In this narrative review, we aim to (i) provide an overview of the physiological and pathophysiological processes linking sleep and epilepsy; (ii) present common sleep disorders in patients with epilepsy; (iii) discuss how sleep and sleep disorders should be considered in new therapeutic approaches to epilepsy such as neurostimulation; and (iv) present the overall nocturnal manifestations and differential diagnosis between epileptic seizures and parasomnia.

The Relationship Between Sleep, Epilepsy, and Development: a Review

PURPOSE OF REVIEW

To review the relationship between sleep, neurodevelopment, and epilepsy and potential underlying physiological mechanisms.

RECENT FINDINGS

Recent studies have advanced our understanding of the role of sleep in early brain development and epilepsy. Epileptogenesis has been proposed to occur when there is a failure of normal adaptive processes of synaptic and homeostatic plasticity. This sleep-dependent transformation may explain the cognitive impairment seen in epilepsy, especially when occurring early in life. The glymphatic system, a recently discovered waste clearance system of the central nervous system, has been described as a potential mechanism underlying the relationship between sleep and seizures and may account for the common association between sleep deprivation and increased seizure risk. Epilepsy and associated sleep disturbances can critically affect brain development and neurocognition. Here we highlight recent findings on this topic and emphasize the importance of screening for sleep concerns in people with epilepsy.

Louis EK, Kremen V, Worrell GA. Automated sleep classification with chronic neural implants in freely behaving canines

Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.

The contribution of sleep and co-occurring neurodevelopmental conditions to quality of life in children with epilepsy

BACKGROUND

Health-related quality of life (HRQOL) in children with epilepsy (CWE) is multifactorial and can be affected not only by epilepsy-specific variables but also co-occurring conditions such as sleep disturbances, autism, and attention deficit hyperactivity disorder (ADHD). While highly prevalent in CWE, these conditions are underdiagnosed despite having a significant impact on HRQOL. Sleep problems have a complex relationship with epilepsy and neurodevelopmental characteristics. However, little is known about how these issues interact and contribute to HRQOL.

OBJECTIVES

The current study aims to explore the relationship between sleep and neurodevelopmental characteristics on HRQOL in CWE.

METHODS

36 CWE aged 4-16 years old were recruited from two hospitals and asked to wear an actiwatch for a period of 14 days and caregivers completed a series of questionnaires assessing co-occurrences and epilepsy-specific variables.

RESULTS

A high proportion of CWE (78.13%) presented significant sleep problems. Informant-reported sleep problems were significantly predictive of HRQOL above seizure severity and the number of antiseizure medications. Interestingly, informant-reported sleep problems were no longer significantly predictive of HRQOL when neurodevelopmental characteristics were considered, indicating a possible mediating effect. Similarly, actigraphy-defined sleep (variability in sleep onset latency) displayed a similar effect but only for ADHD characteristics, whereas autistic characteristics and variability in sleep onset latency continued to exert an individual effect on HRQOL.

CONCLUSION

These data from our study shed light on the complicated relationship between sleep, neurodevelopmental characteristics and epilepsy. Findings suggest that the impact of sleep on HRQOL in CWE is possibly mediated by neurodevelopmental characteristics. Furthermore, the impact this triangular relationship exerts on HRQOL is dependent on the type of tool used to measure sleep. These findings highlight the importance of a multidisciplinary approach to epilepsy management.

Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration

Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

Sleep during and following critical illness: A narrative review

Sleep is a complex process influenced by biological and environmental factors. Disturbances of sleep quantity and quality occur frequently in the critically ill and remain prevalent in survivors for at least 12 mo. Sleep disturbances are associated with adverse outcomes across multiple organ systems but are most strongly linked to delirium and cognitive impairment. This review will outline the predisposing and precipitating factors for sleep disturbance, categorised into patient, environmental and treatment-related factors. The objective and subjective methodologies used to quantify sleep during critical illness will be reviewed. While polysomnography remains the gold-standard, its use in the critical care setting still presents many barriers. Other methodologies are needed to better understand the pathophysiology, epidemiology and treatment of sleep disturbance in this population. Subjective outcome measures, including the Richards-Campbell Sleep Questionnaire, are still required for trials involving a greater number of patients and provide valuable insight into patients’ experiences of disturbed sleep. Finally, sleep optimisation strategies are reviewed, including intervention bundles, ambient noise and light reduction, quiet time, and the use of ear plugs and eye masks. While drugs to improve sleep are frequently prescribed to patients in the ICU, evidence supporting their effectiveness is lacking.

Putative roles for homeostatic plasticity in epileptogenesis

Homeostatic plasticity allows neural circuits to maintain an average activity level while preserving the ability to learn new associations and efficiently transmit information. This dynamic process usually protects the brain from excessive activity, like seizures. However, in certain contexts, homeostatic plasticity might produce seizures, either in response to an acute provocation or more chronically as a driver of epileptogenesis. Here, we review three seizure conditions in which homeostatic plasticity likely plays an important role: acute drug withdrawal seizures, posttraumatic or disconnection epilepsy, and cyclic seizures. Identifying the homeostatic mechanisms active at different stages of development and in different circuits could allow better targeting of therapies, including determining when neuromodulation might be most effective, proposing ways to prevent epileptogenesis, and determining how to disrupt the cycle of recurring seizure clusters.

Clocking Epilepsies: A Chronomodulated Strategy-Based Therapy for Rhythmic Seizures

Epilepsy is a neurological disorder characterized by hypersynchronous recurrent neuronal activities and seizures, as well as loss of muscular control and sometimes awareness. Clinically, seizures have been reported to display daily variations. Conversely, circadian misalignment and circadian clock gene variants contribute to epileptic pathogenesis. Elucidation of the genetic bases of epilepsy is of great importance because the genetic variability of the patients affects the efficacies of antiepileptic drugs (AEDs). For this narrative review, we compiled 661 epilepsy-related genes from the PHGKB and OMIM databases and classified them into 3 groups: driver genes, passenger genes, and undetermined genes. We discuss the potential roles of some epilepsy driver genes based on GO and KEGG analyses, the circadian rhythmicity of human and animal epilepsies, and the mutual effects between epilepsy and sleep. We review the advantages and challenges of rodents and zebrafish as animal models for epileptic studies. Finally, we posit chronomodulated strategy-based chronotherapy for rhythmic epilepsies, integrating several lines of investigation for unraveling circadian mechanisms underpinning epileptogenesis, chronopharmacokinetic and chronopharmacodynamic examinations of AEDs, as well as mathematical/computational modeling to help develop time-of-day-specific AED dosing schedules for rhythmic epilepsy patients.

Decrease in wearable-based nocturnal sleep efficiency precedes epileptic seizures

Introduction

While it is known that poor sleep is a seizure precipitant, this association remains poorly quantified. This study investigated whether seizures are preceded by significant changes in sleep efficiency as measured by a wearable equipped with an electrocardiogram, respiratory bands, and an accelerometer.

Methods

Nocturnal recordings from 47 people with epilepsy hospitalized at our epilepsy monitoring unit were analyzed (304 nights). Sleep metrics during nights followed by epileptic seizures (24 h post-awakening) were compared to those of nights which were not.

Results

Lower sleep efficiency (percentage of sleep during the night) was found in the nights preceding seizure days (p < 0.05). Each standard deviation decrease in sleep efficiency and increase in wake after sleep onset was respectively associated with a 1.25-fold (95 % CI: 1.05 to 1.42, p < 0.05) and 1.49-fold (95 % CI: 1.17 to 1.92, p < 0.01) increased odds of seizure occurrence the following day. Furthermore, nocturnal seizures were associated with significantly lower sleep efficiency and higher wake after sleep onset (p < 0.05), as well as increased odds of seizure occurrence following wake (OR: 5.86, 95 % CI: 2.99 to 11.77, p < 0.001).

Discussion

Findings indicate lower sleep efficiency during nights preceding seizures, suggesting that wearable sensors could be promising tools for sleep-based seizure-day forecasting in people with epilepsy.

John EK, Potesta CV, Gallagher MJ, Macdonald RL, Zhou C. Sleep slow-wave oscillations trigger seizures in a genetic epilepsy model of Dravet syndrome

Sleep is the preferential period when epileptic spike-wave discharges appear in human epileptic patients, including genetic epileptic seizures such as Dravet syndrome with multiple mutations including SCN1A mutation and GABAA receptor γ2 subunit Gabrg2Q390X mutation in patients, which presents more severe epileptic symptoms in female patients than male patients. However, the seizure onset mechanism during sleep still remains unknown. Our previous work has shown that the sleep-like state-dependent homeostatic synaptic potentiation can trigger epileptic spike-wave discharges in one transgenic heterozygous Gabrg2+/Q390X knock-in mouse model.1 Here, using this heterozygous knock-in mouse model, we hypothesized that slow-wave oscillations themselves in vivo could trigger epileptic seizures. We found that epileptic spike-wave discharges in heterozygous Gabrg2+/Q390X knock-in mice exhibited preferential incidence during non-rapid eye movement sleep period, accompanied by motor immobility/facial myoclonus/vibrissal twitching and more frequent spike-wave discharge incidence appeared in female heterozygous knock-in mice than male heterozygous knock-in mice. Optogenetically induced slow-wave oscillations in vivo significantly increased epileptic spike-wave discharge incidence in heterozygous Gabrg2+/Q390X knock-in mice with longer duration of non-rapid eye movement sleep or quiet-wakeful states. Furthermore, suppression of slow-wave oscillation-related homeostatic synaptic potentiation by 4-(diethylamino)-benzaldehyde injection (i.p.) greatly attenuated spike-wave discharge incidence in heterozygous knock-in mice, suggesting that slow-wave oscillations in vivo did trigger seizure activity in heterozygous knock-in mice. Meanwhile, sleep spindle generation in wild-type littermates and heterozygous Gabrg2+/Q390X knock-in mice involved the slow-wave oscillation-related homeostatic synaptic potentiation that also contributed to epileptic spike-wave discharge generation in heterozygous Gabrg2+/Q390X knock-in mice. In addition, EEG spectral power of delta frequency (0.1-4 Hz) during non-rapid eye movement sleep was significantly larger in female heterozygous Gabrg2+/Q390X knock-in mice than that in male heterozygous Gabrg2+/Q390X knock-in mice, which likely contributes to the gender difference in seizure incidence during non-rapid eye movement sleep/quiet-wake states of human patients. Overall, all these results indicate that slow-wave oscillations in vivo trigger the seizure onset in heterozygous Gabrg2+/Q390X knock-in mice, preferentially during non-rapid eye movement sleep period and likely generate the sex difference in seizure incidence between male and female heterozygous Gabrg2+/Q390X knock-in mice.

The interaction between circadian rhythm and epilepsy

Evidence about the interaction between circadian rhythms (CR) and epilepsy has been expanded with the application of advanced detection technology. An adequate understanding of how circadian system and epilepsy interact with each other could contribute to more accurate seizure prediction as well as rapid development of potential treatment timed to specific phases of CR. In this review, we present the reciprocal relationship between CR and epileptic activities from aspects of sleep effect, genetic modulation and brain biochemistry. It has been found that sleep-wake patterns, circadian timing systems and multidien rhythms have essential roles in seizure activities and interictal epileptiform discharge (IED). For instance, specific distribution patterns of seizures and IED have been reported, i.e., lighter non-rapid eye movement (NREM) sleep stage (stage 2) induces seizures while deeper NREM sleep stage (stage 3) activates IEDs. Furthermore, the epilepsy type, seizure type and seizure onset zone can significantly affect the rhythms of seizure occurrence. Apart from the common seizure types, several specific epilepsy syndromes also have a close correlation with sleep-wakefulness patterns. Sleep influences the epilepsy rhythm, and conversely, epilepsy alters the sleep rhythm through multiple pathways. Clock genes accompanied by two feedback loops of regulation have an important role in cortical excitability and seizure occurrence, which may be involved in the mTORopathy. The suprachiasmatic nuclei (SCN) has a rhythm of melatonin and cortisol secretion under the circadian pattern, and then these hormones can feed back into a central oscillator to affect the SCN-dependent rhythms, leading to variable but prominent influence on epilepsy. Furthermore, we discuss the precise predictive algorithms and chronotherapy strategies based on different temporal patterns of seizure occurrence for patients with epilepsy, which may offer a valuable indication for non-invasive closed-loop treatment system. Optimization of the time and dose of antiseizure medications, and resynchronization of disturbed CR (by hormone therapy, light exposure, ketogenic diet, novel small molecules) would be beneficial for epileptic patients in the future. Before formal clinical practice, future large-scale studies are urgently needed to assist prediction and treatment of circadian seizure activities and address unsolved restrictions.

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.

Sleep and epilepsy: A snapshot of knowledge and future research lines

Sleep and epilepsy have a reciprocal relationship, and have been recognized as bedfellows since antiquity. However, research on this topic has made a big step forward only in recent years. In this narrative review we summarize the most stimulating discoveries and insights reached by the "European school." In particular, different aspects concerning the sleep-epilepsy interactions are analysed: (a) the effects of sleep on epilepsy; (b) the effects of epilepsy on sleep structure; (c) the relationship between epilepsy, sleep and epileptogenesis; (d) the impact of epileptic activity during sleep on cognition; (e) the relationship between epilepsy and the circadian rhythm; (f) the history and features of sleep hypermotor epilepsy and its differential diagnosis; (g) the relationship between epilepsy and sleep disorders.

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.

Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.

Thalamic deep brain stimulation modulates cycles of seizure risk in epilepsy

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.

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.