Distinctive polysomnographic traits in nocturnal frontal lobe epilepsy

Mice harboring the T316N variant in the GABAAR γ2 subunit exhibit sleep-related hypermotor epilepsy phenotypes and hypersynchronization in the thalamocortical pathway

OBJECTIVE

Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy syndrome characterized by seizures that predominantly occur during sleep. The pathogenesis of these seizures remains unclear. We previously detected rare variants in GABRG2, which encodes the γ2 subunit of γ-aminobutyric acid type A receptor (GABAAR), in patients with SHE and demonstrated that these variants impaired GABAAR function in vitro. However, the mechanisms by which GABRG2 variants contribute to seizure attacks during sleep remain unclear.

METHODS

In this study, we designed a knock-in (KI) mouse expressing the mouse Gabrg2 T316N variant, corresponding to human GABRG2 T317N variant, using CRISPR/Cas9. Continuous video-electroencephalogram monitoring and in vivo multichannel electrophysiological recordings were performed to explore seizure susceptibility to pentylenetetrazol (PTZ), alterations in the sleep-wake cycle, spontaneous seizure patterns, and synchronized activity in the motor thalamic nuclei (MoTN) and secondary motor cortex (M2). Circadian variations in the expression of total, membrane-bound, and synaptic GABAAR subunits were also investigated.

RESULTS

No obvious changes in gross morphology were detected in Gabrg2T316N/+ mice compared to their wild-type (Gabrg2+/+) littermates. Gabrg2T316N/+ mice share key phenotypes with patients, including sleep fragmentation and spontaneous seizures during sleep. Gabrg2T316N/+ mice showed increased susceptibility to PTZ-induced seizures and higher mortality after seizures. Synchronization of the local field potentials between the MoTN and M2 was abnormally enhanced in Gabrg2T316N/+ mice during light phase, when sleep dominates, accompanied by increased local activities in the MoTN and M2. Interestingly, in Gabrg2+/+ mice, GABAAR γ2 subunits showed a circadian increase on the neuronal membrane and synaptosomes in the transition from dark phase to light phase, which was absent in Gabrg2T316N/+ mice.

CONCLUSION

We generated a new SHE mouse model and provided in vivo evidence that rare variants of GABRG2 contribute to seizure attacks during sleep in SHE.

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.

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.

Fearful arousals in sleep terrors and sleep-related hypermotor epileptic seizures may involve the salience network and the acute stress response of Cannon and Selye

We consider the disorders of arousal and sleep-related hypermotor epilepsy as genetic twin-conditions, one without, one with epilepsy. They share an augmented arousal-activity during NREM sleep with sleep-wake dissociations, culminating in sleep terrors and sleep-related hypermotor seizures with similar symptoms. The known mutations underlying the two spectra are different, but there are multifold population-genetic-, family- and even individual (the two conditions occurring in the same person) overlaps supporting common genetic roots. In the episodes of disorders of arousal, the anterior cingulate, anterior insular and pre-frontal cortices (shown to be involved in fear- and emotion processing) are activated within a sleeping brain. These regions overlap with the seizure-onset zones of successfully operated sleep-related hypermotor seizures, and notably, belong to the salience network being consistent with its hubs. The arousal-relatedness and the similar fearful confusion occurring in sleep terrors and hypermotor seizures, make them alike acute stress-responses emerging from sleep; triggered by false alarms. The activation of the anterior cingulate, prefrontal and insular regions in the episodes of both conditions, can easily mobilize the hypothalamo-pituitary-adrenal axis (preparing fight-flight responses in wakefulness); through its direct pathways to and from the salience network. This hypothesis has never been studied.

Sleep, oscillations, and epilepsy

Sleep and wake are defined through physiological and behavioral criteria and can be typically separated into non-rapid eye movement (NREM) sleep stages N1, N2, and N3, rapid eye movement (REM) sleep, and wake. Sleep and wake states are not homogenous in time. Their properties vary during the night and day cycle. Given that brain activity changes as a function of NREM, REM, and wake during the night and day cycle, are seizures more likely to occur during NREM, REM, or wake at a specific time? More generally, what is the relationship between sleep-wake cycles and epilepsy? We will review specific examples from clinical data and results from experimental models, focusing on the diversity and heterogeneity of these relationships. We will use a top-down approach, starting with the general architecture of sleep, followed by oscillatory activities, and ending with ionic correlates selected for illustrative purposes, with respect to seizures and interictal spikes. The picture that emerges is that of complexity; sleep disruption and pathological epileptic activities emerge from reorganized circuits. That different circuit alterations can occur across patients and models may explain why sleep alterations and the timing of seizures during the sleep-wake cycle are patient-specific.

Sleep architecture in drug-naïve adult patients with epilepsy: Comparison between focal and generalized epilepsy

OBJECTIVE

Sleep impairment is one of the most common comorbidities affecting people with epilepsy (PWE). The bidirectional relation between epilepsy and sleep has been widely established. Several studies investigated subjective sleep quality and daytime vigilance in PWE, highlighting frequent complaints of sleep fragmentation, difficulties in falling asleep, and daytime sleepiness. The present study aimed to evaluate sleep structure in drug-naive PWE, distributed on the basis of epilepsy type, and compared with controls.

METHODS

This observational study included adult patients newly diagnosed with epilepsy and drug-naive as well as a control group of healthy subjects. All PWE and controls underwent a dynamic 24-h EEG with signals for sleep recording to evaluate sleep architecture, structure, continuity, and fragmentation.

RESULTS

Twenty-four PWE were included and distributed in two groups based on epilepsy type. Eleven patients were included in the generalized epilepsy group (63.6% male; 34.91 ± 9.80 years) and 13 patients in the focal epilepsy group (53.8% male; 38.69 ± 12.74 years). The control group included 16 subjects (56.3% male; 32.75 ± 12.19 years). Patients with generalized or focal epilepsy had a significantly lower sleep efficiency than controls. Moreover, both patient groups presented the alteration of markers of sleep fragmentation and loss of continuity, with higher indices of sleep stage transitions and arousal. Finally, the two patient groups presented less REM sleep than controls.

SIGNIFICANCE

This study highlighted the alteration of sleep quality, continuity, and stability in both patients with focal or generalized epilepsy compared with controls, also in the absence of ictal events. This sleep impairment resulted in the reduction of REM sleep. Therefore, these findings may be explained by the increase in awakenings and sleep stage shifts, which may be attributed to both sleep networks impairment and neurotransmission dysfunction in PWE, and also possibly triggered by paroxysmal interictal abnormalities.

The effect of lacosamide monotherapy on sleep architecture in patients with epilepsy: a preliminary evidence

Lacosamide (LCM) is a third-generation antiseizure medication (ASM), and its effect on sleep architecture was supported by a few studies in patients with drug-resistant epilepsy in which LCM was used as an add-on treatment. To gather knowledge on ASMs effects on sleep, this study aimed at evaluating the effects of LCM monotherapy on sleep in patients with focal epilepsy. Ten patients diagnosed with epilepsy (mean age 58.00 ± 14.77, 60.0% female, mean monthly seizure frequency 1.20 ± 2.48) starting LCM as monotherapy were included. Sleep architecture was assessed through polysomnography at baseline and at the 6-month follow-up visit. A significant decrease was observed in seizure frequency (p = 0.004), being all patients seizure-free at follow-up. At baseline, eight patients had poor sleep efficiency (< 85%). Sleep efficiency increased at follow-up, with only three patients having an index < 85% (p = 0.022). From baseline to follow-up, a significant decrease was observed in sleep latency (p = 0.022) and wakefulness after sleep onset (p = 0.047). Moreover, a significant decrease was observed in the percentage of stage 1 (Md = 6.70 vs Md = 3.85, p = 0.005) and stage 3 (Md = 27.70 vs Md = 22.35, p = 0.01) of Non-REM sleep. This study suggests that LCM monotherapy may positively impact sleep architecture in patients with epilepsy. The sleep efficiency improvement and the decrease of sleep latency and wakefulness after sleep onset observed at follow-up highlight better sleep stability and continuity in patients treated with LCM. Notably, all patients were seizure-free at follow-up, and seizure freedom may also concur to sleep structure improvement.

A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern

STUDY OBJECTIVES

Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night's sleep.

METHODS

Two ensemble classifiers were developed to automatically score the signals, one for "A-phase" and the other for "non-rapid eye movement" estimation. Both were based on three one-dimension convolutional neural networks. Six different inputs were produced from the EEG signal to feed the ensembles' classifiers. A proposed heuristic-oriented search algorithm individually tuned the classifiers' structures. The outputs of the two ensembles were combined to estimate the A-phase index (API). The models can also assess the A-phase subtypes, their API, and the CAP cycles and rate.

RESULTS

Four dataset variations were considered, examining healthy and sleep-disordered subjects. The A-phase average estimation's accuracy, sensitivity, and specificity range was 82%-87%, 72%-80%, and 82%-88%, respectively. A similar performance was attained for the A-phase subtype's assessments, with an accuracy range of 82%-88%. Furthermore, in the examined dataset's variations, the API metric's average error varied from 0.15 to 0.25 (with a median range of 0.11-0.24). These results were attained without manually removing wake or rapid eye movement periods, leading to a methodology suitable to produce a fully automatic CAP scoring algorithm.

CONCLUSIONS

Metrics based on API can be understood as a new view for CAP analysis, where the goal is to produce and examine a sleep stability profile.

Sleep macrostructure and cyclic alternating pattern in patients who underwent surgery for hippocampal sclerosis: A prospective controlled polysomnographic study

INTRODUCTION

Temporal lobe epilepsy due to hippocampal sclerosis (TLE-HS) is one of the most common drug-resistant epilepsy. Surgery is currently accepted as an effective and safe therapeutic approach compared to antiseizure medications (ASMs). The study aims to evaluate the effect of surgical treatment of TLE-HS on sleep profile and architecture by subjective and objective evaluation of sleep in basal condition after one month and one year.

METHODS

Thirteen patients with TLE-HS were recruited to undergo overnight polysomnography and a subjective evaluation of nocturnal sleep utilizing the Pittsburgh Sleep Quality Index (PSQI) and daytime somnolence through the Epworth Sleepiness Scale (ESS) in basal condition (T0), one month (T1) and one year after surgery (T2), respectively. Thirteen healthy controls (HC) matched for age, sex and BMI were recruited. Scoring and analysis of sleep macrostructure and cyclic alternating pattern (CAP) parameters were performed.

RESULTS

The comparison between patients in basal condition (T0) and HC showed a significant lower sleep efficiency (p = 0.003) and REM percentage (p < 0.001). Regarding CAP, patients at T0 showed higher total CAP rate (p < 0.001), CAP rate in N2 (p < 0.001), higher A3 (%) (p = 0.001), higher mean duration of A1 (p = 0.002), A3 index (p < 0.001), cycle in sequences (p < 0.001), lower B duration (p < 0.001), cycle mean duration (p < 0.001) than HC. Surgery did not induce significant changes in nocturnal macrostructural polysomnographic variables in T1 and T2. Lower CAP rate (T1 vs T0 and T2 vs T0 p < 0.001), CAP rate in N3 (T1 vs T0 and T2 vs T0 p < 0.001), A3 (%) (T1 vs T0 and T2 vs T0 p < 0.001); lower phase A2 index (T1 vs T0 p < 0.001) and A3 index (T1 vs T0 p < 0.001), lower phase A1 index (T2 vs T0 p < 0.001) and cycle in sequences (T2 vs T0 p = 0.002) higher B mean duration (T2 vs T0 p = 0.002). No significant differences were found between T1 and T2 in CAP parameters.

CONCLUSION

We found a significant NREM sleep instability in patients with TLE-HS compared with HC. In addition, anterior temporal lobectomy (ATL) induced a significant improvement in sleep continuity as evaluated by cyclic alternating pattern already one month later and this effect persisted after one year. ALT seems to restore a more resilient sleeping brain.

Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG

The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels' feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2-F4, C4-A1, F4-C4), which is in line with the CAP protocol to ensure multiple channels' arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application.

Sleep quality and architecture in Idiopathic generalized epilepsy: A systematic review and meta-analysis

Idiopathic generalized epilepsies are a group of sleep related epilepsy syndromes with sleep deprivation as a strong trigger for seizures and increased spike-wave activity during sleep and transition to sleep. Neuropsychological deficits are common in Idiopathic generalized epilepsy patients. Learning and memory processes are closely linked to sleep. Therefore, this systematic review and meta-analysis investigates the evidence of sleep disturbances in Idiopathic generalized epilepsy patients. A search of the databases EMBASE, Medline and Scopus identified 22 studies comparing polysomnographic parameters and scores of sleep questionnaires between Idiopathic generalized epilepsy patients and healthy controls. Random effect univariate meta-analyses revealed reduced sleep efficiency, total sleep time, proportion of N2 stage and prolonged REM onset latency in Idiopathic generalized epilepsy patients. Self-assessed sleep quality of patients measured by the Pittsburgh sleep quality index was lower in two thirds of reporting studies. Considering the influence on behavioral issues, cognitive performance and quality of life, the revealed alteration in sleep architecture and lower subjective sleep quality emphasizes the importance of screening for sleep disturbances in the medical care of patients with Idiopathic generalized epilepsy.

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.

Non-rapid eye movement sleep instability in adults with epilepsy: a systematic review and meta-analysis of cyclic alternating pattern

STUDY OBJECTIVES

Epilepsy is characterized by disrupted sleep architecture. Studies on sleep macro- and microstructure revealed that patients with epilepsy experience disturbed rapid eye movement (REM) sleep; however, no consensus has been reached on non-REM (NREM) sleep changes. Cyclic alternating pattern (CAP) is a marker of sleep instability that occurs only during NREM sleep. This meta-analysis investigated CAP differences between patients with epilepsy and healthy controls.

METHODS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines in searching PubMed, Embase, and Cochrane Central database for studies comparing polysomnographic sleep microstructures between patients with epilepsy and healthy controls. A meta-analysis using a random-effects model was performed. We compared CAP rates, percentages of phase A1, A2, A3 subtypes, and phase B durations between patients with epilepsy and healthy controls.

RESULTS

A total of 11 studies, including 209 patients with epilepsy and 197 healthy controls, fulfilled the eligibility criteria. Compared with healthy controls, patients with epilepsy had significantly increased CAP rates and decreased A1 subtype percentages, and patients with sleep-related epilepsy had increased A3 subtype percentages. Subgroup analyses revealed that antiseizure medications (ASMs) decreased CAP rates and increased phase B durations but did not affect the microstates of phase A in patients with sleep-related epilepsy.

CONCLUSIONS

This meta-analysis detected statistically significant differences in CAP parameters between patients with epilepsy and healthy controls. Our findings suggest patients with epilepsy experience NREM sleep instability. ASMs treatment may decrease NREM instability but did not alter the microstates of phase A.

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches

The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis—Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

Cyclic alternating patterns and arousals: what is relevant in obstructive sleep apnea? In Memoriam Mario Giovanni Terzano

PURPOSE OF REVIEW

To review main knowledges and gaps in the field of sleep microstructure, represented by the cyclic alternating pattern (CAP), in obstructive sleep apnea (OSA).

RECENT FINDINGS

The (electroencephalographic and autonomic) 'intensity' of arousals in OSA patients, measured through the metrics of CAP, correlate with OSA severity and with disease burden. Continuous positive airway pressure determines variations in sleep architecture (conventional parameters) and at the microstructural level, at different time points.

SUMMARY

CAP is not only an 'attractor' of arousals, but also organizes distribution of K-complexes and delta bursts in non-rapid eye movement sleep. Although attention is always concentrated on the A-phase of CAP, a crucial role is play by the phase B, which reflects a period of transient inhibition. Respiratory events in OSA are a typical example of phase B-associated condition, as they occur during the interval between successive A-phases. Accordingly sleep microstructure provides useful insights in the pathophysiology and estimation of OSA severity and may be exploited to follow-up treatment efficacy. In the complex relationship among sleep fragmentation, excessive daytime sleepiness, cognition and cardiovascular risk the CAP framework can offer an integrative perspective in a multidisciplinary scenario.

Expert Opinion: Managing sleep disturbances in people with epilepsy

Poor sleep and daytime sleepiness are common in people with epilepsy. Sleep disorders can disrupt seizure control and in turn sleep and vigilance problems can be exacerbated by seizures and by antiepileptic treatments. Nevertheless, these aspects are frequently overlooked in clinical practice and a clear agreement on the evidence-based guidelines for managing common sleep disorders in people with epilepsy is lacking. Recently, recommendations to standardize the diagnostic pathway for evaluating patients with sleep-related epilepsies and comorbid sleep disorders have been presented. To build on these, we adopted the Delphi method to establish a consensus within a group of experts and we provide practical recommendations for identifying and managing poor night-time sleep and daytime sleepiness in people with epilepsy. We recommend that a comprehensive clinical history of sleep habits and sleep hygiene should be always obtained from all people with epilepsy and their bed partners. A psychoeducational approach to inform patients about habits or practices that may negatively influence their sleep or their vigilance levels should be used, and strategies for avoiding these should be applied. In case of a suspected comorbid sleep disorder an appropriate diagnostic investigation should be performed. Moreover, the possible presence of sleep fragmentation induced by sleep-related seizures should be ruled out. Finally, the dose and timing of antiepileptic medications and other co-medications should be optimized to improve nocturnal sleep and avoid daytime sedation.

Seizure worsening in pregnancy in women with sleep-related hypermotor epilepsy (SHE): A historical cohort study

INTRODUCTION

Data on seizure course during pregnancy in women with epilepsy are limited. In particular, little is known about the causes underlying possible seizure worsening in this population. We therefore set out to explore worsening, in pregnancy, of sleep-related hypermotor epilepsy (SHE), a syndrome in which seizures are known to be triggered by sleep fragmentation, a condition common in pregnancy.

METHODS

From a cohort of consecutive patients with epilepsy who had one or more deliveries between January 2008 and March 2018, we retrospectively compared the rates of seizure worsening during pregnancy in SHE versus other epilepsies (NSHE). Worsening was defined as an increase in seizure frequency compared with the rate for the year prior to conception, including seizure recurrence after a year of seizure freedom, and/or new occurrence of tonic-clonic seizures.

RESULTS

We considered data on 11 pregnancies in women with SHE and 104 pregnancies in women with NSHE. Seizures worsened in six SHE pregnancies (54.5%) versus 18 NSHE ones (17.3%) (OR adjusted for preconception seizure frequency and polytherapy = 5.7, 95% CI = 1.6-20.8, p = 0.019).

CONCLUSIONS

Women with SHE have a higher risk of seizure worsening in pregnancy. This finding should be considered from the perspective of patient counseling.

Sleep in Neurologic Disorders

Sleep is a complex brain state with fundamental relevance for cognitive functions, synaptic plasticity, brain resilience, and autonomic balance. Sleep pathologies may interfere with cerebral circuit organization, leading to negative consequences and favoring the development of neurologic disorders. Conversely, the latter can interfere with sleep functions. Accordingly, assessment of sleep quality is always recommended in the diagnosis of patients with neurologic disorders and during neurorehabilitation programs. This review investigates the complex interplay between sleep and brain pathologies, focusing on diseases in which the association with sleep disturbances is commonly overlooked and whereby major benefits may derive from their proper management.

KCNT1-Related Epilepsy: A Review

KCNT1 gene encodes the sodium-dependent potassium channel reported as a causal factor for several different epileptic disorders. The gene has been also linked with cardiac disorders and in a family to sudden unexpected death in epilepsy. KCNT1 mutations, in most cases, result in a gain of function causing a neuronal hyperpolarization with loss of inhibition. Many early-onset epileptic encephalopathies related to gain of function of KCNT1 gene have been described, most often associated with two phenotypes: malignant migrating focal seizures of infancy and familial autosomal-dominant nocturnal frontal lobe epilepsy; however, there is no clear phenotype–genotype correlation, in fact same mutations have been represented in patients with West syndrome, Ohtahara syndrome, and early myoclonic encephalopathy. Additional neurologic features include intellectual disability, psychiatric disorders, hypotonia, microcephaly, strabismus, and movement disorders. Conventional anticonvulsant, vagal stimulation, and ketogenic diet have been used in the absence of clinical benefit in individuals with KCNT1-related epilepsy; in some patients, quinidine therapy off-label has been practiced successfully. This review aims to describe the characteristics of the gene, the phenotypes related to genetic mutations with the possible genotype–phenotype correlations and the treatments proposed to date, discussing the comorbidities reported in the literature.

Commonalities and Differences in NREM Parasomnias and Sleep-Related Epilepsy: Is There a Continuum Between the Two Conditions?

Introduction: Differential diagnosis between disorders of arousal (DoA) and sleep-related hypermotor epilepsy (SHE) often represents a clinical challenge. The two conditions may be indistinguishable from a semiological point of view and the scalp video-polysomnography is often uninformative. Both disorders are associated with variable hypermotor manifestations ranging from major events to fragments of a hierarchical continuum of increasing intensity, complexity, and duration. Given their semiological overlap we decided to explore the sleep texture of DoA and SHE seeking for similarities and differences.

Methods: We analyzed sleep macrostructure and CAP (cyclic alternating pattern) parameters in a cohort of 35 adult DoA patients, 40 SHE patients and 24 healthy sleepers, all recorded and scored in the same sleep laboratory. Nocturnal behavioral manifestations included minor motor events, paroxysmal arousals and major attacks in SHE, and simple, rising, or complex arousal movements in DoA.

Results: Compared to healthy controls, DoA and SHE showed similar amounts of sleep efficiency, light sleep, deep sleep, REM sleep, CAP subtypes. Both groups also showed slow wave sleep fragmentation and an increased representation of stage N3 in the second part of the night. The only discriminating elements between the two conditions regarded sleep length (more reduced in DoA) and sleep instability (more elevated in SHE). In DoA recordings, all motor episodes arose from NREM sleep: 37% during light NREM stages and 63% during stage N3 (simple arousal movements: 94%). In SHE recordings, 57% of major attacks occurred during stage N3.

Conclusions: So far, emphasis has been placed on the differentiation of sleep-related epilepsy and NREM arousal disorders. However, the impressive analogies between DoA and SHE suggest the existence of an underestimated continuum across the conditions, linked by increased levels of sleep instability, higher amounts of slow wave sleep and NREM/REM sleep imbalance. Sleep texture is extremely similar in the two conditions, although CAP metrics disclose quantitative differences. In particular, SHE patients show a higher arousal instability compared to DoA subjects. Given their clinical and epidemiological overlap, a common genetic background is also hypothesized. In such a perspective, we suggest that the consolidated dichotomy DoA vs. SHE should be reappraised.

On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes

The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern events for the full night is unpractical, with a high probability of miss classification, due to the large quantity of information that is produced during a full night recording. To address this concern, automatic methodologies were proposed using a long short-term memory to perform the classification of one electroencephalogram monopolar derivation signal. The proposed model is composed of three classifiers, one for each subtype, performing binary classification in a one versus all procedure. Two methodologies were tested: feed the pre-processed electroencephalogram signal to the classifiers; create features from the pre-processed electroencephalogram signal which were fed to the classifiers (feature-based methods). It was verified that the A1 subtype classification performance was similar for both methods and the A2 subtype classification was higher for the feature-based methods. However, the A3 subtype classification was found to be the most challenging to be performed, and for this classification, the feature-based methods were superior. A characterization analysis was also performed using a recurrence quantification analysis to further examine the subtypes characteristics. The average accuracy and area under the receiver operating characteristic curve for the A1, A2, and A3 subtypes of the feature-based methods were respectively: 82% and 0.92; 80% and 0.88; 85% and 0.86.

Circadian Rhythms and Epilepsy: A Suitable Case for Absence Epilepsy

Many physiological processes such as sleep, hormonal secretion, or thermoregulation, are expressed as daily rhythms orchestrated by the circadian timing system. A powerful internal clock mechanism ensures proper synchronization of vital functions within an organism on the one hand, and between the organism and the external environment on the other. Some of the pathological processes developing in the brain and body are subjected to circadian modulation as well. Epilepsy is one of the conditions which symptoms often worsen at a very specific time of a day. Variation in peak occurrence depends on the syndrome and localization of the epileptic focus. Moreover, the timing of some types of seizures is closely related to the sleep-wake cycle, one of the most prominent circadian rhythms. This review focuses on childhood absence epilepsy (CAE), a genetic generalized epilepsy syndrome, in which both, the circadian and sleep influences play a significant role in manifestation of symptoms. Human and animal studies report rhythmical occurrence of spike-wave discharges (SWDs), an EEG hallmark of CAE. The endogenous nature of the SWDs rhythm has been confirmed experimentally in a genetic animal model of the disease, rats of the WAG/Rij strain. Well-known detrimental effects of circadian misalignment were demonstrated to impact the severity of ongoing epileptic activity. SWDs are vigilance-dependent in both humans and animal models, occurring most frequently during passive behavioral states and light slow-wave sleep. The relationship with the sleep-wake cycle seems to be bidirectional, while sleep shapes the rhythm of seizures, epileptic phenotype changes sleep architecture. Circadian factors and the sleep-wake states dependency have a potential as add-ons in seizures' forecasting. Stability of the rhythm of recurrent seizures in individual patients has been already used as a variable which refines existing algorithms for seizures' prediction. On the other hand, apart from successful pharmacological approach, circadian hygiene including sufficient sleep and avoidance of internal desynchronization or sleep loss, may be beneficial for patients with epilepsy in everyday management of seizures.

Predictors of early recurrent seizure after first seizure presentation to an emergency service: A retrospective cohort study

PURPOSE

To identify the predictors of seizure recurrence after a first seizure (FS) presentation to an emergency service.

METHODS

The clinical characteristics of FS patients presenting to the emergency department (ED) of our university hospital from January 2001 to December 2014 were retrospectively reviewed. Recurrence of seizures following the FS was classified as: early recurrence (0-1 month), intermediate recurrence (>1-3 months), and late recurrence (>3-12 months). The significant predictors of seizure recurrence in each period were identified by Cox proportional hazard ratios (p < 0.05).

RESULTS

648 FS patients of the 1248 overall seizures patients were initially enrolled. 414 FS patients were eligible for statistical analysis. Following the FS, 134 patients (32.4%) had recurrent seizures in which half of the first recurrences occurred within 3 months. The significant predictors of overall recurrence were remote symptomatic seizure (RSS) (adjusted hazard ratio [adj. HR] = 2.21 (1.38, 3.55), p = 0.003) and nocturnal onset seizure (NOS) (adj. HR = 1.53 (1.03, 2.26), p = 0.039). Those of early recurrence were NOS (adj. HR = 2.78 (1.44, 5.37), p = 0.002) and anti-epileptic drug (AED) prescription (adj. HR = 2.19 (1.00, 4.80), p = 0.038). Those of intermediate recurrence were RSS (adj. HR = 3.96 (1.63, 9.60), p = 0.006) and AED prescription (adj. HR = 4.90 (1.42, 16.95), p = 0.003). No predictor of late recurrence was identified.

CONCLUSION

The seizure recurrence rate was high in the first 3 months following the FS. The significant predictors were RSS, NOS and AED prescription.

Abnormal behaviors during sleep from the viewpoint of sleep epileptology: current and future perspectives on diagnosis

Abnormal behaviors during sleep (ABDS) exhibit a myriad of symptoms. Their underlying diseases are also diverse, which include NREM/REM-related parasomnias, epilepsy and mental disorders. Since ABDS may severely affect a patient’s quality of life, giving an early and accurate diagnosis of the underlying disease (by analyzing video-polysomnographic data during the manifestation of ABDS) is of great importance. However, accurate diagnosis of ABDS is rather difficult. Recently it has been suggested that the pathology of (NREM/REM-related) parasomnias and epilepsy are closely related. In order to unravel the pathophysiological substrate of ABDS, it is essential to develop a novel approach based on sleep epileptology, a field which targets the interface between sleep medicine and epileptology.

Polysomnographic features differentiating disorder of arousals from sleep-related hypermotor epilepsy

Objective

The differential diagnosis between sleep-related hypermotor epilepsy (SHE) and disorders of arousal (DOA) may be challenging. We analyzed the stage and the relative time of occurrence of parasomnic and epileptic events to test their potential diagnostic accuracy as criteria to discriminate SHE from DOA.

Methods

Video-polysomnography recordings of 89 patients with a definite diagnosis of DOA (59) or SHE (30) were reviewed to define major or minor events and to analyze their stage and relative time of occurrence. The “event distribution index” was defined on the basis of the occurrence of events during the first versus the second part of sleep period time. A group analysis was performed between DOA and SHE patients to identify candidate predictors and to quantify their discriminative performance.

Results

The total number of motor events (i.e. major and minor) was significantly lower in DOA (3.2 ± 2.4) than in SHE patients (6.9 ± 8.3; p = 0.03). Episodes occurred mostly during N3 and N2 in DOA and SHE patients, respectively. The occurrence of at least one major event outside N3 was highly suggestive for SHE (p = 2*e-13; accuracy = 0.898, sensitivity = 0.793, specificity = 0.949). The occurrence of at least one minor event during N3 was highly suggestive for DOA (p = 4*e-5; accuracy = 0.73, sensitivity = 0.733, specificity = 0.723). The “event distribution index” was statistically higher in DOA for total (p = 0.012) and major events (p = 0.0026).

Conclusion

The stage and the relative time of occurrence of minor and major motor manifestations represent useful criteria to discriminate DOA from SHE episodes.

The role of sleep-related cognitive functions in the spectrum of benign epilepsy with centro-temporal spikes

Heterogeneous cognitive deficits have been described in the spectrum of benign epilepsy with centro-temporal spikes, which strongly correlate with the intensity of interictal epileptiform discharges and its spreading, in particular during sleep, mostly within the perisylvian cognitive network. The aim of this review is to discuss current findings regarding the connection between sleep alterations and cognitive function in the spectrum of benign epilepsy with centro-temporal spikes. A longer sleep onset latency is the only evident sleep macrostructure alteration reported in the spectrum of benign epilepsy with centro-temporal spikes. On a microstructural level, a higher spike count of descending compared to ascending slopes of sleep cycles, an impairment of slow wave downscaling, and amplitude and slope of slow waves were found in the spectrum of benign epilepsy with centro-temporal spikes. Moreover, children with benign epilepsy with centro-temporal spikes had a reduced non-rapid eye movement sleep instability, in terms of cyclic alternating pattern, similar to that found in children with attention-deficit hyperactivity disorders and in children with obstructive sleep apnea and centro-temporal spike during sleep. Children with benign epilepsy with centro-temporal spikes have a known comorbidity with attention-deficit hyperactivity disorders and obstructive sleep apnea.Conclusion: Considering the common sleep microstructure alterations, the presence of attention deficit and hyperactivity and/or sleep apnea may be a considered warning sign in the case of benign epilepsy with centro-temporal spikes. What is Known: • Sleep related-cognitive deficits have been described in the spectrum of benign epilepsy with centro-temporal spikes. The degree of sleep alterations may predict the neurocognitive outcome, and help clinicians to choose the right treatment. What is New: • Considering the common sleep microstructure alterations, attention deficit and sleep apnea, may be a considered warning signs.

A knowledge discovery methodology from EEG data for cyclic alternating pattern detection

Background

Detection and quantification of cyclic alternating patterns (CAP) components has the potential to serve as a disease bio-marker. Few methods exist to discriminate all the different CAP components, they do not present appropriate sensitivities, and often they are evaluated based on accuracy (AC) that is not an appropriate measure for imbalanced datasets.

Methods

We describe a knowledge discovery methodology in data (KDD) aiming the development of automatic CAP scoring approaches. Automatic CAP scoring was faced from two perspectives: the binary distinction between A-phases and B-phases, and also for multi-class classification of the different CAP components. The most important KDD stages are: extraction of 55 features, feature ranking/transformation, and classification. Classification is performed by (i) support vector machine (SVM), (ii) k-nearest neighbors (k-NN), and (iii) discriminant analysis. We report the weighted accuracy (WAC) that accounts for class imbalance.

Results

The study includes 30 subjects from the CAP Sleep Database of Physionet. The best alternative for the discrimination of the different A-phase subtypes involved feature ranking by the minimum redundancy maximum relevance algorithm (mRMR) and classification by SVM, with a WAC of 51%. Concerning the binary discrimination between A-phases and B-phases, k-NN with mRMR ranking achieved the best WAC of 80%.

Conclusions

We describe a KDD that, to the best of our knowledge, was for the first time applied to CAP scoring. In particular, the fully discrimination of the three different A-phases subtypes is a new perspective, since past works tried multi-class approaches but based on grouping of different sub-types. We also considered the weighted accuracy, in addition to simple accuracy, resulting in a more trustworthy performance assessment. Globally, better subtype sensitivities than other published approaches were achieved.

Sleep in children with neurodevelopmental difficulties

Sleep disorders in children with neurodevelopmental disorders are complex and reflect underlying genetic/biological and behavioural components. The sleep disorders are the same as in the typically developing child, although there may be some modifications to the presentation or the frequency depending on the phenotype. Consideration of the known phenotypes and environmental issues are important in defining management strategies. Despite this complexity, defined behavioural strategies with good sleep hygiene can have a significant effect on the sleep problem and on parental management of the behaviours.

Sleep architecture in adults with epilepsy: a systematic review

OBJECTIVES

To assess whether sleep architecture differs in subgroups of adults with epilepsy or in adults with epilepsy compared to control populations.

METHODS

We completed a systematic review of papers published in two databases up to May 2018, with adults with epilepsy who have undergone either two consecutive nights of in-laboratory polysomnography (PSG) or one night of ambulatory PSG. Our review followed the PRISMA statements and guidelines and the protocol was registered in the PROSPERO platform prior to initiation of the review process (record CRD42018084009).

RESULTS

Five studies out of 872 fulfilled our eligibility criteria. Only one study reported a significant difference in any sleep architecture parameter in group comparisons. Crespel et al., found that wake after sleep onset (WASO) time in minutes was higher in patients with refractory temporal lobe epilepsy when compared to refractory frontal lobe epilepsy (78.2 ± 5.3 vs. 28.1 ± 2.2; p < 0.01) and healthy controls (78.2 ± 5.3 vs. 27.9 ± 18.9; p < 0.007).

CONCLUSIONS

Only a few studies objectively assessed sleep in adults with epilepsy while controlling for key factors that influence sleep. However, even those reports are heterogeneous in regards to methodology and population characteristics. Further studies are required to access the extent of sleep architectural abnormalities in adults with epilepsy.

Increased cortical involvement and synchronization during CAP A1 slow waves

Slow waves recorded with EEG in NREM sleep are indicative of the strength and spatial extent of synchronized firing in neuronal assemblies of the cerebral cortex. Slow waves often appear in the A1 part of the cyclic alternating patterns (CAP), which correlate with a number of behavioral and biological parameters, but their physiological significance is not adequately known. We automatically detected slow waves from the scalp recordings of 37 healthy patients, visually identified CAP A1 events and compared slow waves during CAP A1 with those during NCAP. For each slow wave, we computed the amplitude, slopes, frequency, synchronization (synchronization likelihood) between specific cortical areas, as well as the location of origin and scalp propagation of individual waves. CAP A1 slow waves were characterized by greater spatial extent and amplitude, steeper slopes and greater cortical synchronization, but a similar prominence in frontal areas and similar propagation patterns to other areas on the scalp. Our results indicate that CAP A1 represents a period of highly synchronous neuronal firing over large areas of the cortical mantle. This feature may contribute to the role CAP A1 plays in both normal synaptic homeostasis and in the generation of epileptiform phenomena in epileptic patients.

Acquired Sleep-Related Hypermotor Epilepsy with Disrupted White Matter Tracts Assessed by Multishell Diffusion Magnetic Resonance Imaging

Sleep-related hypermotor epilepsy (SHE) (previously frontal lobe epilepsy) is a rare seizure disorder commonly misdiagnosed or unrecognized, causing negative patient sequelae. While usually reported in familial studies, it is more commonly acquired. Diagnosis is a challenge due to its low incidence in comparison with the more common sleep disorders or psychogenic etiologies in the differential diagnosis. Diagnosis is scaled on degree of certainty based on described or clinically documented semiology, with video EEG as a helpful, but not necessary, adjunct. Current treatment is similar to other focal epilepsies. We studied a 36-year-old active duty male soldier who presented with 2 years of predominantly sleep related, abrupt, short, and anamnestic hyperkinetic movements with unstructured vocalizations. Prior workup included non-contributory video electroencephalograph (EEG) and polysomnography as well as normal brain magnetic resonance imaging (MRI). Treatments for presumed psychiatric and parasomnia disturbances were not effective in establishing diagnosis or relief. Evaluation at our tertiary, multidisciplinary care institution recorded events consistent with the diagnosis of clinical SHE. He was enrolled in an advanced multishell diffusion-weighted imaging MRI research study to evaluate white matter tracts, given his history of mild, repetitive, non-penetrating traumatic brain injury, not otherwise requiring hospitalization. Multishell diffusion MRI tractography found changes not previously described in the right frontal lobe white matter tracts. These changes were consistent with neurological localization and serve as a potential nidus for this patient’s seizure disorder. Misdiagnosis of SHE can result in detrimental biopsychosocial sequelae of untreated epilepsy, unnecessary or harmful intervention, or the stigmata of a behavioral disorder. Further investigation into diagnosis and etiology of acquired SHE is needed. Assessment for white matter abnormalities can potentially provide information into pathogenesis of epilepsy disorders.

[An effect of epilepsy on sleep structure]

In this review, the authors discuss the problem of management of the patient with epilepsy with regard an impact of epileptic seizures and syndromes on sleep structure. Sleep disorders can lead to worsening of seizure control and general medical condition of the patients. However, seizures themselves and types of epilepsy can affect the sleep architecture. One of the most frequent symptoms of disordered sleep in epilepsy patients is excessive daytime sleepiness, which can be a consequence of poor sleep quality due to frequent nocturnal seizures or excessive interictal epileptiform activity during sleep leading to frequent arousals. is frequently referred to antiepileptic drug side effects. In patients with frontal and temporal lobe epilepsies, which are more associated with sleep, the sleep architecture is significantly different from healthy individuals. They have more frequent arousals, disturbed relationship between sleep phases and stages, in particular inhibited REM-sleep phase. Most recent data on this problem are reviewed. Sleep patterns for different major epileptic syndromes are presented and some general recommendations for patient management, with a special consideration of sleep issues, are given.

Epileptic interictal discharges are more frequent during NREM slow wave downstates

Epileptiform activity in various but not all epilepsy and recording types and cerebral areas is more frequent in NREM sleep, and especially during sleep periods with high-amplitude EEG slow waves. Slow waves synchronize high-frequency oscillations: physiological activity from the theta through the gamma band usually appears during scalp-positive upstates while epileptiform activity occurs at transitory phases and the scalp-negative downstate. It has been proposed that interictal discharges (IIDs) are facilitated by the high degree of neuronal firing synchrony during slow wave transitory and downstates. This would suggest that their occurrence increases as a function of slow wave synchronization, indicated by greater amplitude, steeper slopes and higher EEG signal synchronization. We investigated the occurrence of IIDs during NREM sleep slow waves in epileptic patients undergoing presurgical electrophysiological monitoring. Intracranially registered IIDs preferentially occurred during the scalp-negative downstates of frontal scalp slow waves in all subjects. IID occurrence was more frequent during larger slow waves in the pooled sample and a subset of subjects. However, slow wave slope steepness and EEG signal synchronization between two frontal scalp channels was not significantly associated with IID occurrence. Our results indicate that IIDs indeed do not occur at the same slow wave phase as physiological rhythms, but contrary to previous hypotheses their occurrence is not strongly affected by EEG synchronization.

Subjective sleep disturbance in Chinese adults with epilepsy: Associations with affective symptoms

BACKGROUND

As well as being a very common neurological disease worldwide, epilepsy significantly impairs patients' emotional, behavioral, and cognitive functioning. Sleep disturbances are the most frequent complaint in patients with epilepsy. The present study assesses the impact of a range of affective symptoms on subjective sleep quality and sleep disturbances in Chinese adults with epilepsy.

METHODS

Adults with epilepsy who visited our epilepsy clinic from July 2015 to March 2016 were enrolled in our study. Both patients and healthy controls completed the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and Mini-mental State Examination (MMSE). Subjective sleep quality and sleep disturbances were examined with regard to self-reported symptoms of depression and anxiety, seizure-related factors, and demographic factors.

RESULTS

The PSQI scores and ISI scores of patients were significantly higher (indicating lower quality sleep and more serious insomnia) than those of the control group. Symptoms associated with depression and anxiety were independently related to impaired subjective sleep quality and insomnia. Affective symptoms explained more of the variance in PSQI scores and ISI scores than did seizure-related or demographic variables. In addition, these variables also seemed to be less powerful contributing factors to subjective sleep quality and insomnia than affective symptoms, several seizure-related factors, such as seizure control, partial seizures and duration of epilepsy, which are also significantly associated with subjective sleep quality and insomnia. In addition, use of lamotrigine (LTG) was also associated with insomnia and use of clonazepam (CZP) and phenobarbital (PB) with daytime sleepiness in patients with epilepsy.

CONCLUSION

Chinese adults with epilepsy have poorer self-reported subjective sleep quality and a higher prevalence of insomnia than the control group. Depressive- and anxiety-related symptoms independently exert an adverse effect on the subjective sleep quality and insomnia of patients. In addition, seizure control, partial seizures, and the duration of epilepsy affect the quality of sleep and insomnia in patients, but seem less powerful predictors of sleep quality and insomnia than affective symptoms. Early identification and treatment of affective symptoms is of great importance in improving the sleep quality and insomnia of patients with epilepsy.

Definition and diagnostic criteria of sleep-related hypermotor epilepsy

The syndrome known as nocturnal frontal lobe epilepsy is recognized worldwide and has been studied in a wide range of clinical and scientific settings (epilepsy, sleep medicine, neurosurgery, pediatric neurology, epidemiology, genetics). Though uncommon, it is of considerable interest to practicing neurologists because of complexity in differential diagnosis from more common, benign sleep disorders such as parasomnias, or other disorders like psychogenic nonepileptic seizures. Moreover, misdiagnosis can have substantial adverse consequences on patients' lives. At present, there is no consensus definition of this disorder and disagreement persists about its core electroclinical features and the spectrum of etiologies involved. To improve the definition of the disorder and establish diagnostic criteria with levels of certainty, a consensus conference using formal recommended methodology was held in Bologna in September 2014. It was recommended that the name be changed to sleep-related hypermotor epilepsy (SHE), reflecting evidence that the attacks are associated with sleep rather than time of day, the seizures may arise from extrafrontal sites, and the motor aspects of the seizures are characteristic. The etiology may be genetic or due to structural pathology, but in most cases remains unknown. Diagnostic criteria were developed with 3 levels of certainty: witnessed (possible) SHE, video-documented (clinical) SHE, and video-EEG-documented (confirmed) SHE. The main research gaps involve epidemiology, pathophysiology, treatment, and prognosis.

Are Absence Epilepsy and Nocturnal Frontal Lobe Epilepsy System Epilepsies of the Sleep/Wake System?

System epilepsy is an emerging concept interpreting major nonlesional epilepsies as epileptic dysfunctions of physiological systems. I extend here the concept of reflex epilepsy to epilepsies linked to input dependent physiological systems. Experimental and clinical reseach data were collected to create a coherent explanation of underlying pathomechanism in AE and NFLE. We propose that AE should be interpreted as epilepsy linked to the corticothalamic burst-firing mode of NREM sleep, released by evoked vigilance level oscillations characterized by reactive slow wave response. In the genetic variation of NFLE the ascending cholinergic arousal system plays an essential role being in strong relationship with a gain mutation of the nicotinic acethylcholin receptors, rendering the arousal system hyperexcitable. I try to provide a more unitary interpretation for the variable seizure manifestation integrating them as different degree of pathological arosuals and alarm reactions. As a supporting hypothesis the similarity between arousal parasomnias and FNLE is shown, underpinned by overlaping pathomechanism and shared familiarity, but without epileptic features. Lastly we propose that both AE and NFLE are system epilepsies of the sleep-wake system representing epileptic disorders of the antagonistic sleep/arousal network. This interpretation may throw new light on the pathomechanism of AE and NFLE.

Depressive symptoms predict the quality of sleep in patients with partial epilepsy--A combined retrospective and prospective study

INTRODUCTION

Epilepsy is one of the most common neurological diseases and has many detrimental effects on the patients' well-being as well as sleep quality. The aim of this study was to assess the subjective quality of sleep and influencing factors on subjective sleep quality in patients with partial epilepsy using a combined retrospective and prospective study design.

METHODS

We conducted a combined retrospective and prospective study in patients with partial epilepsy and analyzed subjective ratings of sleep quality in 32 patients (17 female, 15 male; mean age: 40.41 ± 12.67 years, range: 20-64) with partial epilepsy (mean duration of epilepsy diagnosis: 18.31 ± 13.26 years) and 32 healthy gender-matched and age-matched controls. All patients filled out a seizure diary for 90 days, which included the number, duration, and type (partial vs. secondary generalized) of epileptic seizures and intake of antiepileptic and sleep medications. At baseline, all participants completed the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Beck's Depression Inventory (BDI), and Beck Anxiety Inventory (BAI). Poor sleepers were defined by a PSQI score of ≥ 5.

RESULTS

Twenty-three patients (72%) reported 15.17 ± 25.54 seizures in the previous three months, and nine (28%) patients reported being seizure-free. During the 90-day diary period, twenty-two patients (69%) documented a total of 319 epileptic seizures, while ten patients (31%) reported that they were seizure-free. The mean PSQI score of all patients was 4.88 ± 2.92 (range: 1-14) and the mean ESS score was 5.25 ± 2.98 (range: 0-10). The mean PSQI score of the control group was 3.25 ± 1.57 (range: 1-6), and their mean ESS score was 6.72 ± 3.48 (range: 0-14). The comparison of the two groups showed a significantly higher PSQI score in the patient group (t = 2.778, p = 0.008), but no statistically significant difference regarding their ESS score (t = -1.811, p = 0.075). Sixteen (50%) patients were poor sleepers. Good sleepers showed a significantly lower PSQI (2.69 ± 1.08 vs. 7.06 ± 2.49; p < 0.001) and BDI scores (2.38 ± 2.50 vs. 9.63 ± 7.63; p < 0.002) than poor sleepers. Linear regression analysis showed that the BDI score was the significant predictor for the PSQI score (estimate: 0.2019; p = 0.00819) and for the ESS score (estimate: 0.2251; p = 0.0321).

CONCLUSION

In patients with partial epilepsy, a higher depression score was the best predictor for a poor subjective sleep quality and increased daytime sleepiness.