Focal epilepsy disrupts spindle structure and function

ANPHY-Sleep: an Open Sleep Database from Healthy Adults Using High-Density Scalp Electroencephalogram

Well-documented sleep datasets from healthy adults are important for sleep pattern analysis and comparison with a wide range of neuropsychiatric disorders. Currently, available sleep datasets from healthy adults are acquired using low-density arrays with a minimum of four electrodes in a typical sleep montage. The low spatial resolution is thus prohibitive for the analysis of the spatial structure of sleep. Here we introduce an open-access sleep dataset from 29 healthy adults (13 female, aged 32.17 ± 6.30 years) acquired at the Montreal Neurological Institute. The dataset includes overnight polysomnograms with high-density scalp electroencephalograms incorporating 83 electrodes, electrocardiogram, electromyogram, electrooculogram, and an average of electrode positions using manual co-registrations and sleep scoring annotations. Data characteristics and group-level analysis of sleep properties were assessed. The database can be accessed through ( https://doi.org/10.17605/OSF.IO/R26FH ). This is the first high-density electroencephalogram open sleep database from healthy adults, allowing researchers to investigate sleep physiology at high spatial resolution. We expect that this database will serve as a valuable resource for studying sleep physiology and for benchmarking sleep pathology.

Pulsatile corticoid therapy reduces interictal epileptic activity burden in children with genetic drug-resistant epilepsy

OBJECTIVE

Corticosteroids and adrenocorticotropic hormone (ACTH) are the therapy of choice to treat infantile spasms. However, systematic studies about their use in other types of childhood epilepsies remain rare and ACTH can have serious side effects. This study compares the interictal epileptic activity (IEA) burden (% of electroencephalography (EEG) time with IEDs) in children with genetic drug-resistant epilepsy before and after a standardized treatment with pulsatile corticoid therapy (PCT).

METHODS

Children with drug-resistant epilepsy underwent a standardized protocol for PCT with cycles of high-dose dexamethasone (20 mg/m2 body surface) intravenously. Patients were hospitalized for 3 days per PCT cycle and EEGs were obtained before initiation of treatment (baseline) and during the hospitalization around the time of every second cycle. EEG recordings during sleep and wakefulness were obtained. IEA burden was compared before and after PCT. Secondary outcome measures included the sleep spindle rate, the seizure frequency and subjective evaluation in a standardized interview.

RESULTS

In the cohort of 24 children (10 female, 6.2 ± 3.4 years), IEA burden was lower in the EEG after PCT compared to the baseline (baseline: 5.4% [0.7-97.3] vs. after PCT: 1.5% [0-96.9], p = 0.001, d = -0.41). Sleep physiology expressed by sleep spindles improved after PCT with enhanced fast spindle rates (0.8/min [0-2.2] vs. 1.5/min [0.2-3.4], p = 0.045, d = 0.36). Seizure frequency was decreased in 17 of the 24 patients (70.8%) with one patient achieving seizure freedom. The majority of patients improved in quality of life (79.2%), and sleep (81.3%). No serious adverse effects were documented.

SIGNIFICANCE

This study systematically assessed the effect of PCT in children with genetic / suspected genetic drug-resistant epilepsy. PCT was found to not only reduce the IEA burden but also increase sleep spindle rates, which are important for cognitive functioning.

PLAIN LANGUAGE SUMMARY

In this study, children with a form of epilepsy, which is resistant against antiseizure medication, received a systematic treatment with corticosteroids over multiple cycles in the hospital. It was found that not only the epileptic activity was reduced but also the sleep of the patients was improved after the treatment. These findings could provide the basis for extending the use of corticosteroids in children with epilepsy.

Thalamic epileptic spikes disrupt sleep spindles in patients with epileptic encephalopathy

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep-activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, P ≈ 0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (P ≈ 0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 s after each thalamic spike (P < 0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

Impaired sleep-dependent memory consolidation predicted by reduced sleep spindles in Rolandic epilepsy

Background and Objectives

Sleep spindles are prominent thalamocortical brain oscillations during sleep that have been mechanistically linked to sleep-dependent memory consolidation in animal models and healthy controls. Sleep spindles are decreased in Rolandic epilepsy and related sleep-activated epileptic encephalopathies. We investigate the relationship between sleep spindle deficits and deficient sleep dependent memory consolidation in children with Rolandic epilepsy.

Methods

In this prospective case-control study, children were trained and tested on a validated probe of memory consolidation, the motor sequence task (MST). Sleep spindles were measured from high-density EEG during a 90-minute nap opportunity between MST training and testing using a validated automated detector.

Results

Twenty-three children with Rolandic epilepsy (14 with resolved disease), and 19 age- and sex-matched controls were enrolled. Children with active Rolandic epilepsy had decreased memory consolidation compared to control children (p=0.001, mean percentage reduction: 25.7%, 95% CI [10.3, 41.2]%) and compared to children with resolved Rolandic epilepsy (p=0.007, mean percentage reduction: 21.9%, 95% CI [6.2, 37.6]%). Children with active Rolandic epilepsy had decreased sleep spindle rates in the centrotemporal region compared to controls (p=0.008, mean decrease 2.5 spindles/min, 95% CI [0.7, 4.4] spindles/min). Spindle rate positively predicted sleep-dependent memory consolidation (p=0.004, mean MST improvement of 3.9%, 95% CI [1.3, 6.4]%, for each unit increase in spindles per minute).

Discussion

Children with Rolandic epilepsy have a sleep spindle deficit during the active period of disease which predicts deficits in sleep dependent memory consolidation. This finding provides a mechanism and noninvasive biomarker to aid diagnosis and therapeutic discovery for cognitive dysfunction in Rolandic epilepsy and related sleep activated epilepsy syndromes.

Automated sleep staging on reduced channels in children with epilepsy

Objectives

This study aimed to validate a sleep staging algorithm using in-hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE), and with drug-resistant epilepsy (DRE).

Methods

Overnight video-EEG, along with electrooculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG, and chin EMG.

Results

In 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE, and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77, and 0.73 respectively). Performance per sleep stage was assessed with an F1 score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3, and 0.86 for rapid eye movement (REM) sleep.

Conclusion

We concluded that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1, and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time, however, significantly affected in children with DRE, can be detected reliably by the algorithm.Clinical trial registration: ClinicalTrials.gov, identifier NCT04584385.

Neurofeedback and epilepsy: Renaissance of an old self-regulation method?

Neurofeedback is a brain-computer interface tool enabling the user to self-regulate their neuronal activity, and ultimately, induce long-term brain plasticity, making it an interesting instrument to cure brain disorders. Although this method has been used successfully in the past as an adjunctive therapy in drug-resistant epilepsy, this approach remains under-explored and deserves more rigorous scientific inquiry. In this review, we present early neurofeedback protocols employed in epilepsy and provide a critical overview of the main clinical studies. We also describe the potential neurophysiological mechanisms through which neurofeedback may produce its therapeutic effects. Finally, we discuss how to innovate and standardize future neurofeedback clinical trials in epilepsy based on evidence from recent research studies.

Scalp and hippocampal sleep correlates of memory function in drug-resistant temporal lobe epilepsy

Seminal animal studies demonstrated the role of sleep oscillations such as cortical slow waves, thalamocortical spindles, and hippocampal ripples in memory consolidation. In humans, whether ripples are involved in sleep-related memory processes is less clear. Here, we explored the interactions between sleep oscillations (measured as traits) and general episodic memory abilities in 26 adults with drug-resistant temporal lobe epilepsy who performed scalp-intracranial electroencephalographic recordings and neuropsychological testing, including two analogous hippocampal-dependent verbal and nonverbal memory tasks. We explored the relationships between hemispheric scalp (spindles, slow waves) and hippocampal physiological and pathological oscillations (spindles, slow waves, ripples, and epileptic spikes) and material-specific memory function. To differentiate physiological from pathological ripples, we used multiple unbiased data-driven clustering approaches. At the individual level, we found material-specific cerebral lateralization effects (left-verbal memory, right-nonverbal memory) for all scalp spindles (rs > 0.51, ps < 0.01) and fast spindles (rs > 0.61, ps < 0.002). Hippocampal epileptic spikes and short pathological ripples, but not physiological oscillations, were negatively (rs > -0.59, ps < 0.01) associated with verbal learning and retention scores, with left lateralizing and antero-posterior effects. However, data-driven clustering failed to separate the ripple events into defined clusters. Correlation analyses with the resulting clusters revealed no meaningful or significant associations with the memory scores. Our results corroborate the role of scalp spindles in memory processes in patients with drug-resistant temporal lobe epilepsy. Yet, physiological and pathological ripples were not separable when using data-driven clustering, and thus our findings do not provide support for a role of sleep ripples as trait-like characteristics of general memory abilities in epilepsy.

Temporal association between sleep spindles and ripples in the human anterior and mediodorsal thalamus

Sleep spindles are major oscillatory components of Non-Rapid Eye Movement (NREM) sleep, reflecting hyperpolarization-rebound sequences of thalamocortical neurons. Reports suggest a link between sleep spindles and several forms of high-frequency oscillations which are considered as expressions of pathological off-line neural plasticity in the central nervous system. Here we investigated the relationship between thalamic sleep spindles and ripples in the anterior and mediodorsal nuclei (ANT and MD) of epilepsy patients. Whole-night LFP from the ANT and MD were co-registered with scalp EEG/polysomnography by using externalized leads in 15 epilepsy patients undergoing a Deep Brain Stimulation protocol. Slow (~12 Hz) and fast (~14 Hz) sleep spindles were present in the human ANT and MD and roughly, 20% of them were associated with ripples. Ripple-associated thalamic sleep spindles were characterized by longer duration and exceeded pure spindles in terms of spindle power as indicated by time-frequency analysis. Furthermore, ripple amplitude was modulated by the phase of sleep spindles within both thalamic nuclei. No signs of pathological processes were correlated with measures of ripple and spindle association, furthermore, the density of ripple-associated sleep spindles in the ANT showed a positive correlation with verbal comprehension. Our findings indicate the involvement of the human thalamus in coalescent spindle-ripple oscillations of NREM sleep.

Can epilepsy affect normal EEG variants? A comparative study between subjects with and without epilepsy

OBJECTIVES

To compare the prevalence of benign EEG variants (BEVs) between epileptic and non-epileptic subjects.

METHODS

A prospective, observational EEG study of 1,163 consecutive patients, using the 10-20 international system with systematically two additional anterior/inferior temporal electrodes. The video-EEG monitoring duration was between 24 h and eight days.

RESULTS

We identified 917 (78.9%) epileptic patients (mean age: 33.42 ± 15.5 years; females: 53.4%) and 246 (21.2%) non-epileptic patients (mean age: 35.6 ± 18.75 years; females: 54.9%). Despite a shorter mean duration of the EEG recordings, the prevalence of BEVs was higher in non-epileptic vs. epileptic patients (73.2% vs. 57.8%, p = 0.000011). This statistical difference was confirmed for lambda waves (23.6% in the non-epilepsy group vs. 14.8% in the epilepsy group, p = 0.001), POSTs (50.8% vs. 32.5%, p < 0.000001), wicket spikes (20.3% vs. 13.6%, p = 0.009) in particular in NREM and REM sleep, and 14- and 6-Hz positive bursts (13% vs. 7.1% p = 0.003). Mu rhythm was observed at the same frequency in both groups (21.1% in the non-epilepsy group vs. 22.7% in the epilepsy group). There was no difference between the two groups for rarer rhythms, such as rhythmic mid-temporal theta burst of drowsiness, small sharp spikes, and midline theta rhythm.

CONCLUSIONS

There was no increase in any of the BEVs in the epilepsy group. On the contrary, BEVs were more frequent and diversified in the non-epilepsy group. Epilepsy may negatively affect the occurrence of the most common BEVs, with the exception of the mu rhythm, which is present in about one-fifth of the population with or without epilepsy.

A robust deep learning detector for sleep spindles and K-complexes: towards population norms

Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variability in both electroencephalography (EEG) signals and expert annotation rules. We introduce the Sleep EEG Event Detector (SEED), a deep learning system that outperforms existing approaches in SS and KC detection, reaching an F1-score of 80.5% and 83.7%, respectively, on the MASS2 dataset. SEED transfers well and requires minimal fine-tuning for new datasets and annotation styles. Remarkably, SEED substantially reduces the required amount of annotated data by using a novel pretraining approach that leverages the rule-based detector A7. An analysis of 11,224 subjects revealed that SEED's detections provide better estimates of SS population statistics than existing approaches. SEED is a powerful resource for obtaining sleep-event statistics that could be useful for establishing population norms.

Epilepsy Surgery for Cognitive Improvement in Epileptic Encephalopathy

Epileptic encephalopathies are defined by the presence of frequent epileptiform activity that causes neurodevelopmental slowing or regression. Here, we review evidence that epilepsy surgery improves neurodevelopment in children with epileptic encephalopathies. We describe an example patient with epileptic encephalopathy without drug refractory seizures, who underwent successful diagnostic and therapeutic surgeries. In patients with epileptic encephalopathy, cognitive improvement alone is a sufficient indication to recommend surgical intervention in experienced centers.

Sleep Spindle Generation Before and After Epilepsy Surgery: A Source Imaging Study in Children with Drug-Resistant Epilepsy

INTRODUCTION

Literature lacks studies investigating the cortical generation of sleep spindles in drug-resistant epilepsy (DRE) and how they evolve after resection of the epileptogenic zone (EZ). Here, we examined sleep EEGs of children with focal DRE who became seizure-free after focal epilepsy surgery, and aimed to investigate the changes in the spindle generation before and after the surgery using low-density scalp EEG and electrical source imaging (ESI).

METHODS

We analyzed N2-sleep EEGs from 19 children with DRE before and after surgery. We identified slow (8-12 Hz) and fast spindles (13-16 Hz), computed their spectral features and cortical generators through ESI and computed their distance from the EZ and irritative zone (IZ). We performed two-way ANOVA testing the effect of spindle type (slow vs. fast) and surgical phase (pre-surgery vs. post-surgery) on each feature.

RESULTS

Power, frequency and cortical activation of slow spindles increased after surgery (p < 0.005), while this was not seen for fast spindles. Before surgery, the cortical generators of slow spindles were closer to the EZ (57.3 vs. 66.2 mm, p = 0.007) and IZ (41.3 vs. 55.5 mm, p = 0.02) than fast spindle generators.

CONCLUSIONS

Our data indicate alterations in the EEG slow spindles after resective epilepsy surgery. Fast spindle generation on the contrary did not change after surgery. Although the study is limited by its retrospective nature, lack of healthy controls, and reduced cortical spatial sampling, our findings suggest a spatial relationship between the slow spindles and the epileptogenic generators.

Altered Sleep Microarchitecture and Cognitive Impairment in Patients With Temporal Lobe Epilepsy

BACKGROUND AND OBJECTIVES

To investigate the spatiotemporal characteristics of sleep waveforms in temporal lobe epilepsy (TLE) and examine their association with cognition.

METHODS

In this retrospective, cross-sectional study, we examined overnight EEG data from adult patients with TLE and nonepilepsy comparisons (NECs) admitted to the epilepsy monitoring unit at Mass General Brigham hospitals. Automated algorithms were used to characterize sleep macroarchitecture (sleep stages) and microarchitecture (spindles, slow oscillations [SOs]) on scalp EEG and to detect hippocampal interictal epileptiform discharges (hIEDs) from foramen ovale electrodes simultaneously recorded in a subset of patients with TLE. We examined the association of sleep features and hIEDs with memory and executive function from clinical neuropsychological evaluations.

RESULTS

A total of 81 adult patients with TLE and 28 NEC adult patients were included with similar mean ages. There were no significant differences in sleep macroarchitecture between groups, including relative time spent in each sleep stage, sleep efficiency, and sleep fragmentation. By contrast, the spatiotemporal characteristics of sleep microarchitecture were altered in TLE compared with NEC and were associated with cognitive impairments. Specifically, we observed a ∼30% reduction in spindle density in patients with TLE compared with NEC, which was significantly associated with worse memory performance. Spindle-SO coupling strength was also reduced in TLE and, in contrast to spindles, was associated with diminished executive function. We found no significant association between sleep macroarchitectural and microarchitectural parameters and hIEDs.

DISCUSSION

There is a fundamental alteration of sleep microarchitecture in TLE, characterized by a reduction in spindle density and spindle-SO coupling, and these changes may contribute to neurocognitive comorbidity in this disorder.

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.

Shorter duration of slow wave sleep is related to symptoms of depression in patients with epilepsy

Slow wave sleep duration and spectral abnormalities are related to both epilepsy and depression, but it is unclear how depressive symptoms in patients with epilepsy are affected by slow wave sleep duration and clinical factors, and how the spectral characteristics of slow wave sleep reflect a potential interaction of epilepsy and depression. Long-term video-EEG monitoring was conducted in 51 patients with focal epilepsy, 13 patients with generalized epilepsy, and 9 patients without epilepsy. Slow wave sleep segments were manually marked in the EEG and duration as well as EEG power spectra were extracted. Depressive symptoms were documented with the Beck Depression Inventory (BDI). At least mild depressive symptoms (BDI > 9) were found among 23 patients with focal epilepsy, 5 patients with generalised epilepsy, and 6 patients who had no epilepsy diagnosis. Slow wave sleep duration was shorter for patients with at least mild depressive symptoms (p =.004), independently from epilepsy diagnosis, antiseizure medication, age, and sex. Psychoactive medication was associated with longer slow wave sleep duration (p =.008). Frontal sigma band power (13-15 Hz) during slow wave sleep was higher for patients without epilepsy and without depressive symptoms as compared to patients without depressive symptoms but with focal epilepsy (p =.005). Depressive symptoms affect slow wave sleep duration of patients with epilepsy similarly as in patients without epilepsy. Since reduced slow wave sleep can increase the likelihood of seizure occurrence, these results stress the importance of adequate treatment for patients with epilepsy who experience depressive symptoms.

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.

Self-limited childhood epilepsies are disorders of the perisylvian communication system, carrying the risk of progress to epileptic encephalopathies-Critical review

"Sleep plasticity is a double-edged sword: a powerful machinery of neural build-up, with a risk to epileptic derailment." We aimed to review the types of self-limited focal epilepsies..."i.e. keep as two separate paragraphs" We aimed to review the types of self-limited focal epilepsies: (1) self-limited focal childhood epilepsy with centrotemporal spikes, (2) atypical Rolandic epilepsy, and (3) electrical status epilepticus in sleep with mental consequences, including Landau-Kleffner-type acquired aphasia, showing their spectral relationship and discussing the debated topics. Our endeavor is to support the system epilepsy concept in this group of epilepsies, using them as models for epileptogenesis in general. The spectral continuity of the involved conditions is evidenced by several features: language impairment, the overarching presence of centrotemporal spikes and ripples (with changing electromorphology across the spectrum), the essential timely and spatial independence of interictal epileptic discharges from seizures, NREM sleep relatedness, and the existence of the intermediate-severity "atypical" forms. These epilepsies might be the consequences of a genetically determined transitory developmental failure, reflected by widespread neuropsychological symptoms originating from the perisylvian network that have distinct time and space relations from secondary epilepsy itself. The involved epilepsies carry the risk of progression to severe, potentially irreversible encephalopathic forms.

Neuronal hyperexcitability in the Tg2576 mouse model of Alzheimer's disease - the influence of sleep and noradrenergic transmission

The link between Alzheimer's disease (AD) and network hypersynchrony - manifesting as epileptic activities - received considerable attention in the past decade. However, several questions remain unanswered as to its mechanistic underpinnings. Therefore, our objectives were (1) to better characterise epileptic events in the Tg2576 mouse model throughout the sleep-wake cycle and disease progression via electrophysiological recordings and (2) to explore the involvement of noradrenergic transmission in this pathological hypersynchrony. Over and above confirming the previously described early presence and predominance of epileptic events during rapid-eye-movement (REM) sleep, we also show that these events do not worsen with age and are highly phase-locked to the section of the theta cycle during REM sleep where hippocampal pyramidal cells reach their highest firing probability. Finally, we reveal an antiepileptic mechanism of noradrenergic transmission via α1-adrenoreceptors that could explain the intriguing distribution of epileptic events over the sleep-wake cycle in this model, with potential therapeutic implications in the treatment of the epileptic events occurring in many AD patients.

Childhood absence epilepsy patients with cognitive impairment have decreased sleep spindle density

OBJECTIVE

To explore the differences in sleep spindle (SS) characteristics during stage N2 sleep between children with childhood absence epilepsy and healthy controls, and between children with childhood absence epilepsy with or without cognitive impairment.

METHODS

We recruited 29 children (14 females, 15 males, mean age: 8 (2.5) years) with childhood absence epilepsy who did not undergone antiseizure treatments previously and 30 age-matched controls (14 females, 16 males, mean age: 9 (3.0) years). For all patients, data on medical history were collected. Each child was monitored overnight by long-term video electroencephalography and was evaluated by the Wechsler Intelligence Scale for Children-Fourth Edition. Next, we compared anterior SS characteristics, including density, frequency, cycle length, duration, amplitude, and percentage of sleep stages.

RESULTS

The childhood absence epilepsy group exhibited lower spindle density and duration in the first 37.5 min of stage N2 sleep than the control group (P < 0.01). A decrease in spindle density could be observed in the childhood absence epilepsy group with aggravated cognition impairment. The spindle density was substantially lower in the cognitively impaired group than in the cognitively unimpaired group (P < 0.01). No significant differences were observed in SS amplitude, SS frequency, SS cycle length, and the distribution of sleep stages.

CONCLUSIONS

Reduction in spindle density and duration is associated with the mechanisms underlying childhood absence epilepsy. The deficit in SS density is related with impaired cognition. This deficiency in SSs may be a useful predictive indicator of cognitive impairment in children with absence epilepsy, indicating that SSs may become a useful biomarker and potential adjuvant anti-seizure target for cognitive impairment caused by childhood absence epilepsy.

Focal epilepsy impacts rapid eye movement sleep microstructure

STUDY OBJECTIVES

Whereas there is plenty of evidence on the influence of epileptic activity on non-rapid eye movement (NREM) sleep macro- and micro-structure, data on the impact of epilepsy on rapid eye movement (REM) sleep remains sparse. Using high-density electroencephalography (HD-EEG), we assessed global and focal disturbances of sawtooth waves (STW) as cortically generated sleep oscillations of REM sleep in patients with focal epilepsy.

METHODS

Twenty-two patients with drug-resistant focal epilepsy (13 females; mean age, 32.6 ± 10.7 years; 12 temporal lobe epilepsy) and 12 healthy controls (3 females; 24.0 ± 3.2 years) underwent combined overnight HD-EEG and polysomnography. STW rate, duration, frequency, power, spatial extent, IED rates and sleep homeostatic properties were analyzed.

RESULTS

STW rate and duration were reduced in patients with focal epilepsy compared to healthy controls (rate: 0.64/min ± 0.46 vs. 1.12/min ± 0.41, p = .005, d = -0.98; duration: 3.60 s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01). Not surprisingly given the fronto-central maximum of STW, the reductions were driven by extratemporal lobe epilepsy patients (rate: 0.45/min ± 0.31 vs. 1.12/min ± 0.41, p = .0004, d = -1.35; duration: 3.49 s ± 0.92 vs. 4.57 ± 1.00, p = .017, d = -0.99) and were more pronounced in the first vs. the last sleep cycle (rate first cycle patients vs. controls: 0.60/min ± 0.49 vs. 1.10/min ± 0.55, p = .016, d = -0.90, rate last cycle patients vs. controls: 0.67/min ± 0.51 vs. 0.99/min ± 0.49, p = .11, d = -0.62; duration first cycle patients vs. controls: 3.60s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01, duration last cycle patients vs. controls: 3.66s ± 0.84 vs. 4.51 ± 1.26, p = .039, d = -0.80). There was no regional decrease of STWs in the region with the epileptic focus vs. the contralateral side (all p > .05).

CONCLUSION

Patients with focal epilepsy and in particular extratemporal lobe epilepsy show a global reduction of STW activity in REM sleep. This may suggest that epilepsy impacts cortically generated sleep oscillations even in REM sleep when epileptic activity is low.