Slow Sleep Research Articles

The Effectiveness and Safety of Hormonal Combinations of Antiepileptic Drugs in the Treatment of Epileptic Electrical Continuity in Children during Sleep: A Meta-Analysis.

Objective A systematic evaluation of the efficacy of hormones in combination with antiepileptic drugs (AEDs) compared to AEDs alone in the treatment of children with encephalopathy related to status epilepticus during slow sleep (ESES). This study provides an evidence-based approach to the treatment of children with ESES. Materials and Methods To find all relevant studies published before March 2022, we searched PubMed, Embase, Web of Science, Clinical Trials, Cochrane Library, CNKI, and Wanfang databases. We explore the difference between AEDs combined with hormones and AEDs alone for ESES treatment. All outcome data, including Wechsler Intelligence Scale for Children, the effective rate, EEG discharges, and adverse effects rate (AER), were compared using Review Manager 5.3. Results There were 805 patients in this study's seven investigations, including 403 in the experimental group and 402 in the control group. Meta-analysis showed that after treatment, compared with the AEDs alone group, the hormone combined with AEDs. The difference in clinical improvement rate [RR = 1.25, 95% CI (1.15, 1.36), p < 0.00001], electroencephalographic (EEG) discharge improvement rate [RR = 1.31, 95% CI (1.22, 1.41), p < 0.00001], and cognitive intelligence score [SMD = 1.02, 95% CI (0.76, 1.28), p < 0.00001] was statistically significant. The differences were statistically significant in terms of 0.00001; the incidence of adverse reactions was higher in the hormone combined with AEDs group than in the AEDs group alone, and the differences were statistically significant [RR = 4.13, 95% CI (1.06, 16.13), p < 0.01], and all adverse reactions improved or disappeared after discontinuation of the drug. Conclusions The combination of hormones with AEDs for the treatment of epileptic electrical continuity in sleep has advantages over AEDs alone in terms of controlling seizures, improving EEG abnormalities, and improving cognition. The combination of hormones with AEDs has advantages over AEDs alone in controlling seizures, improving EEG abnormalities, and improving cognition and is relatively safe.

Sleep Disorders and Dual Disorders

IntroductionWhile it is well known that there is an interaction between sleep disorders and substance abuse, it is certainly more complex than was previously thought. The effects on sleep depend on the substance used, but it has been shown that both during use and in withdrawal periods consumers have various sleep problems, and basically more fragmented sleep. We know that sleep problems must be taken into account to prevent addiction relapses.ObjectivesTo explain the different sleep disorders caused by substances such as alcohol and cannabisMethodsAs an example of this, two cases are introduced: the first one, a 17-year-old boy, who is diagnosed with ADHD with daily cannabis use since the age of 14. As a result of reducing consumption, he presents an episode of sleep paralysis that he had not previously had. The second one is a 50-year-old man diagnosed with a personality disorder and with dependence on cannabis and alcohol for years. He currently has abstinence from alcohol for months and maintains daily cannabis use. However, he has long-standing sleep pattern disturbances and frequent depersonalization phenomena at night.ResultsAlcohol at low doses has no clear effects on sleep architecture. At higher doses it decreases sleep latency, as well as awakenings. In chronic alcoholic patients, a decrease in deep slow sleep, and more fragmented sleep have been found. Cannabis withdrawal reduces sleep quality, increases latency, and produces strange dreams.ConclusionsThere is a positive relationship both between having a substance use disorder and suffering from a sleep disorder.DisclosureNo significant relationships.

0235 A Systematic Review of Sleep Health and Outpatient Opioid Use Disorder Treatment in Adults

Abstract Introduction Opioid Use Disorder (OUD) affects 2 million people in the United States and poor sleep health (satisfaction, alertness, timing, efficiency, and duration) is a primary driver of medication-assisted treatment (MAT) failure and relapse. It is known that people in therapy for OUD have a high prevalence of sleep problems (&amp;gt;75%) and poorer sleep health compared to people without OUD (e.g., lower sleep efficiency, shorter duration, and more awakenings). However, sleep health is not routinely assessed. Thus, in this systematic review, we examined original studies on sleep health within the context of adults receiving outpatient MAT for OUD. Methods We conducted a systematic review of original research on sleep health in adults receiving outpatient treatment for OUD. Multiple electronic databases (PubMed, PsycINFO, and CINAHL) were searched for relevant studies published in English from the establishment of each database to September 14, 2021. Quality was assessed with the Mixed Methods Appraisal Tool (v. 2001). Results Sixty two studies were selected including 17,913 adults with OUD and 604 comparison participants without OUD (mean age = 37.4 ± 6.6 years; 54.1% male). Sixty-one studies were quantitative (50 cross sectional, 6 longitunal, 5 interventional) and 1 was mixed methods. Participants with OUD had poorer satisfaction (Pittsburgh Sleep Quality Index mean 7.4 ± 2.2 v. 4.7 ± 2.3), shorter polysomnography (PSG) measured total sleep time (336.6 ± 41.4 mins (5.6 h) v. 411.8 ± 33.3 mins (6.8 h), spent less time in PSG measured slow wave N3 sleep (7.2 ± 5.8% v. 13.4 ± 6.4%), and had a lower percentage of PSG measured rapid eye movement sleep (14.6 ± 4.6% v. 21.7 ± 4.0%) than comparison participants without OUD. Conclusion Studies were predominantly observational ranging from a period of 1-2 nights to 2 years with participants at various points in treatment. More work is needed to understand the multidimensional depth of sleep health in adults with OUD. Optimizing sleep health in adults with OUD may improve their addiction trajectory and should be a priority in practice and research. Support (If Any) American Academy of Sleep Medicine (220-BS-19).

Nine-day continuous recording of EEG and 2-hour of high-density EEG under chronic sleep restriction in mice

This work provides an EEG dataset collected from nine mice during the sleep deprivation (SD) paradigm for the sleep science community. It includes 9-day of continuous recording of the frontal and parietal EEG, accelerometer, and 2-hour of high-density EEG (HD-EEG) under SD and SD-free conditions. Eighteen hours of SD were conducted on 5 consecutive days. The HD-EEG data were saved in the EEGLAB format and stored as the brain imaging data structure (BIDS). These datasets can be used to (i) compare mouse HD-EEG to human HD-EEG, (ii) track oscillatory activities of the sleep EEG (e.g., slow waves, spindles) across the cortical regions under different conditions of sleep pressure, and (iii) investigate the cortical traveling waves in the mouse brain. We also provided Python code for basic analyses of this dataset, including the detection of slow waves and sleep spindles. We hope that our dataset will reveal hidden activities during sleep and lead to a better understanding of the functions and mechanisms of sleep.

Brain states in freely behaving marmosets.

Study ObjectivesWe evaluated common marmosets as a perspective animal model to study human sleep and wake states.MethodsUsing wireless neurologger recordings, we performed longitudinal multichannel local field potential (LFP) cortical, hippocampal, neck muscle, and video recordings in three freely behaving marmosets. The brain states were formally identified using self-organizing maps.ResultsMarmosets were generally awake during the day with occasional 1–2 naps, and they slept during the night. Major electrographic patterns fall in five clearly distinguished categories: wakefulness, drowsiness, light and deep NREM sleep, and REM. Marmosets typically had 14–16 sleep cycles per night, with either gradually increasing or relatively low, but stable delta power within the cycle. Overall, the delta power decreased throughout the night sleep. Marmosets demonstrated prominent high amplitude somatosensory mu-rhythm (10–15 Hz), accompanied with neocortical ripples, and alternated with occipital alpha rhythm (10–15 Hz). NREM sleep was characterized with the presence of high amplitude slow waves, sleep spindles and ripples in neocortex, and sharp-wave-ripple complexes in CA1. Light and deep stages differed in levels of delta and sigma power and muscle tone. REM sleep was defined with low muscle tone and activated LFP with predominant beta-activity and rare spindle-like or mu-like events.ConclusionsMultiple features of sleep–wake state distribution and electrographic patterns associated with behavioral states in marmosets closely match human states, although marmoset have shorter sleep cycles. This demonstrates that marmosets represent an excellent model to study origin of human electrographical rhythms and brain states.

Cross-Frequency Slow Oscillation-Spindle Coupling in a Biophysically Realistic Thalamocortical Neural Mass Model.

Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in memory formation. Here, we analyze a neural mass model of the thalamocortical loop in which the cortical node can generate slow oscillations (approximately 1 Hz) while its thalamic component can generate fast sleep spindles of σ-band activity (12–15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic node's potassium leak and hyperpolarization-activated cation-nonselective currents, and for different parameter regimes of the cortical node. The latter are listed as follows: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced σ-band activity in the cortical node, however, is typically the strongest during the UP state, which follows a DOWN state “window of opportunity” for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation.

The adolescent pattern of sleep spindle development revealed by HD-EEG.

Sleep spindles are developmentally relevant cortical oscillatory patterns; however, they have mostly been studied by considering the entire spindle frequency range (11-15 Hz) without a distinction between the functionally and topographically different slow and fast spindles, using relatively few electrodes and analysing wide age-ranges. Here, we employ high-density night sleep electroencephalography in three age-groups between 12 and 20 years of age (30 females and 30 males) and analyse the adolescent developmental pattern of the four major parameters of slow and fast sleep spindles. Most of our findings corroborate those very few previous studies that also make a distinction between slow and fast spindles in their developmental analysis. We find spindle frequency increasing with age. A spindle density change is not obvious in our study. We confirm the declining tendencies for amplitude and duration, although within narrower, more specific age-windows than previously determined. Spindle frequency seems to be higher in females in the oldest age-group. Based on the pattern of our findings, we suggest that high-density electroencephalography, specifically targeting slow and fast spindle ranges and relatively narrow age-ranges would advance the understanding of both adolescent cortical maturation and development and the functional relevance of sleep spindles in general.

The role of sleep for episodic memory consolidation: Stabilizing or rescuing?

Prior studies suggest a role for sleep in memory consolidation, with specific contributions from slow oscillations and sleep spindles (Rasch & Born, 2013). However, recent studies failed to replicate sleep’s superiority over wake in strengthening memory against interference (Cordi & Rasch, 2021). The goal of the current study is to investigate whether sleep protects newly formed memory from unspecific interference induced by daytime experiences over 24 h, as well as to elucidate the sleep features that are involved. 56 healthy adults were randomly assigned to either the Sleep First or Wake First group. The Sleep First group encoded word pairs at night before sleep, while the Wake First group encoded word pairs in the morning before a day of wakefulness. Memory was tested 30 min, 12 h, and 24 h after encoding for both groups. The Sleep First group performed significantly better 12 h after encoding, replicating prior findings that memory is better after a period of sleep compared to wake. However, after 24 h, the two groups performed similarly. The Wake First group showed a positive correlation between overnight memory improvement and the theta and delta band power during slow wave sleep, an effect not found in the Sleep First group. These correlations suggest the possibility that after a day of waking interference, the brain recruits extra sleep resources to rescue memories from further forgetting. Our results are not consistent with prior studies showing a significant role for sleep in stabilizing memory from future interference, but they may suggest that sleep rescues memories after interference has occurred.

P 87 Slow wave sleep of a 90-minute nap relates to hippocampus-dependent spatial memory consolidation

Introduction : The hippocampus is critical for learning and consolidation of behaviourally-relevant spatial information. During spatial learning, spatially-related firing patterns of hippocampal place cells reflecting the subjects‘ location can be detected. It is widely acknowledged that the stabilization of newly encoded memory traces during active system consolidation is promoted by post-learning slow wave sleep (SWS). Here, hippocampal neural sequences are reactivated (“replay”) accompanied by neocortical slow oscillations thereby synchronizing spindle-ripple events originating from hippocampal and thalamic regions. Studies in rodents and humans reported a facilitation of spatial memories after full nights of sleep, in particular in early sleep SWS (i.e., sharp-wave ripples and slow waves). In this ongoing study, we investigated the effect of a 90-min nap on sleep-associated memory consolidation of spatial information. The advantage of an isolated nap condition is the possibility to selectively study the effect of SWS on memory consolidation. Patients & Methods: Forty healthy students between 21 and 32 years were divided into a sleep group (24±1,64 years, 55% female) and a wake group (24±2,68 years, 55% female). For testing spatial learning, the Virtual Water Maze (VWM), a digitalised version of the established Morris Water Maze Test for hippocampus-dependent place learning was used. Here, students had to find a treasure box hidden in the same position on a virtual island. Between learning and the recall sessions of the VWM (90 minutes), students either performed a mid-day nap accompanied by polysomnography (sleep condition) or watched emotionally neutral documentaries (wake condition). The improvement in the recall session was then compared between the groups and the influence of EEG sleep measures was analysed. Results: We found a significant positive correlation between spatial memory consolidation and time spent in SWS (SWS [min]: r=0,612, p=0,004). Accordingly, there was a tendency of a stronger consolidation when amounts of slow oscillations and sleep spindles were higher. When directly compared, there was no significant difference in the various consolidation measures of VWM between the groups . Conclusion: We found that consolidation of hippocampus-dependent spatial memory in a young healthy population was related to longer time spent in SWS as well as the amount of sleep spindles and slow oscillations during NonREM sleep of a 90-minute nap. Our data support the notion that the efficacy of hippocampus-dependent spatial memory consolidation depends on the duration of SWS.

Literature Cases Summarized Based on Their Polysomnographic Findings in Rett Syndrome.

Rett syndrome (RTT) is a severe and rare neurodevelopmental disorder affecting mostly girls. In RTT, an impaired sleep pattern is a supportive criterion for the diagnosis, yet little is known regarding the sleep structure and sleep respiratory events. Aiming to delineate sleep by aggregating RTT case (series) data from published polysomnographic studies, seventy-four RTT cases were collected from eleven studies up until 6 February 2022 (PROSPERO: CRD 42020198099). We compared the polysomnographic data within RTT stratifications and to a typically developing population. MECP2 cases demonstrated shortened total sleep time (TST) with increased stage N3 and decreased REM sleep. In cases with CDKL5 mutations, TST was longer and they spent more time in stage N1 but less in stage N3 than those cases affected by MECP2 mutations and a typically developing population. Sleep-disordered breathing was confirmed by the abnormal apnea/hypopnea index of 11.92 ± 23.67/h TST in these aggregated cases. No association of sleep structure with chronological age was found. In RTT, the sleep macrostructure of MECP2 versus CDKL5 cases showed differences, particularly regarding sleep stage N3. A severe REM sleep propensity reduction was found. Aberrant sleep cycling, possibly characterized by a poor REM ‘on switch’ and preponderance in slow and high-voltage sleep, is proposed.

Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults.

Previously, we demonstrated that precise temporal coordination between slow oscillations (SOs) and sleep spindles indexes declarative memory network development (Hahn et al., 2020). However, it is unclear whether these findings in the declarative memory domain also apply in the motor memory domain. Here, we compared adolescents and adults learning juggling, a real-life gross-motor task. Juggling performance was impacted by sleep and time of day effects. Critically, we found that improved task proficiency after sleep lead to an attenuation of the learning curve, suggesting a dynamic juggling learning process. We employed individualized cross-frequency coupling analyses to reduce inter- and intragroup variability of oscillatory features. Advancing our previous findings, we identified a more precise SO-spindle coupling in adults compared to adolescents. Importantly, coupling precision over motor areas predicted overnight changes in task proficiency and learning curve, indicating that SO-spindle coupling relates to the dynamic motor learning process. Our results provide first evidence that regionally specific, precisely coupled sleep oscillations support gross-motor learning.

HEURISTIC AUTODIALOG IN A LUCID DREAM

The article discusses the mechanism of solving creative tasks in a dream. The concepts of heuristic autodialog and lucid dreaming are formulated, the structure of the first is described and the possibilities of its implementation in the process of the second are shown. The role of the phases of fast and slow sleep in solving the problem by the method of heuristic autodialog in lucid dreaming is considered.

SQNN: A Spike-wave index Quantification Neural Network with a pre-labeling algorithm for epileptiform activity identification and quantification in children.

Electrical status epilepticus during slow sleep (ESES) is a phenomenon identified by strong activation of epileptiform activity in the electroencephalogram (EEG) during sleep. For children disturbed by ESES, spike-wave index (SWI) is defined to quantify the epileptiform activity in the EEG during sleep. Accurate SWI quantification is important for clinical diagnosis and prognosis. To quantify SWI automatically, a deep learning method is proposed in this paper. Firstly, a pre-labeling algorithm (PreLA) composed of the adaptive wavelet enhanced decomposition and a slow-wave discrimination rule is designed to efficiently label the EEG signal. It enables the collection of large-scale EEG dataset with fine-grained labels. Then, an SWI Quantification Neural Network (SQNN) is constructed to accurately classify each sample point as normal or abnormal and to identify the abnormal events. SWI can be calculated automatically based on the total duration of abnormalities and the length of the signal. Experiments on two datasets demonstrate that the PreLA is effective and robust for labeling the EEG data and the SQNN accurately and reliably quantifies SWI without using any thresholds. The average estimation error of SWI is 3.12%, indicating that our method is more accurate and robust than experts and previous related works. The processing speed of SQNN is 100 times faster than that of experts. Deep learning provides a novel approach to automatic SWI quantification and PreLA provides an easy way to label the EEG data with ESES syndromes. The results of the experiments indicate that the proposed method has a high potential for clinical diagnosis and prognosis of epilepsy in children.

Cranial Electrotherapy Stimulation to Improve the Physiology and Psychology Response, Response-Ability, and Sleep Efficiency in Athletes with Poor Sleep Quality.

Athletes often have poor sleep quality before a competition. Sleep quality can stabilize mood and improve sports performance. The randomized controlled study explored the effects of cranial electrotherapy stimulation (CES) on the physiology, psychology, response-ability, and sleep quality of athletes who had poor sleep quality before a competition. Athletes who had poor sleep quality (Pittsburgh Sleep Quality Scale score > 5) and had a competition in less than 2 months were recruited. The athletes were grouped into the CES group, which received a 2-week CES treatment (n = 20, age = 21.55 ± 2.26 years), and a placebo group (n = 20, age = 21.05 ± 1.46 years), which received a 2-week sham CES treatment. We performed biochemical analysis, a simple reaction time test, choice reaction time tests, the Profile of Mood States, heart rate variability (HRV), and an Actigraphy activity recorder to measure outcomes before and after the interventions. Our results revealed no significant differences in blood urea nitrogen, creatine phosphate, testosterone, cortisol, and saliva pH between and within groups (p > 0.05). Significant decreases in negative mood states (i.e., anger, tension, and depression) and choice reaction time in the CES group were noted (p < 0.05), moreover, the anger, tension, and depression mood decreased from 0.36 ± 0.45 (95% CI = 0.16–0.55), 1.62 ± 0.97 (95% CI = 1.19–2.04), and 1.67 ± 1.06 (95% CI = 1.20–2.13) to 0.11 ± 0.20 (95% CI = 0.02–0.19, p = 0.03), 1.12 ± 0.74 (95% CI = 0.79–1.44, p = 0.04), and 0.81 ± 0.75 (95% CI = 0.48–1.13, p = 0.001), respectively. Additionally, choice reaction time was decreased from 420.85 ± 41.22 ms (95% CI = 402.78–438.91) to 399.90 ± 36.71 ms (95% CI = 383.81–415.98, p = 0.04) and was also noted in the CES group. For HRV, and Actigraphy activity for sleep measure, the low-frequency (LF)/high-frequency (HF) ratios changed from 1.80 ± 1.39 (95% CI = 1.19–2.40) to 1.21 ± 0.73 (95% CI = 0.89–1.53, p = 0.10), and sleep efficiency decreased from 87.94 ± 6.76% (95% CI = 84.97–90.90) to 81.75 ± 9.62% (95% CI = 77.53–85.96, p = 0.02) in the CES group. The change in LF/HF after the trial were found between CES and placebo groups (p < 0.05). Yet, the decrease in sleep efficiency in the placebo group were noted (p < 0.05). However, we found that the regression line for sleep efficiency was decreased less during the study while using CES. The CES intervention could reduce negative emotions, improve choice reaction times, enhance the parasympathetic and sympathetic nerve activity imbalances, and slow sleep efficiency deterioration. Regardless, small effect sizes of the application of CES on psychology response, response-ability, and sleep efficiency were concluded in athletes with poor sleep quality before a competition.

SQNN: a spike-wave index quantification neural network with a pre-labeling algorithm for epileptiform activity identification and quantification in children

Objective. Electrical status epilepticus during slow sleep (ESES) is a phenomenon identified by strong activation of epileptiform activity in the electroencephalogram (EEG) during sleep. For children disturbed by ESES, spike-wave index (SWI) is defined to quantify the epileptiform activity in the EEG during sleep. Accurate SWI quantification is important for clinical diagnosis and prognosis. To quantify SWI automatically, a deep learning method is proposed in this paper. Approach. Firstly, a pre-labeling algorithm (PreLA) composed of the adaptive wavelet enhanced decomposition and a slow-wave discrimination rule is designed to efficiently label the EEG signal. It enables the collection of large-scale EEG dataset with fine-grained labels. Then, an SWI quantification neural network (SQNN) is constructed to accurately classify each sample point as normal or abnormal and to identify the abnormal events. SWI can be calculated automatically based on the total duration of abnormalities and the length of the signal. Main results. Experiments on two datasets demonstrate that the PreLA is effective and robust for labeling the EEG data and the SQNN accurately and reliably quantifies SWI without using any thresholds. The average estimation error of SWI is 3.12%, indicating that our method is more accurate and robust than experts and previous related works. The processing speed of SQNN is 100 times faster than that of experts. Significance. Deep learning provides a novel approach to automatic SWI quantification and PreLA provides an easy way to label the EEG data with ESES syndromes. The results of the experiments indicate that the proposed method has a high potential for clinical diagnosis and prognosis of epilepsy in children.

Continuous spikes and waves during slow sleep related to sulthiame?

Continuous spikes and waves during slow sleep related to sulthiame?

What is the effect of pharmacological treatment for continuous spike-wave during slow wave sleep syndrome and Landau-Kleffner syndrome? A Cochrane Review summary with commentary.

What is the effect of pharmacological treatment for continuous spike-wave during slow wave sleep syndrome and Landau-Kleffner syndrome? A Cochrane Review summary with commentary.

Sleep Biomarkers Help Predict the Development of Alzheimer Disease.

Middle-aged or older adults who self-report sleep-wake disorders are at an increased risk for incident dementia, mild cognitive impairment, and Alzheimer disease. Dementia in people with mild cognitive impairment and Alzheimer disease who complain of sleep-wake disorders progress faster than those without sleep-wake disorders. Removal of amyloid-beta and tau tangles occurs preferentially in non-rapid eye movement 3 sleep and fragmented or insufficient sleep may lead to accumulation of these neurotoxins even in preclinical stages. Selective atrophy in the medial temporal lobe on brain MRI has been shown to predict impaired coupling of slow oscillations and sleep spindles. Impaired slow wave-spindle coupling has been shown to correlate with impaired overnight memory consolidation. Whereas, a decrease in the amplitude of 0.6 to 1 Hz slow wave activity predicts higher cortical Aβ burden on amyloid PET scans. Overexpression of the wake-promoting neurotransmitter orexin may predispose patients with mild cognitive impairment and Alzheimer disease to increased wakefulness, decreasing time they need to clear from the brain the neurotoxic accumulation of amyloid-beta and especially tau. More research exploring these relationships is needed and continuing.

Panayiotopoulos syndrome: Unusual clinical manifestations

PurposeWe retrospectively analyzed the electroclinical features, treatment, and outcome of patients with Panayiotopoulos syndrome (PS) who presented with unusual clinical manifestations. MethodA retrospective, descriptive, multicenter study was conducted evaluating 44 patients with PS who had seizures with an unusual semiology. Data from patients with PS seen at eight Argentine centers between April 2000 and April 2019 were collected. ResultsTwelve patients (29.2%) had ictal syncope or syncope-like epileptic seizures. Three children (7.3%) had recurrent episodes of vomiting. Four patients (9.7%) presented with urinary incontinence associated with autonomic signs and consciousness impairment. One child had hiccups with autonomic manifestations followed by eye deviation. One boy had episodes of laughter with autonomic symptoms followed by loss of consciousness. Six patients (14.6%) had hyperthermia without acute febrile illness with autonomic symptoms as the first manifestation. Six others (14.6%) had focal motor seizures characterized by eye and head deviation in four and eyelid blinking in two. Four patients (9.7%) had ictal headache as the initial manifestation followed by nausea and vomiting. Two children (4.8%) had their first seizure while asleep associated with cardiorespiratory arrest. Two children (4.8%) had oral automatisms, such as sucking and chewing. In two children (4.8%) coughing was the initial manifestation followed by emetic symptoms. One patient (2.3%) had vertigo with a sensation of fear, with eye deviation and unresponsiveness. One child started with continuous spikes and waves during slow sleep, behavior disturbances, and emetic symptoms. ConclusionIn this study, evidence of the existence of unusual clinical cases of PS with typical EEG patterns was found. Outcome was excellent.

Epileptic encephalopathy with electrical status epilepticus during slow sleep: evaluation of treatment response from a tertiary center.

This study aimed to evaluate the clinical, electrophysiological, etiological features, and treatment response in children with epileptic encephalopathy with electrical status epilepticus during slow sleep (ESES). Clinical data, records of electroencephalograms (EEG), and brain magnetic resonance imaging (MRI) findings of 33 patients with ESES who were treated, and followed up for at least one year were retrospectively analyzed. Of all patients, 57.6% were male, and 42.4% were female. The mean age was 10.45 ± 2.88 years. At first admission, 90% of patients had seizures, and 10% had only school failure. Twelve patients had childhood focal epileptic syndrome. In etiology, asphyxia (n=6), hydrocephalus (n=2), polymicrogyria (n=1), and mesial temporal sclerosis (n=1) were determined. Neurological examination was abnormal in 27.2%, and brain MRI findings were pathological in 36.3% of the patients. During the ESES phase, the spike-wave index (SWI) on the non-rapid eye movement (NREM) sleep EEG was > 85% in 16 patients and 50-85% in 17 patients. Only one patient received one, and the others had at least two antiseizure medications. Benzodiazepines were found to be the most effective treatment. In the two-year follow-up, 24 patients (72.7%) were seizure-free, and nineteen patients (57.5%) had complete recovery of SWI on their NREM sleep EEG. There was a significant correlation with reduction of the SWI on the EEG and seizure control (p < 0.001). In addition, a significant correlation was found between neurocognitive and behavioral scores scored before and after treatment, seizure control, and EEG recovery. ESES is an epileptic encephalopathy that can be treated safely with antiseizure medications. Neurocognitive examinations and follow-up of EEG findings are valuable in terms of the treatment response. Benzodiazepines were found to be very effective in additional treatment.