Waking EEG Research Articles

Changes in awake and sleep electroencephalography characteristics after 1-year treatment for childhood and juvenile absence epilepsy

PurposeTo compare electroencephalography (EEG) features of newly diagnosed drug-naive childhood absence epilepsy (CAE) and juvenile absence epilepsy (JAE) patients and analyze their response to anti-seizure medications (ASMs). MethodEEG characteristics between CAE and JAE patients and responders and non-responders to ASM at baseline and 12 months were compared, and the changes from baseline were analysed. ResultsA total of 62 patients (32 CAE and 30 JAE) were included. Discharges in baseline awake and sleep EEGs and interictal and polyspike discharges in baseline sleep EEGs were more frequent in JAE patients. Although the median discharge densities (discharge containing seconds per minute) were similar in baseline awake and sleep EEGs between the groups, the median was higher in the JAE group at 12 months and decreased significantly in both groups at 12 months compared to the baseline values. Responses to initial ASMs were 94% and 77% in the CAE and JAE groups, respectively. In initial sleep EEGs of non-responders with JAE, focal onset generalized spike and slow wave discharges (GSWDs) were more frequent, and the median ictal and interictal discharge densities were higher. ConclusionJAE patients had more frequent disorganized discharges at baseline in both awake and sleep EEGs and interictal and polyspike discharges in sleep EEGs than those of CAE patients. Improvement in EEG was more pronounced in CAE patients than in JAE patients. Focal-onset GSWDs and higher ictal and interictal discharge densities on baseline EEG were associated with a poor response to initial ASMs in JAE patients.

Face validation and pharmacologic analysis of Sik3Sleepy mutant mouse as a possible model of idiopathic hypersomnia

Idiopathic hypersomnia (IH) is a chronic neurologic disorder with unknown mechanisms that result in long night-time sleep, daytime sleepiness, long non-refreshing naps, and difficult awakening presenting as sleep drunkenness. IH patients are typically diagnosed by shorter sleep latency on multiple sleep latency test (MSLT) along with long sleep time. Only symptomatic drug treatments are currently available for IH and no animal model to study it. Sleepy mice carry a splicing mutation in the Sik3 gene, leading to increased sleep time and sleep need. Here we used a mouse version of MSLT and a decay analysis of wake EEG delta power to validate the Sleepy mutant mouse as an animal model for IH. Sleepy mice had shorter sleep latency in the dark (active) phase than wild-type mice. They also showed lower decay of EEG delta density during wakefulness, possibly reflecting increased sleep inertia. These data indicate that the Sleepy mouse may have partial face validity as a mouse model for idiopathic hypersomnia. We then investigated the effect of orexin-A and the orexin receptor 2-selective agonist YNT-185 on the sleepiness symptoms of the Sleepy mouse. Intracerebroventricular orexin-A promoted wakefulness for 3 h and decreased wake EEG delta density after injection in Sleepy mice and wild-type mice. Moreover, Sleepy mice but not wild-type mice showed a sleep rebound after the orexin-A-induced wakefulness. Intraperitoneal YNT-185 promoted wakefulness for 3 h after injection in Sleepy mice, indicating the potential of using orexin agonists to treat not only orexin deficiency but hypersomnolence of various etiologies.

0907 Macro and microarchitectural sleep features in Alzheimer’s dementia and mild cognitive impairment in a large clinical cohort

Abstract Introduction Recent work has characterized polysomnographic (PSG) changes in Alzheimer’s Dementia (AD) that correlate with disease severity and precede clinical diagnosis, although these findings are limited by relatively small sample sizes and a lack of studies examining sleep microarchitecture. In this study we examined sleep macro- and microarchitectural features in large cohorts of patients with AD and mild cognitive impairment (MCI). Methods Using a database of over 15,000 clinical PSGs with associated clinical metadata, 125 and 173 subjects were identified with mild AD and MCI, respectively. 260 age and sex matched subjects without neurological disease were identified as healthy controls (HC). Sleep staging results were based on AASM criteria, and micro-architectural features were assessed using an algorithm (Wake EEG Similarity Index, WESI) trained to predict state using spectral data from a unique healthy adult cohort. Results Sleep fragmentation was apparent in both the MCI and AD cohorts (relative to healthy controls), with AD subjects demonstrating substantially more severe PSG disturbances. Healthy controls demonstrated 400 minutes of total sleep time, in contrast to 374 minutes for MCI subjects and 362 minutes for AD subjects. Healthy subjects demonstrated 33 minutes of wake after sleep onset (WASO), compared to 60 and 67 minutes for MCI and AD subjects, respectively. As has been previously reported in smaller cohorts, wakeful bouts and N1 durations were substantially prolonged for AD and (to a lesser extent) MCI subjects, while N2, N3, and REM durations were reduced (with a pronounced reduction in AD N3 percentage). Sleep microarchitectural features assessed using WESI demonstrated that AD and MCI subjects had markedly higher relative wakefulness in all sleep stages except for N3, which showed the same sleep depth as HC. Conclusion We confirm increased sleep fragmentation and loss of deeper sleep stages that correlate to disease severity. Spectral features of all sleep stages except N3 appear more wakeful in AD and MCI subjects, suggesting microarousals or impaired sleep maintenance contribute to sleep pathology. While N3 duration was markedly reduced, depth of sleep as measured by the WESI algorithm was comparable between populations. PSG findings offer promise as early diagnostic and therapeutic targets. Support (if any)

‘High-Density-SleepCleaner’: An open-source, semi-automatic artifact removal routine tailored to high-density sleep EEG

Background:With up to 256 channels, high-density electroencephalography (hd-EEG) has become essential to the sleep research field. The vast amount of data resulting from this magnitude of channels in overnight EEG recordings complicates the removal of artifacts. New Method:We present a new, semi-automatic artifact removal routine specifically designed for sleep hd-EEG recordings. By employing a graphical user interface (GUI), the user assesses epochs in regard to four sleep quality markers (SQMs). Based on their topography and underlying EEG signal, the user eventually removes artifactual values. To identify artifacts, the user is required to have basic knowledge of the typical (patho-)physiological EEG they are interested in, as well as artifactual EEG. The final output consists of a binary matrix (channels x epochs). Channels affected by artifacts can be restored in afflicted epochs using epoch-wise interpolation, a function included in the online repository. Results:The routine was applied in 54 overnight sleep hd-EEG recordings. The proportion of bad epochs highly depends on the number of channels required to be artifact-free. Between 95% and 100% of bad epochs could be restored using epoch-wise interpolation. We furthermore present a detailed examination of two extreme cases (with few and many artifacts). For both nights, the topography and cyclic pattern of delta power look as expected after artifact removal. Comparison with Existing Methods:Numerous artifact removal methods exist, yet their scope of application usually targets short wake EEG recordings. The proposed routine provides a transparent, practical, and efficient approach to identify artifacts in overnight sleep hd-EEG recordings. Conclusion:This method reliably identifies artifacts simultaneously in all channels and epochs.

Comparison of sleep deprivation and a low dose of ketamine on sleep and the electroencephalogram in Brown Norway rats.

Ketamine is known for its antidepressant effects, but the mechanism underlying this effect remains largely unclear. In contrast to most antidepressant drugs, the action of ketamine is rapid, suggesting a different mode of action. A rapid antidepressant effect is also observed following sleep deprivation (SD). In the present study, we aimed to evaluate the effect of a 6-h SD and acute ketamine treatment on vigilance states, locomotor activity, and electroencephalogram (EEG) power density spectra in Brown Norway rats under constant condition over 2 recording days. After SD and after the initial waking period induced by ketamine, both treatments induced a similar increase in non-rapid eye movement (NREM) sleep and EEG slow-wave activity (SWA) in NREM sleep. Rapid eye movement (REM) sleep was reduced immediately after both treatments but was recovered later only after the SD. The effects on the waking EEG differed between the treatments, with a faster theta peak during and after SD, and no change in the waking spectrum after ketamine. In conclusion, SD and ketamine both lead to an acute increment in NREM sleep SWA as well as in a reduction in REM sleep. The results suggest that selective suppression of REM sleep, combined with enhancement of SWA during NREM may be effective in the treatment of depression.

EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease.

Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales. To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD. We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function. SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power. SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.

Evaluation of long-term neurocognitive functions in patients with epileptic encephalopathy with continuous spike-and-wave during sleep (CSWS)/epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS)

ObjectivesEpileptic encephalopathy with continuous spike-and-wave during sleep (CSWS) or the newly named Epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS) is a syndrome in which epileptiform abnormalities are associated with the progressive impairment of cognitive functions. This study aimed to evaluate the neurocognitive executive functions of patients at later ages and determine the long-term prognosis of the condition, as well as the factors affecting this. MethodsThis is a hospital-based cross-sectional study of 17 patients with a diagnosis of CSWS, and a minimum age of 7.5 years. The Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) was used for neurocognitive assessment. The use of immunotherapy (intravenous immunoglobulin and/or steroid for at least 6 months) at the time of initial diagnosis, baseline activity and spike wave index (SWI) of the last wake and sleep EEG, cranial MRI findings, active epileptic seizures since the last examination, and WISC-IV parameters were statistically compared. The results of patients with genetic etiology determined by the whole exome sequencing (WES) method are also reported. ResultsA total of 17 patients were included in the study, with a mean age of 10.30 ± 3.15 years (range from 7.9 to 15.8 years). The mean full scale IQ score of the subjects was 61.41 ± 17.81 (range 39–91), classified as follows: 5.9% (n = 1), average; 23.5% (n = 4), low average; 5.9% (n = 1), very low; 35.3% (n = 6), extremely low (upper range); 29.4% (n = 5), extremely low (lower range) intelligence. Among the four domains of WISC-IV, the most affected index was the Working Memory Index (WMI). EEG parameters, cranial MRI findings and treatment with immunotherapy did not have a significant effect on neurocognitive outcomes. Thirteen patients (76%) were evaluated with WES for a genetic etiology. Pathogenic variants in 5 different genes (GRIN2A, SLC12A5, SCN1A, SCN8A, ADGRV1) associated with epilepsy were detected in 5/13 patients (38%). ConclusionThese results indicated that neurocognition is highly affected in the long term in CSWS.

Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach.

Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.

A novel machine learning technique using resting‐state EEG to assess and monitor cognitive impairment

AbstractBackgroundMonitoring changes in cognitive impairment is an important aspect of care for aging patients. Traditionally, this is done with pen‐and‐paper assessments such as the Mini‐Mental State Exam (MMSE). EEG is a low‐cost and widely available neurophysiology tool that provides a data‐rich measure of brain health, but signal complexity has limited its impact. Advances in machine learning bring new possibilities to decode those complex signals and leverage EEG in patient care. Here, we engineer novel EEG features for assessing brain health with the [Banded Fractal Variability] (BFV) technique. We use [BFV] and other EEG metrics to assess patients’ cognitive function via a continuous score (a [Cognitive Impairment Index], or [CII]). We validate the diagnostic value of the EEG features by comparing our model against reduced models lacking the EEG features.MethodThe patient sample (N=97, mean (sd) age = 69.1 (11.0), 52.6% female) was collected at the Pacific Brain Health Center. Diagnostic categories included subjective cognitive impairment (n=44), mild cognitive impairment (n=26), and dementia (n=27). Mixed disease (AD, DLB, FTD, vascular, diabetes) was common in the impaired sample, and 39.6% were diagnosed with AD. We developed a continuous [CII] model trained on patients’ MMSE scores (range = 11 to 30, mean = 26.6) using a gradient boosting model with [Banded Fractal Variability] and other EEG features computed from clinical wake EEGs. Model development and testing took place within a rigorous nested cross‐validation (NCV) procedure to prevent overfitting. Performance was compared to reduced (no‐EEG features) models using the non‐parametric Wilcoxon sign‐rank test.ResultThe approach showed a Mean Absolute Error of 1.98 (sd = 0.28) across 5 test folds in the NCV outer loops, significantly outperforming both the baseline intercept‐only model (p = 0.031) and the demographics (age and years of education) model (p = 0.031).ConclusionThese preliminary results suggest that our [CII] derived from machine‐learning and quantitative EEG using [BFV] is a viable approach to assess and track patients’ cognitive impairment regardless of disease status across all impairment levels, raising the possibility that a brief EEG scan could shed light on patient health normally accessible only through extensive cognitive and neuropsychological testing.

O068 Slow frequency wake EEG and hypoxemia during sleep predicts vigilance performance following extended wakefulness in patients with OSA

Abstract Study Objectives Obstructive sleep apnea is highly prevalent, but heterogeneous making identifying patients at risk of driving impairment challenging. Resting wake electroencephalography (qEEG) is associated with vigilance performance in healthy subjects. We examined if qEEG and clinical measures predict vigilance impairment in OSA. Methods Fifty-four patients underwent baseline PSG and 28hr extended wakefulness (EW) with 2 hourly psychomotor vigilance test (PVT) and Karolinska drowsiness test and driving simulator performance at baseline and following EW. Resting wake EEG was subjected to fast Fourier transform analysis to derive EEG power in delta, theta, alpha and beta ranges. Cluster analysis assigned participants into groups of either resistant (n=39) or vulnerable (n=15) to alertness failure based on PVT and driving performance. Backward stepwise regression models were used to determine the strongest clincal and wake EEG predictors vigilance impairment. Results Compared with resistant patients, the vulnerable group exhibited increased wake EEG delta (p<0.001) and theta (p=0.003) power during eyes open across all testing periods. The only significant predictors of vulnerability to alertness failure (PVT and driving) included O2Nadir from baseline PSG and baseline wake EEG theta and delta (eyes open) respectively explaining 42 and 32% of the variance in vigilance outcome in models that included Age, BMI, ESS, SOL, TST, AHI, O2 Nadir. Conclusions Slow frequency wake EEG (delta and theta) and hypoxemia are predictive of future alertness failure and driving impairments in patients with OSA. These findings are important for fitness to drive assessment in OSA but require validation in independent sample.

Recording of eeg Signals and Role in Diagnosis of Sleep Disorder

Electroencephalogram (EEG) is a recording of the electrical movement of the brain from the scalp. For sleep disorder analysis the EEG test is done while the subject is sleeping. In this paper discuss about recording of brain signal (EEG) and how these signal play major role to finding in different brain diseases. EEG data can be different when subjects are asleep or when exhausted or when some sort of action takes place. When the patient is awake standard EEG test can be taken, but it may not demonstrate any unusual electrical action. During the sleep, brainwave patterns alter and may show more unusual electrical action.

FMRI spectral signatures of sleep

Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level-dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (<0.1 Hz) oscillation prominent in light sleep and correlated with the occurrence of spindles, and a high-frequency oscillation (>0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake-sleep transitions, and investigate local homeostatic sleep processes.

0519 Sleep Dysfunction in Rett Syndrome

Abstract Introduction Rett syndrome (RTT) is an X-linked neurodevelopmental disorder affecting females and is linked to mutations in the methyl-CpG-binding protein 2 (MeCP2) gene. Typical comorbidities in RTT include poor growth, feeding difficulties, hyperventilation and breath-holding, seizures, scoliosis, and disrupted sleep. A few studies indicate sleep disruption in patients with RTT yet there is minimal data on polysomnographic findings in this population. We reviewed our cohort of Rett syndrome patients who underwent polysomnography. Methods This retrospective case control of 10 RTT subjects (mean age, 11.19 years; ranged from 1 to 33 years) underwent standard polysomnography (PSG) recording. Subjects were compared to 10 age and gender matched controls with an AHI &amp;lt;5 and no chronic medical problems using a student paired t test. All studies were scored using the AASM criteria. Results We found our cohort to have increased N3% sleep, decreased N2%, and shorter sleep onset latency (p&amp;lt; 0.05). Trends of lower BMI, lower oxygen saturation, and shorter REM latency (p &amp;lt; 0.10 but &amp;gt; 0.05). Review of PSGs shows the slow wave have morphological of typical slow waves and not that of the slowing seen on wake EEG nor the epileptiform activity. 4 of the 10 PSGs are notable for frequent interictal epileptiform discharges. 2 of the 10 subjects had a central apnea index &amp;gt;5, 2 had an AHI &amp;gt;5 and 1 demonstrated hypoventilation. There were central apneas associated with hyperventilation during awake and sleep-awake transition. Conclusion Our cohort of RTT patients demonstrates differences in sleep architecture, manifested most notably by a high percentage of SWS. This population has high amplitude rhythmic slow (theta) activity on wake EEG, primarily in the frontal-central regions. This slowing was distinct from the epileptiform activity seen in 40% of our cohort. Central apnea also appeared in older patients and the very young. Larger population studies are needed for future research. Support (If Any)

0176 Comparing Sleep and Homeostatic Sleep Drive Between Retired Night Shift Workers and Retired Day Workers

Abstract Introduction Retired night shift workers report poorer sleep quality compared to retired day workers, even after returning to a nocturnal sleep schedule, suggesting a potential “scarring” of night shift work on sleep. History of sleep deprivation and sleep at an unfavorable circadian phase may compromise sleep and homeostatic sleep regulation, potentially contributing to poor sleep quality in retired night shift workers. This study compared sleep efficiency and homeostatic sleep regulation in response to sleep deprivation (delta EEG power during NREM sleep, theta EEG power during wakefulness) between retired night shift workers and retired day workers. Methods Participants (N = 75, mean age: 68.3 +/-5.5 years, 51% females, 87% non-Hispanic White) were 35 retired night shift workers and 40 age-, sex-, and race-equated retired day workers. Participants completed a 60-hour laboratory study including one baseline night of sleep, followed by 36 hours of sleep deprivation, followed by one recovery night of sleep. Sleep efficiency and NREM delta EEG power were measured by polysomnography on both nights, and waking theta EEG power was measured every other hour during the 36-hour sleep deprivation period. We analyzed the effects of group (retired night shift workers vs. retired day workers), time (baseline vs. recovery night for sleep efficiency and delta EEG power, hour for theta EEG power), and their interaction on each outcome using linear mixed models. Results Groups did not differ in sleep efficiency averaged across nights (F=1.48, p&amp;gt;0.05) or from baseline to recovery nights (F=0.05, p&amp;gt;0.05). Delta EEG responses to sleep deprivation did not differ by group (F=0.33, p&amp;gt;0.05). Compared to retired day workers, retired night shift workers showed greater overall waking EEG theta power during sleep deprivation (F=13.20, p&amp;lt;0.001). The group by time interaction was not significant (F=0.36, p&amp;gt;0.05), suggesting that group differences in theta EEG power were not due to increased sleep deprivation. Conclusion Sleep efficiency and homeostatic sleep regulation appeared to be preserved in retired night shift workers. Theta EEG power findings suggested greater sleep propensity during wakefulness in retired night shift workers. Interventions to improve sleep quality in retired night shift workers may leverage intact homeostatic sleep regulation mechanisms. Support (If Any) R01AG047139, T32HL082610, T32HL07560, T32MH019986

Sleep EEG-Based Approach to Detect Mild Cognitive Impairment.

Mild Cognitive Impairment (MCI) is an early stage of dementia, which may lead to Alzheimer’s disease (AD) in older adults. Therefore, early detection of MCI and implementation of treatment and intervention can effectively slow down or even inhibit the progression of the disease, thus minimizing the risk of AD. Currently, we know that published work relies on an analysis of awake EEG recordings. However, recent studies have suggested that changes in the structure of sleep may lead to cognitive decline. In this work, we propose a sleep EEG-based method for MCI detection, extracting specific features of sleep to characterize neuroregulatory deficit emergent with MCI. This study analyzed the EEGs of 40 subjects (20 MCI, 20 HC) with the developed algorithm. We extracted sleep slow waves and spindles features, combined with spectral and complexity features from sleep EEG, and used the SVM classifier and GRU network to identify MCI. In addition, the classification results of different feature sets (including with sleep features from sleep EEG and without sleep features from awake EEG) and different classification methods were evaluated. Finally, the MCI classification accuracy of the GRU network based on features extracted from sleep EEG was the highest, reaching 93.46%. Experimental results show that compared with the awake EEG, sleep EEG can provide more useful information to distinguish between MCI and HC. This method can not only improve the classification performance but also facilitate the early intervention of AD.

Sex-related differences within sleep-wake dynamics, cataplexy, and EEG fast-delta power in a narcolepsy mouse model.

Narcolepsy type 1 (NT1) is a sleep-wake disorder caused by selective loss of hypocretin (HCRT, also called orexin) neurons. Although the prevalence of NT1 is equal in men and women, sex differences in NT1 symptomatology have been reported in humans and other species. Yet, most preclinical studies fail to include females, resulting in gender bias within translational drug development. We used hcrt-tTA;TetO DTA mice (NT1 mice) that lose their HCRT neurons upon dietary doxycycline removal to examine in detail the effect of sex on NT1 symptoms and sleep-wake characteristics. We recorded 24-h electroencephalography (EEG), electromyography (EMG), and video in adult male and female NT1 mice for behavioral state quantification. While conducting this study, we recognized another type of behavioral arrest different from cataplexy: shorter lasting and with high δ power. We termed these delta attacks and propose a set of criteria for quantifying these in future research. Our findings show that both sexes exhibit high behavioral state instability, which was markedly higher in females with more behavioral arrests interrupting the wake episodes. Females exhibited increased wake at the expense of sleep during the dark phase, and decreased rapid eye movement (REM) sleep during the 24-h day. During the dark phase, fast-δ (2.5-4 Hz) in non-rapid eye movement (NREM) sleep and θ (6-10 Hz) EEG spectral power in REM sleep were lower in females compared to males. We demonstrate that biologically driven sex-related differences exist in the symptomatology of NT1 mice which calls for including both sexes in future research.

Different circuitry dysfunction in drug-naive patients with juvenile myoclonic epilepsy and juvenile absence epilepsy

RationaleJuvenile myoclonic epilepsy (JME) and juvenile absence epilepsy (JAE) are generalized epileptic syndromes presenting in the same age range. To explore whether uneven network dysfunctions may underlie the two different phenotypes, we examined drug-naive patients with JME and JAE at the time of their earliest presentation. MethodsPatients were recruited based on typical JME (n = 23) or JAE (n = 18) presentation and compared with 16 age-matched healthy subjects (HS). We analyzed their awake EEG signals by Partial Directed Coherence and graph indexes. ResultsOut-density and betweenness centrality values were different between groups. With respect to both JAE and HS, JME showed unbalanced out-density and out-strength in alpha and beta bands on central regions and reduced alpha out-strength from fronto-polar to occipital regions, correlating with photosensitivity. With respect to HS, JAE showed enhanced alpha out-density and out-strength on fronto-polar regions. In gamma band, JAE showed reduced Global/Local Efficiency and Clustering Coefficient with respect to HS, while JME showed more scattered values. ConclusionsOur data suggest that regional network changes in alpha and beta bands underlie the different presentation distinguishing JME and JAE resulting in motor vs non-motor seizures characterizing these two syndromes. Conversely, impaired gamma-activity within the network seems to be a non-local marker of defective inhibition.

Bioelectric activity of the brain in 3-4 years old children in eyes-open resting state

Background. Electroencephalography is the main technique for assessing the functional state of the brain. Indications for EEG are diagnosis of paroxysmal states, prediction of the outcome of a pathological state, evaluation of bioelectrical activity if brain death is suspected. Up to 90 % of the native EEG in calm wakefulness in healthy individuals is occupied by “alpha activity”. In children in active wakefulness, the EEG pattern depends to a great extent on their age.Objective. The aim of the work was to assess EEG parameters in children aged 3–4 years in eyes-open resting state. Design and methods. 31 healthy participants aged 3–4 years were enrolled. EEG was registered for 30 minutes in a state of passive wakefulness in the supine position with open eyes. Average values of the power of the spectra for the alpha-rhythm, delta-rhythm and theta-rhythm in the frontal and temporal leads, as well as the ratio of the average power of alpha/theta and alpha/delta rhythms in the frontal and temporal leads were calculated.Results. Average power of the alpha-rhythm was significantly higher over the right frontal lobe than over the right frontal-temporal area, as well as average amplitude of it was significantly higher in F3-A1 than F7-A1, F4-A2 than F8-A2, which is associated with the articulatory praxis. Average alpha-rhythm power was significantly higher in T5-A1 than T3-A1 and T6-A2 than T4-A2, which corresponds to the recognition and naming of objects optically. Significant differences according to the total average power of the alpha- and theta-rhythms above the frontal and frontal-temporal regions reflect the relationship between the frontal cortex temporal lobes and the premotor zones, i.e. arcuate bundle, responsible for the “speech system”.Conclusion. The identified patterns can reflect the characteristics of the state of active wakefulness in a 3–4-year-old child and can be used for comparison in the future (both in the course of behavioral experiments and observation of patients with certain pathological processes).

Prolonged and short epileptiform discharges have an opposite relationship with the sleep–wake cycle in patients with JME: Implications for EEG recording protocols

In a recent study, we found that during 20.55 ± 1.60 h of artifact-free ambulatory EEG recordings, epileptiform discharges (EDs) longer than 2.68 s occurred exclusively in patients with Juvenile Myoclonic Epilepsy (JME) who experienced seizure recurrence within a year after the EEG. Here we expanded this analysis, exploring whether long EDs (>2.68 s), and short ones, were uniformly distributed during the day. Lastly, we evaluated the temporal distribution of seizure relapses.By Friedman test, we demonstrated that hourly frequencies of both short and long EDs were dependent on the hours of day and sleep–wake cycle factors, with an opposite trend. Short EDs were found mostly during the night (with two peaks at 1 AM and 6 AM), and sleep, dropping at the wake onset (p < 0.001). Conversely, long EDs surged at the wake onset (0.001), remaining frequent during the whole wake period, when compared to sleep (p = 0.002). Of note, this latter pattern mirrored that of seizures, which occurred exclusively during the wake period, and in 9 out of 13 cases at the wake onset.We therefore suggested that short and long EDs could reflect distinct pathophysiological phenomena. Extended wake EEG recordings, possibly including the awakening, could be extremely useful in clinical practice, as well as in further studies, with the ambitious goal of predicting seizure recurrences.

High-Frequency Oscillations on Interictal Epileptiform Discharges in Routinely Acquired Scalp EEG: Can It Be Used as a Prognostic Marker?

IntroductionDespite all the efforts for optimizing epilepsy management in children over the past decades, there is no clear consensus regarding whether to treat or not to treat epileptiform discharges (EDs) after a first unprovoked seizure or the optimal duration of therapy with anti-seizure medication (ASM). It is therefore highly needed to find markers on scalp electroencephalogram (EEG) that can help identify pathological EEG discharges that require treatment.Aim of the studyThis retrospective study aimed to identify whether the coexistence of ripples/high-frequency oscillations (HFOs) with interictal EDs (IEDs) in routinely acquired scalp EEG is associated with a higher risk of seizure recurrence and could be used as a prognostic marker.Methods100 children presenting with new onset seizure to Children’s Medical Center- Dallas during 2015–2016, who were not on ASM and had focal EDs on an awake and sleep EEG recorded with sample frequency of 500 HZ, were randomly identified by database review. EEGs were analyzed blinded to the data of the patients. HFOs were visually identified using review parameters including expanded time base and adjusted filter settings.ResultsThe average age of patients was 6.3 years (±4.35 SD). HFOs were visually identified in 19% of the studied patients with an inter-rater reliability of 99% for HFO negative discharges and 78% agreement for identification of HFOs. HFOs were identified more often in the younger age group; however, they were identified in 11% of patients >5 years old. They were more frequently associated with spikes than with sharp waves and more often with higher amplitude EDs. Patients with HFOs were more likely to have a recurrence of seizures in the year after the first seizure (P < 0.05) and to continue to have seizures after 2 years (P < 0.0001). There was no statistically significant difference between the two groups with regards to continuing ASM after 2 years.ConclusionIncluding analysis for HFOs in routine EEG interpretation may increase the yield of the study and help guide the decision to either start or discontinue ASM. In the future, this may also help to identify pathological discharges with deleterious effects on the growing brain and set a new target for the management of epilepsy.