A predictive model combining connectomics and entropy biomarkers to discriminate long-term vagus nerve stimulation efficacy for pediatric patients with drug-resistant epilepsy

AIMS

To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug-resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi-modal feature set with high prediction accuracy.

METHODS

Sixty-five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups.

RESULTS

The proportion of VNS responders was 58.5% (38/65) at the last follow-up. In the analysis of parieto-occipital α band activity, higher synchronization level and nodal efficiency were found in responders. The central-frontal θ band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838.

CONCLUSION

Our results revealed that, compared to nonresponders, VNS responders had a more efficient α band brain network, especially in the parieto-occipital region, and less spectral complexity of θ brain activities in the central-frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model.

Vertebrates on a Chip: Noninvasive Electrical and Optical Mapping of Whole Brain Activity Associated with Pharmacological Treatments

Mapping brain activities is necessary for understanding brain physiology and discovering new treatments for neurological disorders. Such efforts have greatly benefited from the advancement in technologies for analyzing neural activity with improving temporal or spatial resolution. Here, we constructed a multielectrode array based brain activity mapping (BAM) system capable of stabilizing and orienting zebrafish larvae for recording electroencephalogram (EEG) like local field potential (LFP) signals and brain-wide calcium dynamics in awake zebrafish. Particularly, we designed a zebrafish trap chip that integrates with an eight-by-eight surface electrode array, so that brain electrophysiology can be noninvasively recorded in an agarose-free and anesthetic-free format with a high temporal resolution of 40 μs, matching the capability typically achieved by invasive LFP recording. Benefiting from the specially designed hybrid system, we can also conduct calcium imaging directly on immobilized awake larval zebrafish, which further supplies us with high spatial resolution brain-wide activity data. All of these innovations reconcile the limitations of sole LFP recording or calcium imaging, emphasizing a synergy of combining electrical and optical modalities within one unified device for activity mapping across a whole vertebrate brain with both improved spatial and temporal resolutions. The compatibility with in vivo drug treatment further makes it suitable for pharmacology studies based on multimodal measurement of brain-wide physiology.

Current challenges in focal epilepsy treatment: An Italian Delphi consensus

BACKGROUND

Epilepsy, a globally prevalent neurological condition, presents distinct challenges in management, particularly for focal-onset types. This study aimed at addressing the current challenges and perspectives in focal epilepsy management, with focus on the Italian reality.

METHODS

Using the Delphi methodology, this research collected and analyzed the level of consensus of a panel of Italian epilepsy experts on key aspects of focal epilepsy care. Areas of focus included patient flow, treatment pathways, controlled versus uncontrolled epilepsy, follow-up protocols, and the relevance of patient-reported outcomes (PROs). This method allowed for a comprehensive assessment of consensus and divergences in clinical opinions and practices.

RESULTS

The study achieved consensus on 23 out of 26 statements, with three items failing to reach a consensus. There was strong agreement on the importance of timely intervention, individualized treatment plans, regular follow-ups at Epilepsy Centers, and the role of PROs in clinical practice. In cases of uncontrolled focal epilepsy, there was a clear inclination to pursue alternative treatment options following the failure of two previous therapies. Divergent views were evident on the inclusion of epilepsy surgery in treatment for uncontrolled epilepsy and the routine necessity of EEG evaluations in follow-ups. Other key findings included concerns about the lack of pediatric-specific research limiting current therapeutic options in this patient population, insufficient attention to the transition from pediatric to adult care, and need for improved communication. The results highlighted the complexities in managing epilepsy, with broad consensus on patient care aspects, yet notable divergences in specific treatment and management approaches.

CONCLUSION

The study offered valuable insights into the current state and complexities of managing focal-onset epilepsy. It highlighted many deficiencies in the therapeutic pathway of focal-onset epilepsy in the Italian reality, while it also underscored the importance of patient-centric care, the necessity of early and appropriate intervention, and individualized treatment approaches. The findings also called for continued research, policy development, and healthcare system improvements to enhance epilepsy management, highlighting the ongoing need for tailored healthcare solutions in this evolving field.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications

Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.

Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model

Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and the capability of bidirectional long short-term memory (BiLSTM) in mining the long-range dependency of multi-channel time-series, we propose an automatic seizure detection method with a novel end-to-end TCN-BiLSTM model in this work. First, raw EEG is filtered with a 0.5-45 Hz band-pass filter, and the filtered data are input into the proposed TCN-BiLSTM network for feature extraction and classification. Post-processing process including moving average filtering, thresholding and collar technique is then employed to further improve the detection performance. The method was evaluated on two EEG database. On the CHB-MIT scalp EEG database, our method achieved a segment-based sensitivity of 94.31%, specificity of 97.13%, and accuracy of 97.09%. Meanwhile, an event-based sensitivity of 96.48% and an average false detection rate (FDR) of 0.38/h were obtained. On the SH-SDU database we collected, the segment-based sensitivity of 94.99%, specificity of 93.25%, and accuracy of 93.27% were achieved. In addition, an event-based sensitivity of 99.35% and a false detection rate of 0.54/h were yielded. The total detection time consumed for 1[Formula: see text]h EEG data was 5.65[Formula: see text]s. These results demonstrate the superiority and promising potential of the proposed method in real-time monitoring of epileptic seizures.

Parvalbumin neurons in the anterior nucleus of thalamus control absence seizures

OBJECTIVE

Anterior nucleus of thalamus (ANT) has been widely accepted as a potential therapeutic target for drug-resistant epilepsy. Although increased volume of the ANT was also reported in patients with absence epilepsy, the relationship between the ANT and absence epilepsy has been barely illustrated.

METHODS

Using chemogenetics, we evaluated the effect of ANT parvalbumin (PV) neurons on pentylenetetrazole (PTZ)-induced absence seizures in mice.

RESULTS

We found that intraperitoneal injection of PTZ (30 mg/kg) can stably induce absence-like seizures characterized by bilaterally synchronous spike-wave discharges (SWDs). Selective activation of PV neurons in the ANT by chemogenetics could aggravate the severity of absence seizures, whereas selective inhibition of that cannot reverse this condition and even promote absence seizures as well. Moreover, chemogenetic inhibition of ANT PV neurons without administration of PTZ was also sufficient to generate SWDs. Analysis of background EEG showed that chemogenetic activation or inhibition of ANT PV neurons could both significantly increase the EEG power of delta oscillation in the frontal cortex, which might mediate the pro-seizure effect of ANT PV neurons.

SIGNIFICANCE

Our findings indicated that either activation or inhibition of ANT PV neurons might disturb the intrinsic delta rhythms in the cortex and worsen absence seizures, which highlighted the importance of maintaining the activity of ANT PV neurons in absence seizure.

Remote collection of electrophysiological data with brain wearables: opportunities and challenges

Collection of electroencephalographic (EEG) data provides an opportunity to non-invasively study human brain plasticity, learning and the evolution of various neuropsychiatric disorders. Traditionally, due to sophisticated hardware, EEG studies have been largely limited to research centers which restrict both testing contexts and repeated longitudinal measures. The emergence of low-cost "wearable" EEG devices now provides the prospect of frequent and remote monitoring of the human brain for a variety of physiological and pathological brain states. In this manuscript, we survey evidence that EEG wearables provide high-quality data and review various software used for remote data collection. We then discuss the growing body of evidence supporting the feasibility of remote and longitudinal EEG data collection using wearables including a discussion of potential biomedical applications of these protocols. Lastly, we discuss some additional challenges needed for EEG wearable research to gain further widespread adoption.

Machine learning seizure prediction: one problematic but accepted practice

Objective.Epilepsy is one of the most common neurological disorders and can have a devastating effect on a person's quality of life. As such, the search for markers which indicate an upcoming seizure is a critically important area of research which would allow either on-demand treatment or early warning for people suffering with these disorders. There is a growing body of work which uses machine learning methods to detect pre-seizure biomarkers from electroencephalography (EEG), however the high prediction rates published do not translate into the clinical setting. Our objective is to investigate a potential reason for this.Approach.We conduct an empirical study of a commonly used data labelling method for EEG seizure prediction which relies on labelling small windows of EEG data in temporal groups then selecting randomly from those windows to validate results. We investigate a confound for this approach for seizure prediction and demonstrate the ease at which it can be inadvertently learned by a machine learning system.Main results.We find that non-seizure signals can create decision surfaces for machine learning approaches which can result in false high prediction accuracy on validation datasets. We prove this by training an artificial neural network to learn fake seizures (fully decoupled from biology) in real EEG.Significance.The significance of our findings is that many existing works may be reporting results based on this confound and that future work should adhere to stricter requirements in mitigating this confound. The problematic, but commonly accepted approach in the literature for seizure prediction labelling is potentially preventing real advances in developing solutions for these sufferers. By adhering to the guidelines in this paper future work in machine learning seizure prediction is more likely to be clinically relevant.

Case report: Neonatal diabetes mellitus caused by KCNJ11 mutation presenting with intracranial hemorrhage

Neonatal diabetes mellitus (NDM) is a rare type of monogenic diabetes. At present, most published studies have focused on the types of gene mutations associated with NDM and the therapeutic effect of sulfonylureas (SUs) on the disease; few studies on NDM-associated intracranial hemorrhage (ICH) exist. In addition, p.V59M mutations generally lead to intermediate DEND (iDEND: intermediate developmental delay and neonatal diabetes) syndrome without epilepsy. Here, we present a case of a 1-month-old male infant who was diagnosed with NDM caused by a KCNJ11 missense mutation (p.V59M), presenting with cerebral injury. In the early stage of the disease, continuous insulin dose adjustment did not achieve an ideal level of blood glucose. Although blood glucose was subsequently controlled by oral SUs, which were administered after the genetic test result, the patient still displayed epilepsy and developmental delay. In this case report, we present our experience in the treatment of the infant, switching from insulin to oral SUs and we thought that SUs have limited effects on improving the prognosis of neurodevelopmental disturbances in NDM with foci of encephalomalacia. In addition, there may be a relationship between KCNJ11 missense mutations and cerebral injury, and further research must be carried out to confirm these points.

Electroencephalogram utilization and psychiatric comorbidities among children and adolescents with epilepsy in rural Southwestern Uganda

OBJECTIVE

This study aimed at describing routine electroencephalogram (EEG) findings among children and adolescents with a clinical diagnosis of epilepsy and determines how interictal EEG abnormalities vary with the psychiatric comorbidities.

METHODS

We conducted a cross-sectional study among children and adolescents with epilepsy aged 5-18 years receiving care from a regional referral hospital in Southwestern Uganda. Psychiatric comorbidities were assessed using an adapted parent version of Child and Adolescent Symptom Inventory-5. Thirty-minute EEG samples were taken from routine EEG recordings that were locally performed and remotely interpreted for all participants.

RESULTS

Of the 140 participants, 71 (50.7%) had normal EEG findings and 51 (36.4%) had epileptiform abnormalities while 18 (12.9%) had non-epileptiform. Of those who had epileptiform abnormalities on EEG, 23 (45.1%) were focal, 26 (51.0%) were generalized, and 2 (3.9%) were focal with bilateral spread. There was no significant association between the different psychiatric comorbidities and the interictal EEG abnormalities.

CONCLUSIONS

Among children and adolescents with a clinical diagnosis of epilepsy in Southwestern Uganda, only 36% showed epileptiform abnormalities on their EEG recordings. There was no association between the interictal EEG abnormalities and psychiatric comorbidities.

Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography

Automatic algorithms are a proposed alternative to manual assessment of polysomnography data for analyzing sleep structure; however, none are acceptably accurate for clinical use. We investigated the feasibility of an automated sleep stage scoring system called Sleep Scope, which is intended for use with portable 1-channel electroencephalograph, and compared it with the traditional polysomnography scoring method. Twenty-six outpatients and fourteen healthy volunteers underwent Sleep Scope and polysomnography assessments simultaneously. Polysomnography records were manually scored by three sleep experts. Sleep Scope records were scored using a dedicated auto-staging algorithm. Sleep parameters, including total sleep time, sleep latency, wake after sleep onset, and sleep efficiency, were calculated. The epoch-by-epoch pairwise concordance based on the classification of sleep into five stages (i.e., wake, rapid eye movement, N1, N2, and N3) was also evaluated after validating homogeneity and bias between Sleep Scope and polysomnography. Compared with polysomnography, Sleep Scope seemed to overestimate sleep latency by approximately 3 min, but there was no consistent tendency in bias in other sleep parameters. The Κ values ranged from 0.66 to 0.75 for experts' inter-rater polysomnography scores and from 0.62 to 0.67 for Sleep Scope versus polysomnography scores, which indicated sufficient agreement in the determination of sleep stages based on the Landis and Koch criteria. We observed sufficient concordance between Sleep Scope and polysomnography despite lower concordance in sleep disorder patients. Thus, this auto-staging system might serve as a novel clinical tool for reducing the time and expenses required of medical staff and patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41105-022-00421-5.

A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection

Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.

Brain magnetic resonance imaging findings among children with epilepsy in two urban hospital settings, Kampala-Uganda: a descriptive study

Background

Epilepsy is one of the most common neurological conditions in children worldwide. Its presentation is heterogeneous, with diverse underlying aetiology, clinical presentation, and prognosis. Structural brain abnormalities are among the recognized causes of epilepsy. Brain Magnetic Resonance Imaging (MRI) is the imaging modality of choice for epilepsy workup.

We aimed to determine the prevalence and describe the structural abnormalities identified in the brain MRI studies performed on children with epilepsy from two urban hospitals in Kampala, Uganda.

Methods

This was a cross-sectional descriptive study performed at two urban hospital MRI centres. The study population was 147 children aged 1 day to 17 years with confirmed epilepsy. Brain MRI was performed for each child and a questionnaire was used to collect clinical data.

Results

The prevalence of structural abnormalities among children with epilepsy was 74.15% (109 out of 147). Of these, 68.81% were male, and the rest were female. Among these, the majority, 40.14% (59 of 144) were aged 1 month to 4 years. Acquired structural brain abnormalities were the commonest at 69.22% with hippocampal sclerosis (HS) leading while disorders of cortical development were the most common congenital causes.

An abnormal electroencephalogram (EEG) was significant for brain MRI abnormalities among children with epilepsy with 95% of participants with an abnormal EEG study having epileptogenic structural abnormalities detected in their brain MRI studies.

Conclusion and recommendation

Two-thirds of children with epilepsy had structural brain abnormalities. Abnormal activity in the EEG study was found to positively correlate with abnormal brain MRI findings. As such, EEG study should be considered where possible before MRI studies as a determinant for children with epilepsy who will be having imaging studies done in the Ugandan setting.

A Combination of Curcuma longa and Diazepam Attenuates Seizures and Subsequent Hippocampal Neurodegeneration

Epilepsy is one of the most common neurological disorders, which occurs due to the instability in the inhibitory and excitatory synaptic transmissions in the brain. However, many patients develop resistance to the available drugs, which results in cell degeneration caused due to inadequate control of the seizures. Curcumin, Curcuma longa, is known to be effective for the treatment of organic disorders and may prevent seizures, reduce oxidative stress, and decrease brain damage. Given this, the present study evaluated the antiepileptic effects of C. longa in comparison with both the diazepam and the combined application of these two substances, in terms of their effects on the brain activity and the potential histopathological changes in the hippocampus. This study used male Wistar rats (age: 10–12 weeks; weight: 260 ± 20 g), which were pretreated for 4 days with either saline, C. longa, diazepam, or C. longa + diazepam; and on the fifth day, pentylenetetrazol (PTZ) was administered to induce the seizure. In the C. longa group, a significant increase was observed in the latency of the onset of seizure-related behavior. Surprisingly, however, the combined treatment resulted in the best control of the seizure-related behavior, with the greatest latency of the onset of spasms and isolated clonic seizures. This group also obtained the best results in the electroencephalographic trace and seizure control, with a reduction in the frequency and amplitude of the spike-waves. In the saline group, PTZ significantly reduced the number of cells present in the CA1 and CA3 regions of the hippocampus, while the combined treatment obtained the best results in terms of the preservation of the neuron-like cells. These findings indicate that C. longa may contribute to the control of both seizures and the cell damage induced by PTZ, and that its association with diazepam may be a potentially effective option for the treatment of epilepsy in the future.

Feature Extraction of the Brain's Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy

Most of the current complex network studies about epilepsy used the electroencephalogram (EEG) to directly construct the static complex network for analysis and discarded the dynamic characteristics. This study constructed the dynamic complex network on EEG from pediatric epilepsy and pediatric control when they were asleep by the sliding window method. Dynamic features were extracted and incorporated into various machine learning classifiers to explore their classification performances. We compared these performances between the static and dynamic complex network. In the univariate analysis, the initially insignificant topological characteristics in the static complex network can be transformed to be significant in the dynamic complex network. Under most connectivity calculation methods between leads, the accuracy of using dynamic complex network features for discrimination was higher than that of static complex network features. Particularly in the imaginary part of the coherency function (iCOH) method under the full-frequency band, the discrimination accuracies of most machine learning classifiers were higher than 95%, and the discrimination accuracies in the higher-frequency band (beta-frequency band) and the full-frequency band were higher than that of the lower-frequency bands. Our proposed method and framework could efficiently summarize more time-varying features in the EEG and improve the accuracies of the discrimination of the machine learning classifiers more than using static complex network features.

Intestinal Flora Composition Determines Microglia Activation and Improves Epileptic Episode Progress

In response to environmental stimuli, immune memory mediates the plasticity of myeloid cells. Immune training and immune tolerance are two aspects of plasticity. Microglia that are immunologically trained or immunologically tolerant are endowed with a tendency to differentiate into alternative dominant phenotypes (M1/M2). Male C57BL/6 mice (immune-training group, immune-tolerant group, and control group) were used to establish the kainic acid epilepsy model. The seizure grade, duration, latency, hippocampal potential, and energy density were used to evaluate seizures, and the changes in the polarization of microglia were detected by western blot. 16S rDNA sequencing showed that the abundance of Ruminococcus in the immune-tolerant group was the dominant flora. Our research connections Intestinal microorganisms, brain immune status, and epilepsy behavior together. Pro-inflammatory M1 phenotype and anti-inflammatory M2 phenotype mediate and enhance and suppress subsequent inflammation, respectively. We conclude that intestinal microorganisms influence the occurrence and development of epilepsy by regulating the polarization of microglia.

Pedunculopontine Nucleus--Rapid Eye Movement Sleep--Electroencephalogram--Desynchronization (PRED) Axis in the Evolution of Epilepsy: A Novel Concept

Epilepsy is one of the commonest and oldest neurological diseases in the history of mankind, the exact pathophysiology of the evolution of which still remains elusive. The intimate and intriguing relation between epilepsy and sleep has been known for a long time. Rapid eye movement sleep (REMS) is well documented to exert potent antiepileptic action in human epilepsies and the underlying mechanism of which is largely based on its property to induce widespread electroencephalogram (EEG)-desynchronization. The pedunculopontine nucleus (PPN) owing to its property to enhance REMS has recently been under study for its potential role in intractable epilepsy (IE) and has been proposed as a novel deep brain stimulation target in IE. This brief paper unfolds the existing role of PPN, REMS, and EEG-desynchronization (PRED) in the evolution of epilepsy in an axial manner, the realization and comprehension of which is likely to open new avenues for further understanding of epileptogenesis, improved treatment of epilepsy and reducing the risk of IE.

From Physiology to Pathology of Cortico-Thalamo-Cortical Oscillations: Astroglia as a Target for Further Research

The electrographic hallmark of childhood absence epilepsy (CAE) and other idiopathic forms of epilepsy are 2.5-4 Hz spike and wave discharges (SWDs) originating from abnormal electrical oscillations of the cortico-thalamo-cortical network. SWDs are generally associated with sudden and brief non-convulsive epileptic events mostly generating impairment of consciousness and correlating with attention and learning as well as cognitive deficits. To date, SWDs are known to arise from locally restricted imbalances of excitation and inhibition in the deep layers of the primary somatosensory cortex. SWDs propagate to the mostly GABAergic nucleus reticularis thalami (NRT) and the somatosensory thalamic nuclei that project back to the cortex, leading to the typical generalized spike and wave oscillations. Given their shared anatomical basis, SWDs have been originally considered the pathological transition of 11-16 Hz bursts of neural oscillatory activity (the so-called sleep spindles) occurring during Non-Rapid Eye Movement (NREM) sleep, but more recent research revealed fundamental functional differences between sleep spindles and SWDs, suggesting the latter could be more closely related to the slow (<1 Hz) oscillations alternating active (Up) and silent (Down) cortical activity and concomitantly occurring during NREM. Indeed, several lines of evidence support the fact that SWDs impair sleep architecture as well as sleep/wake cycles and sleep pressure, which, in turn, affect seizure circadian frequency and distribution. Given the accumulating evidence on the role of astroglia in the field of epilepsy in the modulation of excitation and inhibition in the brain as well as on the development of aberrant synchronous network activity, we aim at pointing at putative contributions of astrocytes to the physiology of slow-wave sleep and to the pathology of SWDs. Particularly, we will address the astroglial functions known to be involved in the control of network excitability and synchronicity and so far mainly addressed in the context of convulsive seizures, namely (i) interstitial fluid homeostasis, (ii) K⁺ clearance and neurotransmitter uptake from the extracellular space and the synaptic cleft, (iii) gap junction mechanical and functional coupling as well as hemichannel function, (iv) gliotransmission, (v) astroglial Ca²⁺ signaling and downstream effectors, (vi) reactive astrogliosis and cytokine release.

Newly diagnosed seizures assessed at two established first seizure clinics: Clinic characteristics, investigations, and findings over 11 years

Objective

'First seizure' clinics (FSCs) aim to achieve early expert assessment for individuals with possible new-onset epilepsy. These clinics also have substantial potential for research into epilepsy evolution, outcomes, and costs. However, a paucity of FSCs details has implications for interpretation and utilization of this research.

Methods

We reviewed investigation findings over 11 years (2000-2010) from two established independent FSCs at Austin Health (AH) and Royal Melbourne Hospital (RMH), Australia. These adult clinics are in major public hospitals and operate with similar levels of expertise. Organizational differences include screening and dedicated administration at AH. Included were N = 1555 patients diagnosed with new-onset unprovoked seizures/epilepsy (AH n = 901, RMH n = 654). Protocol-driven interviews and investigations had been recorded prospectively and were extracted from medical records for study.

Results

Median patient age was 37 (IQR 26-52, range 18-94) years (AH 34 vs RMH 42 years; P < .001). Eighty-six percent of patients attended FSC within three weeks postindex seizure (median AH 12 vs RMH 25 days; P < .01). By their first appointment, 42% had experienced ≥2 seizures. An EEG was obtained within three weeks postindex seizure in 73% of patients, demonstrating epileptiform discharges in 25% (AH 33% vs RMH 15%). Seventy-six percent of patients had an MRI within 6 weeks. Of those with imaging (n = 1500), 19% had potentially epileptogenic abnormalities (RMH 28% vs AH 12%; P < .01). At both sites, changes due to previous stroke/hemorrhage were the commonest lesions, followed by traumatic brain injury. ≥WHO level 1 brain tumors diagnosed at presentation comprised a very small proportion (<1%) at each clinic. At both sites, epilepsy type could be determined in 60% of patients; RMH had more focal and AH more generalized epilepsy diagnoses.

Significance

Differences between the clinics' administrative and screening practices may contribute to differences in investigation findings. Insight into these differences will facilitate interpretation and utilization, and planning of future research.

Impairments in path integration, rotational memory and balancing in patients with temporal lobe epilepsy

Objectives

The vestibulo-medial temporal lobe (MTL) axis model proposes that the vestibular system and the MTL are tightly linked both structurally and functionally so that alterations of one structure should entail disturbances in the other. Accordingly, patients with temporal lobe epilepsy (TLE) with their functional and possible structural temporal lobe pathology should show deficits in vestibular-related behaviour. This study aimed at assessing behavioural deficits related to a suspected disturbance of the vestibulo-MTL axis in patients with TLE.

Methods

Twenty patients with TLE (46.7±15.1 years, seven females) and their age-matched and gender-matched controls (46.7±15.1, seven females) underwent three test batteries that challenged vestibular and MTL functions: balancing, path integration (triangle completion test) and rotational memory. In addition, participants underwent a structural MRI for grey matter analysis using voxel-based morphometry.

Results

Compared with controls, patients with TLE showed significantly inferior performance in all three behavioural tests, with large effect sizes. There were no significant grey matter differences between the two groups.

Conclusion

These results indicate a potential disturbance in the vestibulo-MTL axis in TLE; these are to be verified by future large-scale studies. In the current study, these behavioural deficits emerged without evidence of any brain volume differences between the patients and their controls as depicted by high-resolution MRI. This speaks for a dissociation between functional and structural alterations in TLE.

How are results of EEG activation procedures associated with patient perception of seizure provocative factors? A single-center cross-sectional pilot study

PURPOSE

The purpose of this study was to examine the relationship between subjectively perceived seizure provocative factors or inhibitors and objectively recorded changes in epileptiform activity (EA) during EEG activation procedures.

MATERIAL AND METHODS

Consenting epilepsy patients (≥18 years old) were asked to complete a questionnaire by indicating whether items on a list provoke, inhibit or have no effect on their seizures. A scalp EEG was recorded afterwards to evaluate baseline epileptiform activity and its change (increase/decrease in frequency) during a set of activation procedures. These included hyperventilation, intermittent photic stimulation (IPS), eye-closing/eye-opening, tasks of reading aloud in a native and a foreign language, solving a Rubik's cube and crossing-out letters. We used correlation and multiple regression analysis to search for associations between the sum of self-reported provocative/inhibiting items and changes in EA.

RESULTS

Of the 90 patients recruited 75 (83.3%) indicated at least one seizure provocative factor. Sleep deprivation, emotional stress, negative emotions and alcohol use were most frequently selected as provoking seizures. Positive feelings, focused thinking, mental calculation and exercising were the most predominant seizure inhibitors. EEG data revealed a weak, but statistically significant correlation with the sum of items in distinct questionnaire groups (0.20 ≤ Spearman's ρ ≤ 0.39). Sensory stimuli (olfactory, gustatory, auditory and visual), cognitive phenomena (thoughts and feelings) and substance use were found to be significantly correlated with EEG results by being self-reported as both provoking and inhibiting seizures. A statistically significant relationship was also found between the increase in EA while reading aloud in a native language and the number of physiological states (sleep deprivation, stress etc.) indicated as provoking seizures (Spearman's ρ = 0.320, P = 0.005). A suitable stepwise multiple regression model was feasible for this finding (F(3, 71) = 7.396, P < 0.001, adjusted R squared = 0.206) with the additional inclusion of EA change during IPS and epilepsy type as explanatory variables.

CONCLUSION

Our pilot study indicates that there is a previously non-explored association between patients' self-perception of seizure provocative/inhibiting factors and objectively recorded changes in epileptiform activity during activation EEGs. Distinct EEG tests might be useful in activating ictogenic networks that are sensitive to indirect influence by hormonal, emotional or diurnally variable factors.

Model Predictive Control for Seizure Suppression Based on Nonlinear Auto-Regressive Moving-Average Volterra Model

This article investigates a closed-loop brain stimulation method based on model predictive control strategy to suppress epileptic seizures. A neural mass model (NMM), exhibiting the normal and various epileptic seizures by changing physiologically meaningful parameters, is used as a black-box model of the brain. Based on system identification, an auto-regressive moving-average Volterra model is established to reveal the relationship between stimulation and neuronal responses. Then, the model predictive control strategy is implemented based the Volterra model, which can generate an optimal stimulation waveform to eliminate epileptiform waves. The computational simulation results indicate the proposed closed-loop control strategy can optimize the stimulation waveform without particular knowledge of the physiological properties of the system. The robustness of the proposed control strategy to system disturbances makes it more appropriate for future clinical application.

Pencil-paper on-skin electronics

Pencils and papers are ubiquitous in our society and have been widely used for writing and drawing, because they are easy to use, low-cost, widely accessible, and disposable. However, their applications in emerging skin-interfaced health monitoring and interventions are still not well explored. Herein, we report a variety of pencil-paper-based on-skin electronic devices, including biophysical (temperature, biopotential) sensors, sweat biochemical (pH, uric acid, glucose) sensors, thermal stimulators, and humidity energy harvesters. Among these devices, pencil-drawn graphite patterns (or combined with other compounds) serve as conductive traces and sensing electrodes, and office-copy papers work as flexible supporting substrates. The enabled devices can perform real-time, continuous, and high-fidelity monitoring of a range of vital biophysical and biochemical signals from human bodies, including skin temperatures, electrocardiograms, electromyograms, alpha, beta, and theta rhythms, instantaneous heart rates, respiratory rates, and sweat pH, uric acid, and glucose, as well as deliver programmed thermal stimulations. Notably, the qualities of recorded signals are comparable to those measured with conventional methods. Moreover, humidity energy harvesters are prepared by creating a gradient distribution of oxygen-containing groups on office-copy papers between pencil-drawn electrodes. One single-unit device (0.87 cm²) can generate a sustained voltage of up to 480 mV for over 2 h from ambient humidity. Furthermore, a self-powered on-skin iontophoretic transdermal drug-delivery system is developed as an on-skin chemical intervention example. In addition, pencil-paper-based antennas, two-dimensional (2D) and three-dimensional (3D) circuits with light-emitting diodes (LEDs) and batteries, reconfigurable assembly and biodegradable electronics (based on water-soluble papers) are explored.

Respiratory-related brain pulsations are increased in epilepsy-a two-centre functional MRI study

Resting-state functional MRI has shown potential for detecting changes in cerebral blood oxygen level-dependent signal in patients with epilepsy, even in the absence of epileptiform activity. Furthermore, it has been suggested that coefficient of variation mapping of fast functional MRI signal may provide a powerful tool for the identification of intrinsic brain pulsations in neurological diseases such as dementia, stroke and epilepsy. In this study, we used fast functional MRI sequence (magnetic resonance encephalography) to acquire ten whole-brain images per second. We used the functional MRI data to compare physiological brain pulsations between healthy controls (n = 102) and patients with epilepsy (n = 33) and furthermore to drug-naive seizure patients (n = 9). Analyses were performed by calculating coefficient of variation and spectral power in full band and filtered sub-bands. Brain pulsations in the respiratory-related frequency sub-band (0.11-0.51 Hz) were significantly (P < 0.05) increased in patients with epilepsy, with an increase in both signal variance and power. At the individual level, over 80% of medicated and drug-naive seizure patients exhibited areas of abnormal brain signal power that correlated well with the known clinical diagnosis, while none of the controls showed signs of abnormality with the same threshold. The differences were most apparent in the basal brain structures, respiratory centres of brain stem, midbrain and temporal lobes. Notably, full-band, very low frequency (0.01-0.1 Hz) and cardiovascular (0.8-1.76 Hz) brain pulses showed no differences between groups. This study extends and confirms our previous results of abnormal fast functional MRI signal variance in epilepsy patients. Only respiratory-related brain pulsations were clearly increased with no changes in either physiological cardiorespiratory rates or head motion between the subjects. The regional alterations in brain pulsations suggest that mechanisms driving the cerebrospinal fluid homeostasis may be altered in epilepsy. Magnetic resonance encephalography has both increased sensitivity and high specificity for detecting the increased brain pulsations, particularly in times when other tools for locating epileptogenic areas remain inconclusive.

Circadian Rhythms and Epilepsy: A Suitable Case for Absence Epilepsy

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

Phase lag index and spectral power as QEEG features for identification of patients with mild cognitive impairment in Parkinson's disease

OBJECTIVES

To identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients.

METHODS

We recorded EEG data for a group of PD patients with MCI (n = 27) and PD patients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated. These measures were used to classify MCI vs. MCI-free patients. We also assessed correlations of these features with cognitive tests based on comprehensive scores (domains).

RESULTS

PLI measures classified PD-MCI from non-MCI patients better than frequency measures. PLI in delta, theta band had highest importance for identifying patients with MCI. Amongst cognitive domains, we identified the most significant correlations between Memory and Theta PLI, Attention and Beta PLI.

CONCLUSION

PLI is an effective quantitative EEG measure to identify PD patients with MCI.

SIGNIFICANCE

We identified quantitative EEG measures which are important for early identification of cognitive decline in PD.

Gas-Permeable, Multifunctional On-Skin Electronics Based on Laser-Induced Porous Graphene and Sugar-Templated Elastomer Sponges

Soft on-skin electronics have broad applications in human healthcare, human-machine interface, robotics, and others. However, most current on-skin electronic devices are made of materials with limited gas permeability, which constrain perspiration evaporation, resulting in adverse physiological and psychological effects, limiting their long-term feasibility. In addition, the device fabrication process usually involves e-beam or photolithography, thin-film deposition, etching, and/or other complicated procedures, which are costly and time-consuming, constraining their practical applications. Here, a simple, general, and effective approach for making multifunctional on-skin electronics using porous materials with high-gas permeability, consisting of laser-patterned porous graphene as the sensing components and sugar-templated silicone elastomer sponges as the substrates, is reported. The prototype device examples include electrophysiological sensors, hydration sensors, temperature sensors, and joule-heating elements, showing signal qualities comparable to conventional, rigid, gas-impermeable devices. Moreover, the devices exhibit high water-vapor permeability (≈18 mg cm^-2 h^-1 ), ≈18 times higher than that of the silicone elastomers without pores, and also show high water-wicking rates after polydopamine treatment, up to 1 cm per 30 s, which is comparable to that of cotton. The on-skin devices with such attributes could facilitate perspiration transport and evaporation, and minimize discomfort and inflammation risks, thereby improving their long-term feasiblity.

Integrating the systematic assessment of psychological states in the epilepsy monitoring unit: Concept and compliance

BACKGROUND

Admission to the epilepsy monitoring unit (EMU) for long-term video-electroencephalography (EEG) monitoring (VEEG) constitutes the gold standard for seizure diagnosis and presurgical evaluation. This study applied the concept of a high-frequency systematic monitoring of psychological states and tested patients' compliance in order to evaluate if its integration in the EMU is feasible and if patients benefit from the graphically underpinned discussion of their EMU stay-related cognitions and emotions.

METHODS

The process-monitoring is technically realized by an internet-based device for data collection and data analysis, the Synergetic Navigation System (SNS). A convenient sample was enrolled: All eligible patients who were admitted to the EMU of the Department of Neurology, Christian Doppler Medical Center, Salzburg, Austria, between November 6th 2017 and January 26th 2018 were approached and recruited upon consent. After a short resource-oriented interview, each enrolled patient was provided with a tablet. The daily questionnaire included eight standardized and up to three personalized items. Self-assessments were collected every 5 h prior to meal times (6:30 am, 11:30 am, and 4:30 pm) and at 9:30 pm. The detailed visualizations of the patients' replies were discussed with the participants during a feedback session at the end of the EMU stay.

RESULTS

Twenty-one patients (12 women/9 men, median age 29 years [range 18-74 years]) were consecutively recruited (72% of all eligible patients). Compliance rates were high (median: 82%, range 60%-100%) among the respondents. Mood correlated strongly with hopefulness (r = 0.71) and moderately with energy (r = 0.63) in all patients. When correlating the intraindividual medians of the process questionnaire time series with the pretest total scores, energy correlated moderately and negatively with the Perceived Stress Scale (PSS) (r = -0.45), while self-efficacy correlated moderately and negatively with the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) total scores in all patients (r = -0.5). Nine patients (43%) reported that they learned something meaningful about themselves after the feedback discussion of their individual time series.

CONCLUSION

The results support the feasibility of high-frequency monitoring of psychological states and processes in routine EMU settings. Repeated daily collections four times per day of psychological surveys allow for the assessment of highly resolved, equidistant time series data, which gives insight into psychological states and processes during EMU admission.

Electroencephalography abnormalities in generalized epilepsy and their predictors: A multicenter experience

Background:

In spite of the overwhelming significance of knowledge of basic elements of electroencephalography (EEG) in its application to the diagnostic workup and the management of patients with suspected or already established generalized epilepsy (GE), there is a dearth of data on the pattern and utility of clinical variables that can independently determine EEG abnormalities in GE.

Objective:

The study was designed to evaluate the frequency and pattern of EEG abnormality as well as assess the utility of clinical variables in predicting the likelihood of an abnormal EEG in GE.

Methods:

It was a cross-sectional study involving the analysis of EEGs of consecutive patients with clinical diagnosis of idiopathic GE from three centers over a 7-year period. Information on sociodemographic and seizure variables was obtained. The International Federation of Societies for Electroencephalography and Clinical Neurophysiology definition of interictal epileptiform discharges (interictal epileptiform activity [IEA]) was adopted in the study.

Results:

A total of 403 patients comprising 242 (60%) males and 161 (40%) females with clinical diagnosis of GE had EEG. Their age ranged between 2 weeks and 70 years, with a median age of 21 years and an interquartile age of 26 years. Two hundred and thirty-seven (58.8%) and 213 (52.9%) patients had abnormal EEG and IEA, respectively. Before adjustment for confounders, female gender (P = 0.0001), pediatric age group (P = 0.0388), duration of epilepsy of 1–4 years (P = 0.01387), uncontrolled seizure (P = 0.0060), and seizure frequency (P = 0.0001) were significantly associated with the presence of abnormal EEG. However, age, female gender, poor seizure control, and seizure frequencies were the independent predictors of EEG abnormality.

Conclusion:

The study showed that about 58% of patients with GE patients had abnormal EEG. Age, poor seizure control, and high frequency of seizure were independent predictors of the presence of EEG abnormality.

Long-interval intracortical inhibition as biomarker for epilepsy: a transcranial magnetic stimulation study

Cortical excitability, as measured by transcranial magnetic stimulation combined with electromyography, is a potential biomarker for the diagnosis and follow-up of epilepsy. We report on long-interval intracortical inhibition data measured in four different centres in healthy controls (n = 95), subjects with refractory genetic generalized epilepsy (n = 40) and with refractory focal epilepsy (n = 69). Long-interval intracortical inhibition was measured by applying two supra-threshold stimuli with an interstimulus interval of 50, 100, 150, 200 and 250 ms and calculating the ratio between the response to the second (test stimulus) and to the first (conditioning stimulus). In all subjects, the median response ratio showed inhibition at all interstimulus intervals. Using a mixed linear-effects model, we compared the long-interval intracortical inhibition response ratios between the different subject types. We conducted two analyses; one including data from the four centres and one excluding data from Centre 2, as the methods in this centre differed from the others. In the first analysis, we found no differences in long-interval intracortical inhibition between the different subject types. In all subjects, the response ratios at interstimulus intervals 100 and 150 ms showed significantly more inhibition than the response ratios at 50, 200 and 250 ms. Our second analysis showed a significant interaction between interstimulus interval and subject type (P = 0.0003). Post hoc testing showed significant differences between controls and refractory focal epilepsy at interstimulus intervals of 100 ms (P = 0.02) and 200 ms (P = 0.04). There were no significant differences between controls and refractory generalized epilepsy groups or between the refractory generalized and focal epilepsy groups. Our results do not support the body of previous work that suggests that long-interval intracortical inhibition is significantly reduced in refractory focal and genetic generalized epilepsy. Results from the second analysis are even in sharper contrast with previous work, showing inhibition in refractory focal epilepsy at 200 ms instead of facilitation previously reported. Methodological differences, especially shorter intervals between the pulse pairs, may have contributed to our inability to reproduce previous findings. Based on our results, we suggest that long-interval intracortical inhibition as measured by transcranial magnetic stimulation and electromyography is unlikely to have clinical use as a biomarker of epilepsy.

Racial disparities in temporal lobe epilepsy

OBJECTIVE

This study reports on epilepsy type period prevalence and black-white racial differences in a large patient population in the Southeastern United States.

METHODS

For all patients visiting the University of Alabama at Birmingham's seizure monitoring unit between 2000 and 2011 (n = 3240), video EEG diagnosis was recorded along with basic demographic information. Descriptive statistics and multivariate logistic regression were used to identify factors associated with temporal lobe epilepsy (TLE) diagnosis.

RESULTS

The racial distribution was 77.3% white, and 20.0% black (other races were only 2.3% of the population). Most patients had either TLE (n = 630) or PNES (n = 1150) compared to other focal (n = 424) or generalized epilepsies (n = 224). The diagnosis of TLE was significantly greater for blacks than whites (odds ratio [OR] = 1.87, 95% confidence interval [CI] 1.47-2.37). The period prevalence measures for the other conclusively diagnosed epilepsies were not significantly different. Women were disproportionately represented in the study population, and black women carried the most statistical weight for the TLE prevalence difference.

INTERPRETATION

The nearly two-fold larger period prevalence of TLE among black patients is a striking finding that merits explanation. Although some selection bias exists due to a moderately lower than expected representation of blacks, socioeconomic status or access to care should not be assumed to be the only factors that might be responsible for the prevalence difference. Rather, all clues for distinct pathophysiological racial differences should be explored.

Establishment of a rhesus monkey model of chronic temporal lobe epilepsy using repetitive unilateral intra-amygdala kainic acid injections

OBJECTIVE

Temporal lobe epilepsy (TLE) is a common type of acquired epilepsy refractory to medical treatment. As such, establishing animal models of this disease is critical to developing new and effective treatment modalities. Because of their small head size, rodents are not suitable for comprehensive electroencephalography (EEG) evaluation via scalp or subdural electrodes. Therefore, a larger primate model that closely recapitulates signs of TLE is needed; here we describe a rhesus monkey model resembling chronic TLE.

METHODS

Eight monkeys were divided into two groups: kainic acid (KA) group (n=6) and saline control group (n=2). Intra-amygdala KA injections were performed biweekly via an Ommaya device until obvious epileptiform discharges were recorded. Video-EEG recording was conducted intermittently throughout the experiment using both scalp and subdural electrodes. Brains were then analyzed for Nissl and glial fibrillary acid protein (GFAP) immunostaining.

RESULTS

After 2-4 injections of KA (approximately 1.2-2.4mg, 0.12-0.24mg/kg), interictal epileptiform discharges (IEDs) were recorded in all KA-treated animals. Spontaneous recurrent seizures (SRSs) accompanied by symptoms mimicking temporal lobe absence (undetectable without EEG recording), but few mild motor signs, were recorded in 66.7% (four of six) KA-treated animals. Both IEDs and seizures indicated a primary epileptic zone in the right temporal region and contralateral discharges were later detected. Segmental pyramidal cell loss and gliosis were detected in the brain of a KA-treated monkey.

CONCLUSIONS

Through a modified protocol of unilateral repetitive intra-amygdala KA injections, a rhesus monkey model with similar behavioral and brain electrical features as TLE was developed.

[Role of video electroencephalogram in diagnosis and localization of epilepsy in children]

OBJECTIVE

To study the role of video electroencephalogram (VEEG) versus regular electroencephalogram (REEG) in the diagnosis of epilepsy and localization of origin of epileptic discharge in children through a comparative analysis.

METHODS

A retrospective analysis was performed for the clinical data of 223 children with clinical paroxysmal symptoms in the past and suspected epilepsy. VEEG and REEG were compared from the aspects of monitoring of clinical seizures, interictal epileptiform discharge (IED), localization of the origin of IED, and identification of non-epileptic seizures, and the detection rate of IED during awakening and sleep stages was also compared.

RESULTS

Compared with REEG, VEEG had significantly higher detection rates of IED and synchronous clinical seizures in children with epileptiform discharge (P<0.01). Of all children, 86 were diagnosed with epilepsy, 78 were diagnosed with epilepsy syndrome, 31 were diagnosed with non-epileptic seizures, and 81 had a definite location of the origin of epileptic discharge according to the VEEG. The detection rate of IED in the sleep stage was higher than that in the awakening stage (46% vs 13.2%; P<0.01), and IED was mainly detected in the NREM I-II stages according to the VEEG.

CONCLUSIONS

VEEG has a significantly better performance than REEG in the diagnosis and localization of epilepsy in children and has a high value in clinical practice.

The role of outpatient ambulatory electroencephalography in the diagnosis and management of adults with epilepsy or nonepileptic attack disorder: A systematic literature review

Electroencephalography (EEG) is an established diagnostic tool with important implications for the clinical management of patients with epilepsy or nonepileptic attack disorder. Different types of long-term EEG recording strategies have been developed over the last decades, including the widespread use of ambulatory electroencephalography (AEEG), which holds great potential in terms of both clinical usefulness and cost-effectiveness. In this paper, we present the results of a systematic review of the scientific literature on the use of AEEG in the diagnosis of epilepsy and nonepileptic attacks in adult patients. Taken together, our findings confirmed that AEEG is a useful diagnostic tool in patients with equivocal findings on routine EEG studies and influences management decisions in the majority of studies. There is evidence that AEEG is also more likely to capture events than sleep-deprived EEG; however, there are currently insufficient data available to compare the diagnostic utility of modern AEEG technology with inpatient video-telemetry. Further research on the combined use of AEEG and home-video recording is, therefore, warranted.

"Cut-and-Paste" Manufacture of Multiparametric Epidermal Sensor Systems

Multifunctional epidermal sensor systems (ESS) are manufactured with a highly cost and time effective, benchtop, and large-area "cut-and-paste" method. The ESS made out of thin and stretchable metal and conductive polymer ribbons can be noninvasively laminated onto the skin surface to sense electrophysiological signals, skin temperature, skin hydration, and respiratory rate.