Subunit Composition of Neurotransmitter Receptors in the Immature and in the Epileptic Brain

Neuronal activity is critical for synaptogenesis and the development of neuronal networks. In the immature brain excitation predominates over inhibition facilitating the development of normal brain circuits, but also rendering it more susceptible to seizures. In this paper, we review the evolution of the subunit composition of neurotransmitter receptors during development, how it promotes excitation in the immature brain, and how this subunit composition of neurotransmission receptors may be also present in the epileptic brain. During normal brain development, excitatory glutamate receptors peak in function and gamma-aminobutiric acid (GABA) receptors are mainly excitatory rather than inhibitory. A growing body of evidence from animal models of epilepsy and status epilepticus has demonstrated that the brain exposed to repeated seizures presents a subunit composition of neurotransmitter receptors that mirrors that of the immature brain and promotes further seizures and epileptogenesis. Studies performed in samples from the epileptic human brain have also found a subunit composition pattern of neurotransmitter receptors similar to the one found in the immature brain. These findings provide a solid rationale for tailoring antiepileptic treatments to the specific subunit composition of neurotransmitter receptors and they provide potential targets for the development of antiepileptogenic treatments

Chronopharmacology of Anti-Convulsive Therapy

Approximately one-third of patients with epilepsy continue to have seizures despite antiepileptic therapy. Many seizures occur in diurnal, sleep/wake, circadian, or even monthly patterns. The relationship between biomarkers and state changes is still being investigated, but early results suggest that some of these patterns may be related to endogenous circadian patterns whereas others may be related to wakefulness and sleep or both. Chronotherapy, the application of treatment at times of greatest seizure susceptibility, is a technique that may optimize seizure control in selected patients. It may be used in the form of differential dosing, as preparations designed to deliver sustained or pulsatile drug delivery or in the form of ‘zeitgebers’ that shift endogenous rhythms. Early trials in epilepsy suggest that chronopharmacology may provide improved seizure control compared with conventional treatment in some patients. The present article reviews chronopharmacology in the treatment of epilepsy as well as future treatment avenues

Nonlinear EEG biomarker profiles for autism and absence epilepsy

Background Although autism and epilepsy are considered to be different disorders, epileptiform EEG activity is common in people with autism even when overt seizures are not present. The relatively high comorbidity between autism and all epilepsy syndromes suggests the possibility of common underlying neurophysiological mechanisms. Although many different epilepsies may be comorbid with autism, absence epilepsy is a generalized epilepsy syndrome with seizures that appear as staring spells, with no motor signs and no focal lesions, making it more difficult to diagnose. Application of nonlinear methods for EEG signal analysis may enable characterization of brain activity that can help to delineate neurophysiological commonalities and differences between autism and epilepsy. Multiscale entropy and recurrence quantitative analysis (RQA) were computed from EEG signals derived from children with autism or absence epilepsy and compared with the goal of finding significant and potentially clinically useful biomarkers neurophysiological differences between these two childhood disorders. Methods Multiscale entropy and a multiscale version of RQA were computed from EEG data obtained from 92 children were collected in two different settings at Boston Children’s Hospital. Short segments of alert resting state EEG were selected for analysis. A complexity index derived from entropy and RQA methods was computed from each of 19 standard EEG channels for all subjects using publicly available software. Statistical comparisons were made between the groups. Machine learning classifiers were also used to determine which derived features were most significantly different among the groups, and to determine classification specificity and sensitivity. Results Significant differences were found between absence, autism, and control groups in a number of different scalp locations and the values of complexity index. Autism values appeared to be intermediate between epilepsy and control in many locations, and differences between controls and absence patients were more widely distributed across scalp locations. Classification algorithms were able to distinguish absence epilepsy and autism cases from controls with high (\u3e95%) accuracy. Importantly, two independent control groups, although they were derived from different settings and with different equipment were statistically indistinguishable. Conclusions Signficant neurophysiological differences were found between absence, autism, and control cases. In most scalp regions, autism values were intermediate between the control values and absence values, suggesting several future research studies. Nonlinear EEG signal analysis, together with classification methods, may provide complementary information to visual EEG analysis and clinical assessment in epilepsy and autism, and may provide useful information for research on pediatric neurodevelopmental and neurological disorders. Additional research may enable neurophysiological biomarker profiles to be derived from these techniques for clinical use

The Need for Antiepileptic Drug Chronotherapy to Treat Selected Childhood Epilepsy Syndromes and Avert the Harmful Consequences of Drug Resistance

Antiepileptic drug (AED) chronotherapy involves the delivery of a greater AED dose at the time of greatest seizure susceptibility usually associated with predictable seizure peaks. Although research has proven AED chronotherapy, commonly known as differential dosing, to be safe, well tolerated, and highly effective in managing cyclic seizure patterns in selected childhood epilepsies, conventional, equally divided AED dosing remains the standard of care. Differential dosing is more often applied in the emergency management of acute seizure clustering resulting from drug resistance—a harmful epilepsy-related consequence that affects 30% of children. Moreover, drug resistance is a major risk factor in status epilepticus and sudden, unexpected death in epilepsy. Although these facts should promote the wider use of differential dosing in selected cases, a credible hypothesis is needed that defines the differential dosing strategy and application in cyclic epilepsy and for the greater purpose of preventing harmful outcomes

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

Objective: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. Methods: Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. Results: Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. Significance: Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization

Devices for Ambulatory Monitoring of Sleep-Associated Disorders in Children with Neurological Diseases

Good sleep quality is essential for a child’s wellbeing. Early sleep problems have been linked to the later development of emotional and behavioral disorders and can negatively impact the quality of life of the child and his or her family. Sleep-associated conditions are frequent in the pediatric population, and even more so in children with neurological problems. Monitoring devices can help to better characterize sleep efficiency and sleep quality. They can also be helpful to better characterize paroxysmal nocturnal events and differentiate between nocturnal seizures, parasomnias, and obstructive sleep apnea, each of which has a different management. Overnight ambulatory detection devices allow for a tolerable, low cost, objective assessment of sleep quality in the patient’s natural environment. They can also be used as a notification system to allow for rapid recognition and prompt intervention of events like seizures. Optimal monitoring devices will be patient- and diagnosis-specific, but may include a combination of modalities such as ambulatory electroencephalograms, actigraphy, and pulse oximetry. We will summarize the current literature on ambulatory sleep devices for detecting sleep disorders in children with neurological diseases

Continuous Spikes and Waves during Sleep: Electroclinical Presentation and Suggestions for Management

Continuous spikes and waves during sleep (CSWS) is an epileptic encephalopathy characterized in most patients by (1) difficult to control seizures, (2) interictal epileptiform activity that becomes prominent during sleep leading to an electroencephalogram (EEG) pattern of electrical status epilepticus in sleep (ESES), and (3) neurocognitive regression. In this paper, we will summarize current epidemiological, clinical, and EEG knowledge on CSWS and will provide suggestions for treatment. CSWS typically presents with seizures around 2–4 years of age. Neurocognitive regression occurs around 5-6 years of age, and it is accompanied by subacute worsening of EEG abnormalities and seizures. At approximately 6–9 years of age, there is a gradual resolution of seizures and EEG abnormalities, but the neurocognitive deficits persist in most patients. The cause of CSWS is unknown, but early developmental lesions play a major role in approximately half of the patients, and genetic associations have recently been described. High-dose benzodiazepines and corticosteroids have been successfully used to treat clinical and electroencephalographic features. Corticosteroids are often reserved for refractory disease because of adverse events. Valproate, ethosuximide, levetiracetam, sulthiame, and lamotrigine have been also used with some success. Epilepsy surgery may be considered in a few selected patients

Risk Factors Associated with Death in In-Hospital Pediatric Convulsive Status Epilepticus

Objective: To evaluate in-patient mortality and predictors of death associated with convulsive status epilepticus (SE) in a large, multi-center, pediatric cohort. Patients and Methods: We identified our cohort from the KID Inpatient Database for the years 1997, 2000, 2003 and 2006. We queried the database for convulsive SE, associated diagnoses, and for inpatient death. Univariate logistic testing was used to screen for potential risk factors. These risk factors were then entered into a stepwise backwards conditional multivariable logistic regression procedure. P-values less than 0.05 were taken as significant. Results: We identified 12,365 (5,541 female) patients with convulsive SE aged 0–20 years (mean age 6.2 years, standard deviation 5.5 years, median 5 years) among 14,965,571 pediatric inpatients (0.08%). Of these, 117 died while in the hospital (0.9%). The most frequent additional admission ICD-9 code diagnoses in addition to SE were cerebral palsy, pneumonia, and respiratory failure. Independent risk factors for death in patients with SE, assessed by multivariate calculation, included near drowning (Odds ratio [OR] 43.2; Confidence Interval [CI] 4.4–426.8), hemorrhagic shock (OR 17.83; CI 6.5–49.1), sepsis (OR 10.14; CI 4.0–25.6), massive aspiration (OR 9.1; CI 1.8–47), mechanical ventilation >96 hours (OR9; 5.6–14.6), transfusion (OR 8.25; CI 4.3–15.8), structural brain lesion (OR7.0; CI 3.1–16), hypoglycemia (OR5.8; CI 1.75–19.2), sepsis with liver failure (OR 14.4; CI 5–41.9), and admission in December (OR3.4; CI 1.6–4.1). African American ethnicity (OR 0.4; CI 0.2–0.8) was associated with a decreased risk of death in SE. Conclusion: Pediatric convulsive SE occurs in up to 0.08% of pediatric inpatient admissions with a mortality of up to 1%. There appear to be several risk factors that can predict mortality. These may warrant additional monitoring and aggressive management