Distinct Slow-Wave Activity Patterns in Resting-State Electroencephalography and Their Relation to Language Functioning in Low-Grade Glioma and Meningioma Patients.

Introduction: Brain tumours frequently cause language impairments and are also likely to co-occur with localised abnormal slow-wave brain activity. However, it is unclear whether this applies specifically to low-grade brain tumours. We investigate slow-wave activity in resting-state electroencephalography (EEG) in low-grade glioma and meningioma patients, and its relation to pre- and postoperative language functioning. Method: Patients with a glioma (N = 15) infiltrating the language-dominant hemisphere and patients with a meningioma (N = 10) with mass effect on this hemisphere underwent extensive language testing before and 1 year after surgery. EEG was registered preoperatively, postoperatively (glioma patients only), and once in healthy individuals. Slow-wave activity in delta- and theta- frequency bands was evaluated visually and quantitatively by spectral power at three levels over the scalp: the whole brain, the affected hemisphere, and the affected region. Results: Glioma patients had increased delta activity (affected area) and increased theta activity (all levels) before and after surgery. In these patients, increased preoperative theta activity was related to the presence of language impairment, especially to poor word retrieval and grammatical performance. Preoperative slow-wave activity was also related to postoperative language outcomes. Meningioma patients showed no significant increase in EEG slow-wave activity compared to healthy individuals, but they presented with word retrieval, grammatical, and writing problems preoperatively, as well as with writing impairments postoperatively. Discussion: Although the brain-tumour pathology in low-grade gliomas and meningiomas has a different effect on resting-state brain activity, patients with low-grade gliomas and meningiomas both suffer from language impairments. Increased theta activity in glioma patients can be considered as a language-impairment marker, with prognostic value for language outcome after surgery.

Resting-State Electroencephalography Functional Connectivity Networks Relate to Pre- and Postoperative Language Functioning in Low-Grade Glioma and Meningioma Patients.

Introduction: Preservation of language functioning in patients undergoing brain tumor surgery is essential because language impairments negatively impact the quality of life. Brain tumor patients have alterations in functional connectivity (FC), the extent to which brain areas functionally interact. We studied FC networks in relation to language functioning in glioma and meningioma patients. Method: Patients with a low-grade glioma (N = 15) or meningioma (N = 10) infiltrating into/pressing on the language-dominant hemisphere underwent extensive language testing before and 1 year after surgery. Resting-state EEG was registered preoperatively, postoperatively (glioma patients only), and once in healthy individuals. After analyzing FC in theta and alpha frequency bands, weighted networks and Minimum Spanning Trees were quantified by various network measures. Results: Pre-operative FC network characteristics did not differ between glioma patients and healthy individuals. However, hub presence and higher local and global FC are associated with poorer language functioning before surgery in glioma patients and predict worse language performance at 1 year after surgery. For meningioma patients, a greater small worldness was related to worse language performance and hub presence; better average clustering and global integration were predictive of worse outcome on language function 1 year after surgery. The average eccentricity, diameter and tree hierarchy seem to be the network metrics with the more pronounced relation to language performance. Discussion: In this exploratory study, we demonstrated that preoperative FC networks are informative for pre- and postoperative language functioning in glioma patients and to a lesser extent in meningioma patients.

Automated EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study.

BACKGROUND: To improve the objective assessment of continuous video-EEG (cEEG) monitoring of neonatal brain function, the aim was to relate automated derived amplitude and duration parameters of the suppressed periods in the EEG background (dynamic Interburst Interval= dIBIs) after neonatal hypoxic-ischemic encephalopathy (HIE) to favourable or adverse neurodevelopmental outcome. METHODS: Nineteen neonates (gestational age 36-41 weeks) with HIE underwent therapeutic hypothermia and had cEEG-monitoring. EEGs were retrospectively analyzed with a previously developed algorithm to detect the dynamic Interburst Intervals. Median duration and amplitude of the dIBIs were calculated at 1 h-intervals. Sensitivity and specificity of automated EEG background grading for favorable and adverse outcomes were assessed at 6 h-intervals. RESULTS: Dynamic IBI values reached the best prognostic value between 18 and 24 h (AUC of 0.93). EEGs with dIBI amplitude ≥15 µV and duration <10 s had a specificity of 100% at 6-12 h for favorable outcome but decreased subsequently to 67% at 25-42 h. Suppressed EEGs with dIBI amplitude <15 µV and duration >10 s were specific for adverse outcome (89-100%) at 18-24 h (n = 10). Extremely low voltage and invariant EEG patterns were indicative of adverse outcome at all time points. CONCLUSIONS: Automated analysis of the suppressed periods in EEG of neonates with HIE undergoing TH provides objective and early prognostic information. This objective tool can be used in a multimodal strategy for outcome assessment. Implementation of this method can facilitate clinical practice, improve risk stratification and aid therapeutic decision-making. A multicenter trial with a quantifiable outcome measure is warranted to confirm the predictive value of this method in a more heterogeneous dataset.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Interrater agreement in visual scoring of neonatal seizures based on majority voting on a web-based system: The Neoguard EEG database.

OBJECTIVE: To assess interrater agreement based on majority voting in visual scoring of neonatal seizures. METHODS: An online platform was designed based on a multicentre seizure EEG-database. Consensus decision based on 'majority voting' and interrater agreement was estimated using Fleiss' Kappa. The influences of different factors on agreement were determined. RESULTS: 1919 Events extracted from 280h EEG of 71 neonates were reviewed by 4 raters. Majority voting was applied to assign a seizure/non-seizure classification. 44% of events were classified with high, 36% with moderate, and 20% with poor agreement, resulting in a Kappa value of 0.39. 68% of events were labelled as seizures, and in 46%, all raters were convinced about electrographic seizures. The most common seizure duration was <30s. Raters agreed best for seizures lasting 60-120s. There was a significant difference in electrographic characteristics of seizures versus dubious events, with seizures having longer duration, higher power and amplitude. CONCLUSIONS: There is a wide variability in identifying rhythmic ictal and non-ictal EEG events, and only the most robust ictal patterns are consistently agreed upon. Database composition and electrographic characteristics are important factors that influence interrater agreement. SIGNIFICANCE: The use of well-described databases and input of different experts will improve neonatal EEG interpretation and help to develop uniform seizure definitions, useful for evidence-based studies of seizure recognition and management.

Sleep Architecture Linked to Airway Obstruction and Intracranial Hypertension in Children with Syndromic Craniosynostosis.

BACKGROUND: Children with syndromic craniosynostosis often have obstructive sleep apnea and intracranial hypertension. The authors aimed to evaluate (1) sleep architecture, and determine whether this is influenced by the presence of obstructive sleep apnea and/or intracranial hypertension; and (2) the effect of treatment on sleep architecture. METHODS: This study included patients with syndromic craniosynostosis treated at a national referral center, undergoing screening for obstructive sleep apnea and intracranial hypertension. Obstructive sleep apnea was identified by polysomnography, and categorized into no, mild, moderate, or severe. Intracranial hypertension was identified by the presence of papilledema on funduscopy, supplemented by optical coherence tomography and/or intracranial pressure monitoring. Regarding sleep architecture, sleep was divided into rapid eye movement or non-rapid eye movement sleep; respiratory effort-related arousals and sleep efficiency were scored. RESULTS: The authors included 39 patients (median age, 5.9 years): 19 with neither obstructive sleep apnea nor intracranial hypertension, 11 with obstructive sleep apnea (four moderate/severe), six with intracranial hypertension, and three with obstructive sleep apnea and intracranial hypertension. Patients with syndromic craniosynostosis, independent of the presence of mild obstructive sleep apnea and/or intracranial hypertension, have normal sleep architecture compared with age-matched controls. Patients with moderate/severe obstructive sleep apnea have a higher respiratory effort-related arousal index (p < 0.01), lower sleep efficiency (p = 0.01), and less rapid eye movement sleep (p = 0.04). An improvement in sleep architecture was observed following monobloc surgery (n = 5; rapid eye movement sleep, 5.3 percent; p = 0.04). CONCLUSIONS: Children with syndromic craniosynostosis have in principle normal sleep architecture. However, moderate/severe obstructive sleep apnea does lead to disturbed sleep architecture, which fits within a framework of a unifying theory for obstructive sleep apnea, intracranial hypertension, and sleep. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, II.

Sirolimus for epilepsy in children with tuberous sclerosis complex: A randomized controlled trial.

OBJECTIVE: To investigate whether mammalian target of rapamycin complex 1 (mTORC1) inhibitors could reduce seizure frequency in children with tuberous sclerosis complex (TSC). METHODS: Due to slow inclusion rate, target inclusion of 30 children was not reached. Twenty-three children with TSC and intractable epilepsy (age 1.8-10.9 years) were randomly assigned (1:1) to open-label, add-on sirolimus treatment immediately or after 6 months. Sirolimus was titrated to trough levels of 5-10 ng/mL. Primary endpoint was seizure frequency change during the sixth month of sirolimus treatment. RESULTS: Intention-to-treat analysis showed sirolimus treatment resulted in 41% seizure frequency decrease (95% confidence interval [CI] -69% to +14%; p = 0.11) compared to the standard-care period. Per protocol analysis of 14 children who reached sirolimus target trough levels in the sixth sirolimus month showed a seizure frequency decrease of 61% (95% CI -86% to +6%; p = 0.06). Cognitive development did not change. All children had adverse events. Five children discontinued sirolimus prematurely. CONCLUSIONS: We describe a randomized controlled trial for a non-antiepileptic drug that directly targets a presumed causal mechanism of epileptogenesis in a genetic disorder. Although seizure frequency decreased, especially in children reaching target trough levels, we could not show a significant benefit. Larger trials or meta-analyses are needed to investigate if patients with TSC with seizures benefit from mTORC1 inhibition. This trial was registered at trialregister.nl (NTR3178) and supported by the Dutch Epilepsy Foundation. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that sirolimus does not significantly reduce seizure frequency in children with TSC and intractable epilepsy. The study lacked the precision to exclude a benefit from sirolimus.

Routine vs extended outpatient EEG for the detection of interictal epileptiform discharges.

OBJECTIVE: To compare the yield of epileptiform abnormalities on 30-minute recordings with those greater than 45 minutes. METHODS: We performed a prospective observational cross-sectional study of all outpatient routine EEGs comparing the rate of interictal epileptiform discharges (IEDs) and clinical events during the initial 30 minutes (routine) with those occurring in the remaining 30-60 minutes (extended). A relative increase of 10% was considered clinically significant. RESULTS: EEGs from 1,803 patients were included; overall EEG duration was 59.4 minutes (SD ±6.5). Of 426 patients with IEDs at any time during the EEG, 81 (19.1%, 95% confidence interval 15.6-23) occurred only after the initial 30 minutes. The rate of late IEDs was not associated with age, indication, IED type, or sleep deprivation. Longer recording times also increased event capture rate by approximately 30%. CONCLUSIONS: The yield of IED and event detection is increased in extended outpatient EEGs compared to 30-minute studies.

Improving Reliability of Monitoring Background EEG Dynamics in Asphyxiated Infants.

The goal of this study is to develop an automated algorithm to quantify background electroencephalography (EEG) dynamics in term neonates with hypoxic ischemic encephalopathy. The recorded EEG signal is adaptively segmented and the segments with low amplitudes are detected. Next, depending on the spatial distribution of the low-amplitude segments, the first part of the algorithm detects (dynamic) interburst intervals (dIBIs) and performs well on the relatively artifact-free EEG periods and well-defined burst-suppression EEG periods. However, on testing the algorithm on EEG recordings of more than 48 h per neonate, a significant number of misclassified and dubious detections were encountered. Therefore, as the next step, we applied machine learning classifiers to differentiate between definite dIBI detections and misclassified ones. The developed algorithm achieved a true positive detection rate of 98%, 97%, 88%, and 95% for four duration-related dIBI groups that we subsequently defined. We benchmarked our algorithm with an expert diagnostic interpretation of EEG periods (1 h long) and demonstrated its effectiveness in clinical practice. We show that the detection algorithm effectively discriminates challenging cases encountered within mild and moderate background abnormalities. The dIBI detection algorithm improves identification of neonates with good clinical outcome as compared to the classification based on the classical burst-suppression interburst interval.

Holistic approach for automated background EEG assessment in asphyxiated full-term infants.

OBJECTIVE: To develop an automated algorithm to quantify background EEG abnormalities in full-term neonates with hypoxic ischemic encephalopathy. APPROACH: The algorithm classifies 1 h of continuous neonatal EEG (cEEG) into a mild, moderate or severe background abnormality grade. These classes are well established in the literature and a clinical neurophysiologist labeled 272 1 h cEEG epochs selected from 34 neonates. The algorithm is based on adaptive EEG segmentation and mapping of the segments into the so-called segments' feature space. Three features are suggested and further processing is obtained using a discretized three-dimensional distribution of the segments' features represented as a 3-way data tensor. Further classification has been achieved using recently developed tensor decomposition/classification methods that reduce the size of the model and extract a significant and discriminative set of features. MAIN RESULTS: Effective parameterization of cEEG data has been achieved resulting in high classification accuracy (89%) to grade background EEG abnormalities. SIGNIFICANCE: For the first time, the algorithm for the background EEG assessment has been validated on an extensive dataset which contained major artifacts and epileptic seizures. The demonstrated high robustness, while processing real-case EEGs, suggests that the algorithm can be used as an assistive tool to monitor the severity of hypoxic insults in newborns.

Clinical reasoning: a 76-year-old man remaining comatose after cardiopulmonary resuscitation.

A 76-year-old man was admitted to the hospital after having a "cardiac arrest" while riding his bicycle and subsequently falling into a canal. Thirty minutes after the accident, he was resuscitated by an ambulance crew. They detected a ventricular tachycardia, which responded to defibrillation. Thereafter, there was a sinus bradycardia, which was treated with atropine and adrenaline. After 30 minutes of resuscitation, there was return of spontaneous circulation (ROSC). At admission to our hospital, the patient was comatose, with a body temperature of 30°C and Glasgow Coma Scale score (GCS) of E1M1V1. According to protocol for presumed acute hypoxic ischemic encephalopathy, he was started on therapeutic hypothermia. The body temperature of the patient was kept at 32°C to 34°C for 24 hours. A few hours after starting hypothermia, twitches around the eyes and mouth were noticed and a neurology consultation was requested. Neurologic examination during hypothermia and under sedation showed a deeply comatose patient with intact pupillary reaction to light bilaterally and present oculocephalic reflex. He had multifocal random twitching movements involving the face, arms, and legs. These shock-like movements were found to increase by external stimuli.

The puzzle of apparent life-threatening events in a healthy newborn.

Apneic neonatal seizures can present as apparent life-threatening events. We report a newborn with unexplained episodes of apnea associated with cyanosis and desaturation, starting on the first day postpartum. Biochemical tests were normal. Central nervous system infections as well as abnormalities of upper airways and cardiovascular system were excluded. Brain monitoring using amplitude-integrated electroencephalography (aEEG) was inconclusive. Continuous monitoring using video EEG revealed epileptic seizures originating from the left temporal region as the cause of the apneas. Magnetic resonance imaging (MRI) of the brain showed a developmental malformation of the left frontal and temporal lobes. The patient became seizure free after treatment with antiepileptic medication. This report illustrates that brain monitoring using amplitude-integrated EEG alone could miss focal neonatal seizures. When clinical suspicion of apneic seizures is high in infants with apparent life threatening events, multichannel polygraphic video-EEG monitoring is indicated. Prompt diagnosis and treatment can be life saving.

Heart rate variability in newborns with hypoxic brain injury.

In neonatal intensive care units, there is a need for continuous monitoring of sick newborns with perinatal hypoxic ischemic brain injury (HIE). We assessed the utility of heart rate variability (HRV) in newborns with acute HIE undergoing simultaneous continuous EEG (cEEG) and ECG monitoring. HIE was classified using clinical criteria as well as visual grading of cEEG. Newborns were divided into two groups depending on the severity of the hypoxic injury and outcome. Various HRV parameters were compared between these groups, and significantly decreased HRV was found in neonates with severe HIE. As HRV is affected by many factors, it is difficult to attribute this difference solely to HIE. However, this study suggests that further investigation of HRV as a monitoring tool for acute neonatal hypoxic injury is warranted.

Relationship of EEG sources of neonatal seizures to acute perinatal brain lesions seen on MRI: a pilot study.

Even though it is known that neonatal seizures are associated with acute brain lesions, the relationship of electroencephalographic (EEG) seizures to acute perinatal brain lesions visible on magnetic resonance imaging (MRI) has not been objectively studied. EEG source localization is successfully used for this purpose in adults, but it has not been sufficiently explored in neonates. Therefore, we developed an integrated method for ictal EEG dipole source localization based on a realistic head model to investigate the utility of EEG source imaging in neonates with postasphyxial seizures. We describe here our method and compare the dipole seizure localization results with acute perinatal lesions seen on brain MRI in 10 full-term infants with neonatal encephalopathy. Through experimental studies, we also explore the sensitivity of our method to the electrode positioning errors and the variations in neonatal skull geometry and conductivity. The localization results of 45 focal seizures from 10 neonates are compared with the visual analysis of EEG and MRI data, scored by expert physicians. In 9 of 10 neonates, dipole locations showed good relationship with MRI lesions and clinical data. Our experimental results also suggest that the variations in the used values for skull conductivity or thickness have little effect on the dipole localization, whereas inaccurate electrode positioning can reduce the accuracy of source estimates. The performance of our fused method indicates that ictal EEG source imaging is feasible in neonates and with further validation studies, this technique can become a useful diagnostic tool.

Somatosensory evoked potentials are of additional prognostic value in certain patterns of brain injury in term birth asphyxia.

OBJECTIVE: (a) To relate MRI patterns of brain injury to somatosensory evoked potentials (SEPs), and (b) to determine the prognostic value of SEPs in addition to continuous EEG monitoring (cEEG) and cerebral imaging, in term asphyxiated newborns. METHODS: Fifty one consecutive neonates were studied. Survivors were followed for at least 2 years. cEEG, started within 24h, was done for ≥ 24 h and scored. SEPs and MRIs were performed in the first week. Brain injury patterns were classified. RESULTS: Bilaterally abnormal SEPs had a sensitivity of 90% (28/31) and specificity of 85% (17/20) in predicting a poor outcome, defined as death or severe handicap. SEPs were of particular value in predicting outcome in isolated symmetrical white matter injury and predicting the development of hemiparesis in isolated asymmetrical watershed injury. Binary logistic regression analysis revealed a significant relation to outcome separately for cEEG, deep grey matter injury on MRI and SEPs. SEPs provided additional value when added to cEEG and MRI in the model (p=0.034). CONCLUSIONS: SEPs are of additional prognostic value after term birth asphyxia. SIGNIFICANCE: In certain patterns of postasphyxial neonatal brain injury like asymmetrical watershed lesions and symmetrical white matter injury, EPs are complementary to information obtained from cEEG and MRI for prognostication.

Automated EEG inter-burst interval detection in neonates with mild to moderate postasphyxial encephalopathy.

EEG inter-burst interval (IBI) and its evolution is a robust parameter for grading hypoxic encephalopathy and prognostication in newborns with perinatal asphyxia. We present a reliable algorithm for the automatic detection of IBIs. This automated approach is based on adaptive segmentation of EEG, classification of segments and use of temporal profiles to describe the global distribution of EEG activity. A pediatric neurologist has blindly scored data from 8 newborns with perinatal postasphyxial encephalopathy varying from mild to severe. 15 minutes of EEG have been scored per patient, thus totaling 2 hours of EEG that was used for validation. The algorithm shows good detection accuracy and provides insight into challenging cases that are difficult to detect.

Technical standards for recording and interpretation of neonatal electroencephalogram in clinical practice.

Neonatal electroencephalogram (EEG), though often perceived as being difficult to record and interpret, is relatively easy to study due to the immature nature of the brain, which expresses only a few well-defined set of patterns. The EEG interpreter needs to be aware of the maturational changes as well as the effect of pathological processes and medication on brain activity. It gives valuable information for the treatment and prognostication in encephalopathic neonates. In this group, serial EEGs or EEG monitoring often gives additional information regarding deterioration/improvement of the brain function or occurrence of seizures.