颅内电极在感觉运动区皮质发育不良癫痫手术中的应用

目的:探讨长程颅内电极监测及电刺激方法,在感觉运动区皮质发育不良的难治性癫痫外科手术评估中的意义。方法:筛选MRI提示的皮质发育不良区域与重要功能区-感觉运动区位置关系密切的11例难治性癫痫患者,且头皮长程视频脑电监测及PET检查也初步提示癫痫发作与皮质发育不良所在脑区有关,在可疑脑区放置颅内电极,然后进行颅内电极长程视频脑电监测及电刺激检测,对癫痫起源位置及功能区定位,明确癫痫发作起源区域与感觉运动功能区的解剖学关系,在定位结果指导下进行切除术。结果:11例中3例位于左侧半球,8例位于右侧半球,11例感觉运动功能区皮质分布均存在不同程度变异,7例癫痫发作起源区域与感觉运动功能区一定范围重叠,其中5例与感觉区重叠,该5例切除了起源区域与发作有关的部分感觉区,2例部分致痫灶与运动区重叠,该2例仅切除了除与发作有关的运动区以外的癫痫起源区域,4例癫痫发作起源区域与感觉运动功能区相对独立,该4例完全切除癫痫发作起源区域;手术后6例患者发作消失,2例患者发作频率减少90%以上,1例癫痫发作控制无效,2例患者发生部分感觉缺失,但对生活无明显影响。结论:在皮质发育不良的癫痫患者中,有较高比例的病人伴有功能区皮层分布的变异,长程颅内电极监测及电刺激能够实现癫痫起源区域及功能区精确定位,明确功能区变异情况,对于指导病灶切除,避免损伤功能区皮质,减少术后并发症具有重要意义。     

颅内电极在感觉运动区皮质发育不良癫痫手术中的应用

目的：探讨长程颅内电极监测及电刺激方法,在感觉运动区皮质发育不良的难治性癫痫外科手术评估中的意义。方法：筛选MRI提示的皮质发育不良区域与重要功能区-感觉运动区位置关系密切的11例难治性癫痫患者,且头皮长程视频脑电监测及PET检查也初步提示癫痫发作与皮质发育不良所在脑区有关,在可疑脑区放置颅内电极,然后进行颅内电极长程视频脑电监测及电刺激检测,对癫痫起源位置及功能区定位,明确癫痫发作起源区域与感觉运动功能区的解剖学关系,在定位结果指导下进行切除术。结果：11例中3例位于左侧半球,8例位于右侧半球,11例感觉运动功能区皮质分布均存在不同程度变异,7例癫痫发作起源区域与感觉运动功能区一定范围重叠,其中5例与感觉区重叠,该5例切除了起源区域与发作有关的部分感觉区,2例部分致痫灶与运动区重叠,该2例仅切除了除与发作有关的运动区以外的癫痫起源区域,4例癫痫发作起源区域与感觉运动功能区相对独立,该4例完全切除癫痫发作起源区域;手术后6例患者发作消失,2例患者发作频率减少90%以上,1例癫痫发作控制无效,2例患者发生部分感觉缺失,但对生活无明显影响。结论：在皮质发育不良的癫痫患者中,有较高比例的病人伴有功能区皮层分布的变异,长程颅内电极监测及电刺激能够实现癫痫起源区域及功能区精确定位,明确功能区变异情况,对于指导病灶切除,避免损伤功能区皮质,减少术后并发症具有重要意义。     

Epileptic seizures may begin hours in advance of clinical onset: a report of five patients

Mechanisms underlying seizure generation are traditionally thought to act over seconds to minutes before clinical seizure onset. We analyzed continuous 3- to 14-day intracranial EEG recordings from five patients with mesial temporal lobe epilepsy obtained during evaluation for epilepsy surgery. We found localized quantitative EEG changes identifying prolonged bursts of complex epileptiform discharges that became more prevalent 7 hr before seizures and highly localized subclinical seizure-like activity that became more frequent 2 hr prior to seizure onset. Accumulated energy increased in the 50 min before seizure onset, compared to baseline. These observations, from a small number of patients, suggest that epileptic seizures may begin as a cascade of electrophysiological events that evolve over hours and that quantitative measures of preseizure electrical activity could possibly be used to predict seizures far in advance of clinical onset.     

Analysis of Epileptic Discharges from Implanted Subdural Electrodes in Patients with Sturge-Weber Syndrome

ObjectiveAlmost two-thirds of patients with Sturge-Weber syndrome (SWS) have epilepsy, and half of them require surgery for it. However, it is well known that scalp electroencephalography (EEG) does not demonstrate unequivocal epileptic discharges in patients with SWS. Therefore, we analyzed interictal and ictal discharges from intracranial subdural EEG recordings in patients treated surgically for SWS to elucidate epileptogenicity in this disorder.MethodsFive intractable epileptic patients with SWS who were implanted with subdural electrodes for presurgical evaluation were enrolled in this study. We examined the following seizure parameters: seizure onset zone (SOZ), propagation speed of seizure discharges, and seizure duration by visual inspection. Additionally, power spectrogram analysis on some frequency bands at SOZ was performed from 60 s before the visually detected seizure onset using the EEG Complex Demodulation Method (CDM).ResultsWe obtained 21 seizures from five patients for evaluation, and all seizures initiated from the cortex under the leptomeningeal angioma. Most of the patients presented with motionless staring and respiratory distress as seizure symptoms. The average seizure propagation speed and duration were 3.1 ± 3.6 cm/min and 19.4 ± 33.6 min, respectively. Significant power spectrogram changes at the SOZ were detected at 10–30 Hz from 15 s before seizure onset, and at 30–80 Hz from 5 s before seizure onset.SignificanceIn patients with SWS, seizures initiate from the cortex under the leptomeningeal angioma, and seizure propagation is slow and persists for a longer period. CDM indicated beta to low gamma-ranged seizure discharges starting from shortly before the visually detected seizure onset. Our ECoG findings indicate that ischemia is a principal mechanism underlying ictogenesis and epileptogenesis in SWS.     

Abnormal interictal gamma activity may manifest a seizure onset zone in temporal lobe epilepsy

Even though recent studies have suggested that seizures do not occur suddenly and that before a seizure there is a period with an increased probability of seizure occurrence, neurophysiological mechanisms of interictal and pre-seizure states are unknown. The ability of mathematical methods to provide much more sensitive tools for the detection of subtle changes in the electrical activity of the brain gives promise that electrophysiological markers of enhanced seizure susceptibility can be found even during interictal periods when EEG of epilepsy patients often looks 'normal'. Previously, we demonstrated in animals that hippocampal and neocortical gamma-band rhythms (30-100 Hz) intensify long before seizures caused by systemic infusion of kainic acid. Other studies in recent years have also drawn attention to the fast activity (>30 Hz) as a possible marker of epileptogenic tissue. The current study quantified gamma-band activity during interictal periods and seizures in intracranial EEG (iEEG) in 5 patients implanted with subdural grids/intracranial electrodes during their pre-surgical evaluation. In all our patients, we found distinctive (abnormal) bursts of gamma activity with a 3 to 100 fold increase in power at gamma frequencies with respect to selected by clinicians, quiescent, artifact-free, 7-20 min "normal" background (interictal) iEEG epochs 1 to 14 hours prior to seizures. Increases in gamma activity were largest in those channels which later displayed the most intensive electrographic seizure discharges. Moreover, location of gamma-band bursts correlated (with high specificity, 96.4% and sensitivity, 83.8%) with seizure onset zone (SOZ) determined by clinicians. Spatial localization of interictal gamma rhythms within SOZ suggests that the persistent presence of abnormally intensified gamma rhythms in the EEG may be an important tool for focus localization and possibly a determinant of epileptogenesis.     

Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using Multivariate Statistical Process Control

An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose a model which can predict seizures well in advance of its occurrence. Multivariate statistical process control (MSPC) has been used for seizure predictions in long-term scalp EEG signal. It has been observed that excessive neuronal activity in the preictal period of seizure changes the electrical characteristic from chaotic to rhythmic behavior. These changes have been utilized for prediction. Eight temporal based features are used for predicting the seizures by using multivariate statistical process control, which is widely known as an anomaly monitoring method. 90 seizures from the CHB-MIT EEG data of ten patients are analyzed.ResultThe results of the proposed method demonstrated that 80 seizures out of 90 in preictal period were correctly predicted prior to the seizure onset, thereby giving a sensitivity of 88.89%. The false positive rate is observed to 0.39 per hour.ConclusionThis study proposed a temporal based patient-specific epileptic seizure prediction method using MSPC in long-term scalp EEG signals. It also provides the possibility of realizing an EEG-based epileptic seizure prediction system which requires less computational power.SignificanceThe proposed method does not require preictal data for modeling. The extracted features are computationally easy. The tested result shows good accuracy on the CHB-MIT data base.     

Dynamic Imaging of Coherent Sources Reveals Different Network Connectivity Underlying the Generation and Perpetuation of Epileptic Seizures

The concept of focal epilepsies includes a seizure origin in brain regions with hyper synchronous activity (epileptogenic zone and seizure onset zone) and a complex epileptic network of different brain areas involved in the generation, propagation, and modulation of seizures. The purpose of this work was to study functional and effective connectivity between regions involved in networks of epileptic seizures. The beginning and middle part of focal seizures from ictal surface EEG data were analyzed using dynamic imaging of coherent sources (DICS), an inverse solution in the frequency domain which describes neuronal networks and coherences of oscillatory brain activities. The information flow (effective connectivity) between coherent sources was investigated using the renormalized partial directed coherence (RPDC) method. In 8/11 patients, the first and second source of epileptic activity as found by DICS were concordant with the operative resection site; these patients became seizure free after epilepsy surgery. In the remaining 3 patients, the results of DICS / RPDC calculations and the resection site were discordant; these patients had a poorer post-operative outcome. The first sources as found by DICS were located predominantly in cortical structures; subsequent sources included some subcortical structures: thalamus, Nucl. Subthalamicus and cerebellum. DICS seems to be a powerful tool to define the seizure onset zone and the epileptic networks involved. Seizure generation seems to be related to the propagation of epileptic activity from the primary source in the seizure onset zone, and maintenance of seizures is attributed to the perpetuation of epileptic activity between nodes in the epileptic network. Despite of these promising results, this proof of principle study needs further confirmation prior to the use of the described methods in the clinical praxis.     

Seizure source localization using a hybrid second order blind identification and extended rival penalized competitive learning algorithm

Localization of seizure sources prior to neurosurgery is crucial. In this paper, a new method is proposed to localize the seizure sources from multi-channel electroencephalogram (EEG) signals. Blind source separation based on second order blind identification (SOBI) is primarily applied to estimate the brain source signals in each window of the EEG signals. A new clustering method based on rival penalized competitive learning (RPCL) is then developed to cluster the rows of the estimated unmixing matrices in all the windows. The algorithm also includes pre and post-processing stages. By multiplying each cluster center to the EEG signals, the brain signal sources are approximated. According to a complexity value measure, the main seizure source signal is separated from the others. This signal is projected back to the electrodes’ space and is subjected to the dipole source localization using a single dipole model. The simulation results verify the accuracy of the system. In addition, correct localization of the seizure source is consistent with the clinical tests derived using the simultaneous intracranial recordings.     

Detection of Seizure Event and Its Onset/Offset Using Orthonormal Triadic Wavelet Based Features

Background

Epileptic seizures are unpredictable in nature and its quick detection is important for immediate treatment of patients. In last few decades researchers have proposed different algorithms for onset and offset detection of seizure using Electroencephalogram (EEG) signals.

基于脑电信号的癫痫发作预测特征及识别

解码癫痫发作前脑电信号的神经元集群异常痫样放电活动,对癫痫发作进行有效预测并实施病前干预,可显著减少疾病病损,是癫痫防治的研究热点之一。基于脑电信号的癫痫发作预测研究关键在于发作间期和前期的异常状态识别,研究上述两状态间的神经动力学特征差异对明确癫痫发病机制、选取高分辨特征,进而有效识别该渐进性疾病所处的发作阶段具有重要价值。目前,研究者已对当前主流特征提取及模式识别方法进行了充分的调研梳理,但忽视了神经动态特征变化对于癫痫发作预测的重要意义。基于此,本文归纳总结了5类典型的发作预测特征分析方法及其优缺点,重点剖析了发作间期至前期神经生理特征的动态变化及其动力学特性,类比分析了当前该领域主流的机器学习和深度学习特征识别方法,以期为进一步建立精准、高效的癫痫发作预测技术提供新思路。     

Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy

Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of “cortico-thalamo-cortical network” in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.     

Increase trend of correlation and phase synchrony of microwire iEEG before macroseizure onset

Micro/macrowire intracranial EEG (iEEG) signals recorded from implanted micro/macroelectrodes in epileptic patients have received great attention and are considered to include much information of neuron activities in seizure transition compared to scalp EEG from cortical electrodes. Microelectrode is contacted more close to neurons than macroelectrode and it is more sensitive to neuron activity changes than macroelectrode. Microwire iEEG recordings are inevitably advantageous over macrowire iEEG recordings to reveal neuronal mechanisms contributing to the generation of seizures. In this study, we investigate the seizure generation from microwire iEEG recordings and discuss synchronization of microwire iEEGs in four frequency bands: alpha (1−30 Hz), gamma (30−80 Hz), ripple (80–250 Hz), and fast ripple (>250 Hz) via two measures: correlation and phase synchrony. We find that an increase trend of correlation or phase synchrony exists before the macroseizure onset mostly in gamma and ripple bands where the duration of the preictal states varied in different seizures ranging up to a few seconds (minutes). This finding is contrast to the well-known result that a decrease of synchronization in macro domains exists before the macroseizure onset. The finding demonstrates that it is only when the seizure has recruited enough surrounding brain tissue does the signal become strong enough to be observed on the clinical macroelectrode and as a result support the hypothesis of progressive coalescence of microseizure domains. The potential ramifications of such an early detection of microscale seizure activity may open a new window on treatment by making possible disruption of seizure activity before it becomes fully established.     

Widespread EEG Changes Precede Focal Seizures

The process by which the brain transitions into an epileptic seizure is unknown. In this study, we investigated whether the transition to seizure is associated with changes in brain dynamics detectable in the wideband EEG, and whether differences exist across underlying pathologies. Depth electrode ictal EEG recordings from 40 consecutive patients with pharmacoresistant lesional focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Predefined EEG sections were selected immediately before (immediate preictal), and 30 seconds before the earliest EEG sign suggestive of seizure activity (baseline). Spectral analysis, visual inspection and discrete wavelet transform were used to detect standard (delta, theta, alpha, beta and gamma) and high-frequency bands (ripples and fast ripples). At the group level, each EEG frequency band activity increased significantly from baseline to the immediate preictal section, mostly in a progressive manner and independently of any modification in the state of vigilance. Preictal increases in each frequency band activity were widespread, being observed in the seizure-onset zone and lesional tissue, as well as in remote regions. These changes occurred in all the investigated pathologies (mesial temporal atrophy/sclerosis, local/regional cortical atrophy, and malformations of cortical development), but were more pronounced in mesial temporal atrophy/sclerosis. Our findings indicate that a brain state change with distinctive features, in the form of unidirectional changes across the entire EEG bandwidth, occurs immediately prior to seizure onset. We postulate that these changes might reflect a facilitating state of the brain which enables a susceptible region to generate seizures.     

Effect of Immunotherapy on Seizure Outcome in Patients with Autoimmune Encephalitis: A Prospective Observational Registry Study

Objective

To evaluate the seizure characteristics and outcome after immunotherapy in adult patients with autoimmune encephalitis (AE) and new-onset seizure.

Methods

Adult (age ≥18 years) patients with AE and new-onset seizure who underwent immunotherapy and were followed-up for at least 6 months were included. Seizure frequency was evaluated at 2–4 weeks and 6 months after the onset of the initial immunotherapy and was categorized as “seizure remission”, “> 50% seizure reduction”, or “no change” based on the degree of its decrease.

Results

Forty-one AE patients who presented with new-onset seizure were analysed. At 2–4 weeks after the initial immunotherapy, 51.2% of the patients were seizure free, and 24.4% had significant seizure reduction. At 6 months, seizure remission was observed in 73.2% of the patients, although four patients died during hospitalization. Rituximab was used as a second-line immunotherapy in 12 patients who continued to have seizures despite the initial immunotherapy, and additional seizure remission was achieved in 66.6% of them. In particular, those who exhibited partial response to the initial immunotherapy had a better seizure outcome after rituximab, with low adverse events.

Conclusion

AE frequently presented as seizure, but only 18.9% of the living patients suffered from seizure at 6 months after immunotherapy. Aggressive immunotherapy can improve seizure outcome in patients with AE.     

磁休克治疗对重度抑郁症患者脑功能网络的影响

目的 磁休克治疗(MST)是一种新兴的神经调节干预技术,在重度抑郁症(MDD)治疗中得到广泛应用,然而其抗抑郁机制尚不清楚。探索MST对不同疗效MDD患者脑功能网络的调控作用,对MST治疗的抗抑郁作用机制研究具有重要意义。方法 本文对18例MDD患者MST作用前后的静息态脑电进行记录,基于皮尔逊相关方法构建脑功能网络;应用复杂网络理论对比分析脑功能网络拓扑结构的改变;根据HDRS-17评分变化率进一步区分治疗有效组和无效组,对网络特征进行二次比较。结果 MST后,MDD患者的脑功能网络平均节点度、平均聚类系数和平均全局效率值均比治疗前显著升高,平均路径长度值显著降低,小世界属性显著增大;相比于治疗无效组,治疗有效组的脑功能网络特征参数变化量更大。结论 MST显著改变了MDD患者脑功能网络拓扑结构,对患者的脑功能网络具有一定的调制作用,这些结果为MST治疗的抗抑郁机制研究提供了实验支持和理论依据。     

The Causal Inference of Cortical Neural Networks during Music Improvisations

We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and “let-go” mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and “let-go” mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.     

Nonlinear analysis of EEG signals: Surrogate data analysis

ObjectivesThe electroencephalogram (EEG) signal contains information about the state and condition of the brain. The aim of the study is to conduct a nonlinear analysis of the EEG signals and to compare the differences in the nonlinear characteristics of the EEG during normal state and the epileptic state.DataThe EEG data used for this study – which consisted of epileptic EEG and normal EEG – were obtained from the EEG database available with the Bonn University, Germany.ResultsThe attractors seen in normal and epileptic human brain dynamics were studied and compared. Surrogate data analyses were conducted on two nonlinear measures, namely the largest Lyapunov exponent and the correlation dimension, to test the hypothesis whether EEG signals were in accordance with linear stochastic models.DiscussionsThe existence of deterministic chaos in brain activity is confirmed by the existence of a chaotic attractor; also, saturation of the correlation dimension towards a definite value is the manifestation of a deterministic dynamics. Also a reduction is observed between the dimensionalities of the brain attractors from normal state to the epileptic state. The evaluation of the largest Lyapunov exponent also confirms the lowering of complexity during an episode of seizure.ConclusionIn case of Lyapunov exponent of EEG data, the change due to surrogating is small suggesting that it is not representing the system complexity properly but there is a marked change in the case of correlation dimension value due to surrogating.     

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in different areas of their brain for the localization of their seizure focus and eloquent areas. After implantation, the patient must remain in the hospital until the pathological area of brain is found and possibly resected. During this time, these patients offer a unique opportunity to the research community because any number of behavioral paradigms can be performed to uncover the neural correlates that guide behavior. Here we present a method for recording brain activity from intracranial implants as subjects perform a behavioral task designed to assess decision-making and reward encoding. All electrophysiological data from the intracranial electrodes are recorded during the behavioral task, allowing for the examination of the many brain areas involved in a single function at time scales relevant to behavior. Moreover, and unlike animal studies, human patients can learn a wide variety of behavioral tasks quickly, allowing for the ability to perform more than one task in the same subject or for performing controls. Despite the many advantages of this technique for understanding human brain function, there are also methodological limitations that we discuss, including environmental factors, analgesic effects, time constraints and recordings from diseased tissue. This method may be easily implemented by any institution that performs intracranial assessments; providing the opportunity to directly examine human brain function during behavior.     

A Physiology-Based Seizure Detection System for Multichannel EEG

Background

Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.

Methodology

This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching.

Principal Findings

We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection.

Conclusion

We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.     

Symptomatic epilepsy associated with intracranial calcifications in children with acute lymphoblastic leukemia (ALL)

Acute and long-term sequels of central nervous system (CNS) prophylaxis with irradiation and intrathecal chemotherapy in children suffering from acute lymphoblastic leukemia (ALL) include vasculopathies, leucoencephalopathies, intracranial calcifications, intellectual and neurological impairment. We report two children at the age 5 and 8 years who manifested partial motor or complex seizures and intracranial calcifications 2-4 years after the diagnosis of ALL had been established. The occurrence of these disorders was much earlier than reported in the literature. Both children received prophylactic CNS treatment with irradiation and intrathecal methotrexate (MTX). Their brain CT scans and EEG had been normal before the first epileptic seizure was registered. Children are now seizure free on carbamazepine, and a boy with complex partial and myoclonic seizures is also on valproate and vigabatrine. Symptomatic epilepsy associated with intracranial calcifications and persisting EEG changes might occur as side effects of ALL treatment.