CO中毒继发癫痫的脑电图和经颅多普勒改变的关系研究

目的:探讨一氧化碳(Carbon monoxide,CO)中毒继发癫痫的经颅多普勒(Transcranial Doppler,TCD)和脑电图(Electroencephalogram,EEG)特征的关系。方法:对我院自2011年6月至2015年5月收治的145例CO中毒患者的临床资料和EEG、TCD特征进行检查,并分析其与继发癫痫的相关性。结果:145例CO中毒患者中,EEG结果显示正常患者93例(64.13%);异常患者52例(35.86%)。TCD正常患者90例(62.07%),异常55例(37.93%)。主要表现为颈内动脉系统血流动力学的改变,大脑前动脉流速与病情严重程度成正相关,TCD血流速度增加或减慢不对称,脑血流速度升高比率显著高于脑血流速度减低比率。无论在睡眠期还是清醒期EEG异常患者全导阵发性3.0~4.0Hz的棘慢波均存在放电。患者出现EEG、TCD异常与年龄、癫痫家族史、发热温度、持续时间、继发癫痫类型以及24h内发作次数有关。继发癫痫39例,占EEG异常患儿的75.00%,占TCD异常患儿的72.73%。继发癫痫患者中,15例额区放电日后发生发生癫痫14例(93.33%),19例枕区放电16例(84.21%)发生癫痫,13例Rolandic区放电8例(61.54%)发生癫痫,5例广泛性棘慢波者继发癫痫的有1例(20%)。结论:EEG、TCD检查异常的CO中毒患者中具有较高的继发癫痫的发病率,中枕区和额区阵发性异常放电者更容易继发癫痫。     

癫痫模型及致痫机制的探讨

青霉素致痫所引起的阵发性除极转变有突触活动增强及自发放电两种学说。在体脑组织癫痫发作有周围抑制、高度同步化及回放等特点。全身性青霉素癫痫发作与失神小发作相似,其棘慢波的产生与丘脑有关。点燃效应作为慢性癫痫模型受到重视,其中产生机制有不同的看法。一些脑内递质如γ-氨基丁酸、去甲肾上腺素、5-羟色胺、阿片样物质及有关的脑区在致病中有一定的作用。     

匹罗卡品颞叶癫痫大鼠模型中尖波对theta节律的抑制作用

癫痫会降低患者的认知能力是常见的临床现象,但这是否与癫痫发生过程等因素有关尚不清楚。尖波是颞叶癫痫的主要特征,本文应用脑深部记录研究了癫痫发生过程中的散发性尖波(sporadic spikes,SSs)对认知节律theta的影响。实验记录4只注射匹罗卡品导致癫痫发生的大鼠在嗅行(exploration)时的CA1脑区的局部场电位(local field potentials,LFPs),计算癫痫发生过程的早期和晚期有尖波和无尖波期间theta节律的相位稳定规模和能量,并与注射前对照进行对比。应用混合动力学模型,改变CA1网络的平均兴奋性和抑制性(分别包括慢树突抑制回路和快胞体抑制回路)突触增益,仿真有尖波和无尖波的发作间期CA1区场电位,并据此计算theta节律的相位稳定规模。结果显示,癫痫尖波对theta节律的抑制作用既是短暂的也是持续的,但随癫痫发生过程而变化,早期作用强烈,晚期则平缓,但即便在早期也有很强的theta节律。仿真结果部分地证明了伴随癫痫尖波而出现的突触不平衡可能与theta相位稳定规模动态变化的规律有关。以上结果提示,癫痫尖波对theta节律的抑制影响是永久性的,与癫痫发生过程有关。     

术中皮质脑电图监测癫痫病灶切除13例分析

目的:探讨继发性癫痫术中运用皮质脑电图监测切除癫痫病灶的疗效。方法:对13例继发性癫痫患者术前经多次常规脑电图、24h动态脑电图检查定位并联合CT、MRI等检查结果,确定癫痫病灶的准确位置。在皮质脑电图精确定位监测下手术切除致痫灶。结果:13例癫痫患者均通过皮质脑电图监测,准确定位,切除致痫灶,切除病灶后的棘波、尖波,棘、尖慢复合波减少或完全消失。结论:利用皮质脑电图监测手术切除痫灶是治疗继发性癫痫最有效的方法之一。     

颞叶癫痫患者海马CA3和内嗅皮层网络的癫痫尖波对theta节律抑制作用的量化评估

海马CA3和内嗅皮层(entorhinal cortex,EC)网络是海马认知功能和颞叶癫痫研究的关键回路之一,尖波对海马网络theta(4~8 Hz)节律抑制作用的研究有利于揭示癫痫对认知功能影响的机制。以往,在网络层面上,该抑制作用常借助脑片来实现定量评估。本文旨在建立依赖于脑深度局部场电位评估癫痫尖波对theta节律抑制作用的方法。从4位术前处于快速眼球运动(rapid eyes movement,REM)睡眠下的颞叶癫痫患者皮质电极记录中择取发作间期有散发性尖波(sporadic spikes,SSs)脑电和两个相邻SSs间无尖波暂态期脑电,尖波分别只在CA3、只在EC、或在CA3和EC同步出现,应用Gabor小波和Hilbert变换计算尖波前后和无尖波暂态期theta能量,并计算无尖波暂态期theta节律的断裂程度。结果显示:(1)尖波可瞬时降低theta能量,CA3和EC同步尖波时下降最为剧烈,抑制作用最强;(2)无尖波暂态期theta能量下降,出现theta节律消失,造成节律断裂,表明抑制作用在持续,且断裂程度与尖波附近抑制作用一致;(3)3例患者无尖波暂态期theta能量水平降低程度与尖波附近抑制作用一致,而1例不一致。本文结果提示,SSs可对theta节律产生瞬时、直接的抑制作用,该抑制作用可在无尖波暂态期持续,并可由theta节律断裂程度反映。该工作首次应用局部场电位证明了癫痫尖波对theta节律抑制作用可通过无尖波时脑电节律的断裂程度来评估,为利用脑电衡量癫痫尖波抑制作用提供了量化分析方法。     

基于时频分析检测EEG中癫痫样棘/尖波的方法

提出了一种基于Ｃｈｏｉ－Ｗｉｌｌｉａｍｓ分布检测ＥＥＧ中癫痫样棘波／尖波的方法。该方法通过计算ＥＥＧ信号的时频分布,得到一段信号在各个时刻上沿频率方向上的能量分布。这种能量分布相当于一种瞬时频谱,反映了ＥＥＧ信号在局部时间范围里的波形特征。以一段ＥＥＧ信号在各个时刻的瞬时频谱的平均作为这段脑电的背景信号频谱,通过计算每一时刻的瞬时频谱与背景信号频谱之间的频谱差,检测这段信号中的棘波／尖波。对临床Ｅ     

丘脑网状核及其GABAB受体在癫痫小发作形成中的作用

目的和方法：实验选用ＳＤ大鼠戊四氮（ＰＴＺ）模型,结合电刺激或电解毁损丘脑网状核、丘脑接替核（丘脑腹后外侧核）、丘脑前核（丘脑前内侧核）,并用蝇蕈醇、氯苯氨丁酸、３疏基丙酸等药物在丘脑网状核内微量注射或腹腔注射后观察棘慢波的变化。结果：电刺激丘脑网状核可增强癫痫小发作,毁损丘脑网状核可抑制癫痫小发作,ＧＡＢＡＢ受体的激活不利于小发作的消除     

IL-1β、IL-lra对戊四氮致痫大鼠行为及皮层、海马脑电的影响

目的:了解白细胞介素1β(IL-1β)在癫痫发作中的作用.方法:采用记录脑电图(EEG)同时观察行为的方法,观察IL-1β和IL-1受体拮抗剂(IL-1ra) 侧脑室注射对戊四氮(PTZ)致痫大鼠行为和皮层、海马EEG的影响.结果:IL -1β能明显缩短 PTZ致大鼠急性惊厥发作及痫波发放的潜伏期,增加痫波的发放频率.IL -1ra能减少急性惊厥痫波发放频率,对急性惊厥发作及痫波发放的潜伏期和惊厥发作强度无明显影响.但IL-1ra能显著延长大鼠点燃后PTZ诱导的惊厥发作和痫波发放的潜伏期,减轻惊厥发作强度.结论:内源性IL-1β是促进癫痫发作的因素之一,可能在癫痫慢性发展中提高大脑神经元的兴奋性中起着重要作用.     

应用小波熵分析大鼠脑电信号的动态变化特性

应用小波熵（一种新的信号复杂度测量方法）分析大鼠在不同生理状态下脑电复杂度的动态时变特性。采用慢性埋植电极记录自由活动大鼠的皮层EEG,使用多分辨率小波变换将EEG信号分解为δ、θ、α和β四个分量,求得随时间变化的小波熵。结果表明：在清醒、慢波睡眠和快动眼睡眠三种生理状态下,EEG的小波熵之间存在显著差别,并且在不同时期其值与各个分解分量之间具有不同的关系,其中,慢波睡眠期小波熵还具有较明显的变化节律,反映了EEG微状态中慢波和纺锤波的互补性。由此可见,小波熵既能区别长时间段EEG复杂度之间的差别,又能反映EEG微状态的快速变化特性。     

癫痫发作间期alpha波的窄带相位同步分析

神经元同步化放电是癫痫发作的一个重要特征,作者提出并运用窄带相位同步技术对比分析了54个癫痫病人和10个正常成年人的脑电信号(EEG)数据.结果表明,相对于对照组,癫痫组的Alpha波的平均窄带相位同步值有显著下降(P=0.02058).为了更准确地刻画和衡量癫痫组和其对照组在同步模式上的差异,提出了一个新的具体的量化指标,即alpha波的窄带相位同步发散率.分析结果显示,对照组的窄带相位同步发散率明显低于癫痫组(P=0.003060),说明对照组的alpha波振子间互诱导强度更高.这可能反映了癫痫组窄带相位同步发散率的升高及所代表的alpha振子间互诱导强度的减弱与患者在癫痫发作间期的状态有密切的联系.     

Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology.     

Dynamics of epileptic seizures: Evolution, spreading, and suppression

Dynamical properties of epileptic seizures are investigated using a recent compact continuum model for electric activity of the brain. Large amplitude limit cycles resembling electroencephalograms during epilepsy emerge when the system loses linear stability. Seizures that are confined to an onset area, or spread synchronously to other areas via spatial coupling, are studied and argued to be associated with clinical partial and secondarily generalized seizures, respectively. Suppression of such seizures is also demonstrated, which implies potential for future clinical applications.     

Predicting Epileptic Seizures in Advance

Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the world''s population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance.     

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.     

Behavioral management of epileptic seizures following EEG biofeedback training of the sensorimotor rhythm

Eight severely epileptic patients, four males and four females, ranging in age from 10 to 29 years, were trained to increase 12–14 Hz EEG activity from the regions overlying the Rolandic area. This activity, the sensorimotor rhythm(SMR), has been hypothesized to be related to motor inhibitory processes(Sterman, 1974). The patients represented a crosssection of several different types of epilepsy, including grand mal, myoclonic, akinetic, focal, and psychomotor types. Three of them had varying degrees of mental retardation. SMR was detected by a combination of an analog filtering system and digital processing. Feedback, both auditory and/or visual, was provided whenever one-half second of 12–14-Hz activity was detected in the EEG. Patients were provided with additional feedback keyed by the output of a 4–7-Hz filter which indicated the presence of epileptiform spike activity, slow waves, or movement. Feedback for SMR was inhibited whenever slow-wave activity spikes or movement was also present. During the treatment period most of the patients showed varying degrees of improvement. Two of the patients who had been severely epileptic, having multiple seizures per week, have been seizure free for periods of up to 1 month. Other patients have developed the ability to block many of their seizures. Seizure intensity and duration have also decreased. Furthermore, the successful patients demonstrated an increase in the amount of SMR and an increase in amplitude of SMR during the training period. Spectral analyses for the EEGs were performed periodically. The effectiveness of SMR conditioning for the control of epileptic seizures is evaluated in terms of patient characteristics and type of seizures.     

The Characterization of Childhood Occipital Epilepsy of Gastaut: A Study of Seven Patients

Characterization of the electroclinical features and evolution of childhood occipital epilepsy of Gastaut (COE-G). Seven children were retrospectively identified as having COE-G and were followed-up clinically using EEGs. Visual manifestations were the most common ictal event. Eye-associated ictal deviation was associated with ipsilateral turning of the head and migraine-like symptoms were frequent. Hemiconvulsions occurred in two children, and only one child had secondary generalized tonic–clonic seizures. In all patients, seizures occurred while awake, while two patients also had seizures while sleeping. EEG showed five patients with occipital spike-wave discharges when their eyes were closed which disappeared once their eyes were opened. Two cases continued having frequent seizures despite antiepileptic drug treatment. These patients also displayed learning difficulties and behavioral impairments after seizure onset. COE-G is a distinctive epileptic syndrome; however, the long-term prognosis for patients with the condition is unclear.     

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。     

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.     

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.     

The Wakayama epileptic rat (WER), a new mutant exhibiting tonic-clonic seizures and absence-like seizures.

A new mutant, the Wakayama epileptic rat (WER), exhibiting both spontaneous absence-like behavior and tonic-clonic convulsions, was identified in a colony of Wistar rats. To determine clear seizure characteristics of this mutant strain, we analyzed the mode of inheritance of the convulsion and observed patterns of electroencephalogram (EEG) during the seizures. F1 progeny were produced between the founder male and normal females of the same colony. Animals were monitored through the inbreeding course to analyze genetic control of epileptic behavior. EEGs were recorded using affected animals in the F3-4 and post F13 generations. After the F2 generation, affected rats spontaneously exhibited both absence-like immobile behavior and tonic-clonic convulsions. The absence-like seizures were characterized by motor arrest and head droop. The tonic-clonic convulsions began with neck and forelimb clonus, wild jumping/running, and opisthotonic posturing, and evolved to tonic, then clonic convulsions. Most convulsion onsets occurred between 25-70 days of age. Mating experiments revealed that 0%(0/18) of the animals in F1, 10%(3/26) in F2, 17%(1/6) in backcross progeny and 86% (100/116) in progeny of crosses between epileptic rats showed tonic-clonic convulsions. Ictal cortical EEGs were characterized by 4-6 (5.1 +/- 0.4, mean +/- SD) Hz spike-and-wave complexes in the absence-like seizures and by low-voltage fast waves in the tonic-clonic convulsions. This new mutant rat spontaneously exhibited both absence-like and tonic-clonic seizures. The tonic-clonic seizure was inherited as an autosomal recessive trait with 86% incidence. Thus, the new mutant rat may become a useful model for studying human inherited epilepsies.