Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies

Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota composition.Satisfactory results are lacking for conventional treatments for epilepsy.Surgical resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy.Non-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research focus.This review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of epilepsy.It also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation.Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures.Additionally,many new technologies for the diagnosis and treatment of epilepsy are being explored.However,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.

The role of axon guidance molecules in the pathogenesis of epilepsy

Current treatments for epilepsy can only manage the symptoms of the condition but cannot alter the initial onset or halt the progression of the disease. Consequently, it is crucial to identify drugs that can target novel cellular and molecular mechanisms and mechanisms of action. Increasing evidence suggests that axon guidance molecules play a role in the structural and functional modifications of neural networks and that the dysregulation of these molecules is associated with epilepsy susceptibility. In this review, we discuss the essential role of axon guidance molecules in neuronal activity in patients with epilepsy as well as the impact of these molecules on synaptic plasticity and brain tissue remodeling. Furthermore, we examine the relationship between axon guidance molecules and neuroinflammation, as well as the structural changes in specific brain regions that contribute to the development of epilepsy. Ample evidence indicates that axon guidance molecules, including semaphorins and ephrins, play a fundamental role in guiding axon growth and the establishment of synaptic connections. Deviations in their expression or function can disrupt neuronal connections, ultimately leading to epileptic seizures. The remodeling of neural networks is a significant characteristic of epilepsy, with axon guidance molecules playing a role in the dynamic reorganization of neural circuits. This, in turn, affects synapse formation and elimination. Dysregulation of these molecules can upset the delicate balance between excitation and inhibition within a neural network, thereby increasing the risk of overexcitation and the development of epilepsy. Inflammatory signals can regulate the expression and function of axon guidance molecules, thus influencing axonal growth, axon orientation, and synaptic plasticity. The dysregulation of neuroinflammation can intensify neuronal dysfunction and contribute to the occurrence of epilepsy. This review delves into the mechanisms associated with the pathogenicity of axon guidance molecules in epilepsy, offering a valuable reference for the exploration of therapeutic targets and presenting a fresh perspective on treatment strategies for this condition.

Interleukin-6 in epilepsy and its neuropsychiatric comorbidities: How to bridge the gap

There is growing evidence that interleukin(IL)-6 plays an important role in neurological and psychiatric disorders.This editorial comments on the study published in the recent issue of the World Journal of Psychiatry,which employed Mendelian randomization to identify a causal relationship between IL-6 receptor blockade and decreased epilepsy incidence.The purpose of this editorial is to highlight the dual effects of IL-6 in epilepsy and its related neuropsychiatric comorbidities.IL-6 plays a critical role in the facilitation of epileptogenesis and maintenance of epileptic seizures and is implicated in neuroinflammatory proce-sses associated with epilepsy.Furthermore,IL-6 significantly influences mood regulation and cognitive dysfunction in patients with epilepsy,highlighting its involvement in neuropsychiatric comorbidities.In summary,IL-6 is not only a pivotal factor in the pathogenesis of epilepsy but also significantly contributes to the emergence of epilepsy-related neuropsychiatric complications.Future resear-ch should prioritize elucidating the specific mechanisms by which IL-6 operates across different subtypes,stages and neuropsychiatric comorbidities of epilepsy,with the aim of developing more precise and effective interventions.Furthermore,the potential of IL-6 as a biomarker for the early diagnosis and prognosis of epile-psy warrants further investigation.

Therapeutic potential of Carum carvi in depression,memory loss,and hippocampal sclerosis reversal in temporal lobe epilepsy

Background:Epilepsy is a disease characterized by unprovoked seizures,and it affects around 70 million people worldwide.Standard treatment is ineffective in one third of all epilepsy patients.Temporal Lobe Epilepsy with Hippocampal Sclerosis(TLE-HS)is the most drug-resistant form of epilepsy,and it also impacts physical,mental,and psychological well-being of patients.Carum carvi extract has demonstrated anti-convulsant,anti-depressant,and anxiolytic properties.This study was designed to investigate if Carum carvi extract can alleviate depression and memory loss symptoms in a TLE-HS animal model.Methods:Male Sprague Dawley rats were used to create a model of TLE-HS and Carum carvi extract treatment,along with appropriate controls,was used to test the efficacy of this herbal extract in reducing the symptoms of depression and memory loss.Results:Forced swim test showed that Carum carvi extract treated TLE-HS rats resulted in significant improvement of the symptoms of depression.However,novel object recognition test showed that memory improvement did not occur.Conclusion:Depression significantly impacts the quality of life in TLE-HS patients,and this study has shown that Carum carvi extract should be explored further as an adjuvant treatment for TLE-HS patients to improve their quality of life.

Sodium channel mutation SCN1A T875M,D188V and associated dysfunction with drug resistant epilepsy

BACKGROUND The NaV1.1 sodium channel alpha subunit,encoded by SCN1A,is crucial for initiating and propagating action potentials in neurons.SCN1A gene has long been an established target in the etiology and therapy of epilepsy.However,very few studies have investigated the relevance of genetic variations in epilepsy and anti-epileptic drug resistance.AIM To investigate associations between polymorphisms,rs121917953 T/A and rs121918623 C/T,and drug resistance in epilepsy patients in the north Indian population.METHODS A total of 100 age-and sex-matched epilepsy patients(50 drug responsive and 50 drug resistant subjects)were recruited and SCN1A rs121918623 C/T*and rs121917953 T/A*polymorphisms were analyzed by the allele specific-PCR technique.χ^(2)and Fisher’s exact test were used to estimate differences between the distribution of SCN1A rs121918623 and rs121917953 gene polymorphisms among various groups.The association between distinct rs121917953 genotypes and drug resistance was analyzed using logistic regression analysis.RESULTS For the SCN1A rs121917953 T/A*(D188V)polymorphism,a significantly higher proportion of individuals with AT genotype were observed in the drug-resistant group as compared to the drug-responsive group.Additionally,a higher risk association was exhibited by AT genotype for drug resistance with an odds ratio of 3.51 and P value=0.017.For the SCN1A rs121918623 C/T*(T875M)polymorphism,no significant difference in genotype distribution was observed between the drug-resistant and drug-sensitive groups.CONCLUSION Our findings indicate that the SCN1A polymorphism D188V is associated with a higher risk of drug resistance for the AT variant as compared to the homozygous TT wild-type.Further research is needed at the functional level and in larger cohorts to determine the potential of these genes as a therapeutic target in epilepsy subjects.

基于平均能量差的运动想象EEG通道选择和特征提取

共空间模式(CSP)广泛应用于脑电信号(EEG)的特征提取,合适的通道选择可以有效地提高CSP的分类性能,增加信噪比。根据运动想象信号的平均能量差来进行通道选择和特征提取。首先取两类运动想象信号的通道均值能量作为投票的阈值,根据投票差值统计各通道上有明显能量差值试次的数量,基于此来选择出合适的通道,然后对这些通道取能量特征进行归一化,再结合CSP空域特征利用SVM进行分类。在BCI CompetitionⅢData SetsⅣa和BCI Competition IV Dataset SetsⅠ两个数据集上进行的分类实验中,所提出的方法相比于全通道CSP,平均精度分别提高了5.7%和10.9%,通道数分别减少了74.3%和51.7%,验证了所提出的通道选择和特征提取方法的有效性。

基于深层图卷积的EEG情绪识别方法研究

针对浅层图卷积提取的局部脑区空间关联信息对情感脑电表征不足的问题,本文提出了一种深层图卷积网络模型。该模型利用深层图卷积学习情绪脑电全局通道间的内在关系,在卷积传播过程中应用残差连接和权重自映射解决深层图卷积网络面临的节点特征收敛到固定空间无法学习到有效特征的问题,并在卷积层后加入PN正则化扩大不同情绪特征间的距离,提高情绪识别的性能。在SEED数据集上进行实验,与浅层图卷积网络相比准确率提高了0.7%,标准差下降了3.15。结果表明该模型提取的全局脑区空间关联信息对情绪识别的有效性。

EEG信号结合特征融合技术诊断精神分裂症和抑郁症

目的探索通过机器学习算法结合脑电信号实现对精神分裂症和抑郁症的诊断。方法分别采集33例精神分裂症患者和28例抑郁症患者的脑电信号,并将采集到的脑电图信号格式由EDF格式转化为ASCII格式,提取脑电信号的Lempel-Ziv复杂度、最大李雅普诺夫指数、Higuchi分形维数等特征。应用特征融合策略对特征进行融合,形成新的特征向量,然后利用机器学习分类算法进行分类研究。结果最终基于高斯核函数的支持向量机(SVM)的分类准确率为84.85%,其中灵敏度为89.47%,特异性为78.57%。结论通过提取EEG脑电信号特征结合机器学习算法对精神分裂症和抑郁症进行识别,对开发新型的精神分裂症和抑郁症的诊断技术具有一定的研究意义。

注意力残差网络结合LSTM的EEG情绪识别研究

基于脑电信号的情感识别已成为情感计算和人机交互领域的一个重要挑战。由于脑电信号中具有时间、空间、频率维度信息,采用结合注意力残差网络与长短时记忆网络的混合网络模型(ECA-ResNet-LSTM)对脑电信号进行特征提取与识别。首先,提取时域分段后脑电信号不同频带微分熵特征,将从不同通道中提取出的微分熵特征转化为四维特征矩阵;然后通过注意力残差网络(ECA-ResNet)提取脑电信号中空间与频率信息,并引入注意力机制重新分配更相关频带信息的权重,长短时记忆网络(LSTM)从ECA-ResNet的输出中提取时间相关信息。实验结果表明:在DEAP数据集唤醒维和效价维二分类准确率分别达到了97.15%和96.13%,唤醒-效价维四分类准确率达到了95.96%,SEED数据集积极-中性-消极三分类准确率达到96.64%,相比现有主流情感识别模型取得了显著提升。

强化学习融合群智能算法的癫痫EEG不平衡分类方法

癫痫智能检测的脑电数据具有不平衡性。考虑到单一的群智能算法在改善数据不平衡方面的不足,提出了一种基于强化学习的自适应融合群智能算法。使用强化学习在种群进化的不同阶段自适应地选择并融合多种群智能算法;通过双种群协同进化策略,更高效地获得全局最优解;使用由全局最优解所表示的样本构建平衡数据集并训练分类器。在2个公共癫痫脑电数据集上的实验表明,该方法优于单一的群智能算法,能够有效提高分类器对少数类样本和整体数据集的分类性能。

基于EEG和面部视频的多模态连续情感识别

针对脑电(Electroencephalogram, EEG)通道间和时间上情绪强度的改变很难被捕捉,以及不同被试的面部特征情绪上的相似性难以挖掘的问题,文章提出了一种基于EEG和面部视频的多模态连续情感识别模型.采用基于时空注意力机制(Spatial-Temporal Attention)的卷积和双向长短期记忆神经网络的组合模型(STA-CNNBiLSTM)对EEG中提取的功率谱密度(Power Spectral Density, PSD)特征进行深层特征学习与情感分类;采用引入自注意力机制的预训练卷积神经网络(SA-CNN)对人脸面部几何特征进行学习与情感分类.采用决策级融合算法,对两个模态的分类结果进行迭代学习与融合,得到最终多模态情感分类结果.在公开数据集MAHNOB-HCI进行了大量对比验证实验,在FER2013数据集的面部几何特征上对SA-CNN模型进行了预训练.在独立被试的实验中,所提模型在效价维度二分类的平均准确率为75.50%,在唤醒维度二分类的平均准确率为79.00%,均优于单模态上的最高平均准确率.和目前流行的模型LSSVM、SE-CNN和AM-LSTM相比较,所提模型的分类效果更优,验证了所提时空注意力机制能够捕捉更多的EEG时空特征,自注意力机制能够关注到不同被试面部特征的相似性,进而提高了多模态情感识别的性能.

颞叶癫痫患者术后EEG尖慢波表现类似ECG“R-on-T”现象的1例报告

“R-on-T”现象为心电图的一种表型,被认为是下一个心室收缩提前落在上一周期心室易损期内,通常会引起恶性心律失常。本文介绍1例癫痫患者行右颞病灶切除术及选择性海马杏仁核切除术后,在过度换气诱发试验中脑电图尖慢波表现为类似“R-on-T”同源机理现象的病例,并对该现象产生的可能机制进行讨论,旨在提示神经电生理与心脏电生理具有同源性,进一步拓宽电生理理论学习、延伸电生理诊断思维。

基于多脑区注意力机制胶囊融合网络的EEG-fNIRS情感识别

为了提高情感识别的准确率,提出多脑区注意力机制和胶囊融合模块的胶囊网络模型(MBA-CFc CapsNet).通过情感视频片段诱发采集EEG-fNIRS信号,构建TYUT3.0数据集.提取EEG和f NIRS的特征,将其映射到矩阵,通过多脑区注意力机制融合EEG和fNIRS的特征,给予不同脑区特征不同的权重,以提取质量更高的初级胶囊.使用胶囊融合模块,减少进入动态路由机制的胶囊数量,减少模型运行的时间.利用MBA-CFc CapsNet模型在TYUT3.0情感数据集上进行实验,与单模态EEG和f NIRS识别结果相比,2种信号结合情感识别的准确率提高了1.53%和14.35%.MBA-CF-cCapsNet模型与原始CapsNet模型相比,平均识别率提高了4.98%,与当前常用的CapsNet情感识别模型相比提高了1%~5%.

Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset

Electroencephalography is a common clinical procedure to record brain signals generated by human activity.EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications,but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience.Various EEG analysis and classification techniques have been proposed to address this problem however,the conventional classification methods require identification and learning of specific EEG characteristics beforehand.Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification.One of the great implementations of deep learning is Convolutional Neural Network(CNN)which has outperformed traditional neural networks in pattern recognition and image classification.Continuous Wavelet Transform(CWT)is an efficient signal analysis technique that presents the magnitude of EEG signals as timerelated Frequency components.Existing deep learning architectures suffer from poor performance when classifying EEG signals in the Time-frequency domain.To improve classification accuracy,we propose an integrated CWT and CNN technique which classifies five types of EEG signals using.We compared the results of proposed integrated CWT and CNN method with existing deep learning models e.g.,GoogleNet,VGG16,AlexNet.Furthermore,the accuracy and loss of the proposed integrated CWT and CNN method have been cross validated using Kfold cross validation.The average accuracy and loss of Kfold cross-validation for proposed integrated CWT and CNN method are,76.12%and 56.02%respectively.This model produces results on a publicly available dataset:Epilepsy dataset by UCI(Machine Learning Repository).

基于深度学习的EEG数据分析技术综述

对近年来的相关工作进行全面分析、横向比较,梳理出基于深度学习的EEG数据分析闭环流程.对EEG数据进行介绍,从深度学习在EEG数据预处理、特征提取以及模型泛化3个关键阶段的应用进行展开,梳理深度学习算法在相应阶段提供的研究思路和解决方案,包括各阶段所存在的难点与问题.全方位总结出不同算法的主要贡献和局限性,讨论深度学习技术在各个阶段处理EEG数据时所面临的挑战及未来的发展方向.

Epilepsy Aspects and EEG Patterns in Neuro-Metabolic Diseases

Neurometabolic diseases (NMD) are a frequent cause of epilepsy in children. Epilepsy is more frequently part of a complex clinical picture than a predominant symptom and may be of different types and various EEG patterns. The primary goal of this article is, departing from a large personal series, to describe the seizure type, EEG patterns and response to antiepileptic drugs in NMD and to discuss clinical value of epilepsy type in the setting of specific NMD. We found epilepsy was associated to NMD in 43.1%. Disorders of energy metabolism were the most frequent cause of epilepsy (61.3%). We observed generalized epilepsy in 75% of the patients with partial epilepsy in 25%. EEG was abnormal in only 71% of cases with variable patterns. Resistance to antiepileptic drugs was observed in 75% of cases. Valproate acid was incriminated in seizure worsening in 22.7% of the patients, all of them affected by mitochondriopathies.

Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals

Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.

乒乓球运动员的视觉运动整合优势——基于功能偏侧化理论的EEG证据

目的探究乒乓球运动员视觉运动整合的优势特征,并基于功能偏侧化理论分析其神经机制。方法选取22名乒乓球运动员作为专家组,21名普通大学生作为新手组。采用半视野速示技术,设置视觉运动整合任务,测试并比较乒乓球运动员与新手视觉运动整合的行为差异及脑电特征。结果(1)乒乓球专家组的右手反应时显著短于新手组(P<0.05);(2)在视觉加工阶段,乒乓球专家组的P1成分潜伏期显著长于新手组,且专家组左半球N2潜伏期显著高于右半球(P<0.05);(3)在视觉运动转换和运动执行阶段,乒乓球专家组诱发了更大的BA6区的negativity波幅和BA4区的N2波幅(P<0.05);(4)在顶-枕区域,乒乓球专家组的高频alpha节律神经振荡水平低于新手组,具体表现为在左视野右手反应模式下高频alpha节律振荡水平较低(P<0.05)。结论(1)乒乓球运动员右手(优势手)的视觉运动反应时更短;(2)乒乓球运动员大脑右半球在早期视觉加工阶段对刺激识别更迅速,在视觉运动转换阶段更直接有效;(3)乒乓球运动员在视觉运动整合过程中表现出更少的注意资源消耗,主要体现在右半球的视觉加工和左半球的动作反应优势上。

基于有效注意力和GAN结合的脑卒中EEG增强算法

在基于脑电的卒中分类诊断任务中,以卷积神经网络为基础的深度模型得到广泛应用,但由于卒中类别病患样本数量少,导致数据集类别不平衡,降低了分类精度。现有的少数类数据增强方法大多采用生成对抗网络(GAN),生成效果一般,虽然可通过引入缩放点乘注意力改善样本生成质量,但存储及运算代价往往较大。针对此问题,构建一种基于线性有效注意力的渐进式数据增强算法LESA-CGAN。首先,算法采用双层自编码条件生成对抗网络架构,分别进行脑电标签特征提取及脑电样本生成,并使生成过程逐层精细化;其次,通过在编码部分引入线性有效自注意力(LESA)模块,加强脑电的标签隐层特征提取,并降低网络整体的运算复杂度。消融与对比实验结果表明,在合理的编码层数与生成数据比例下,LESA-CGAN与其他基准方法相比计算资源占用较少,且在样本生成质量指标上实现了10%的性能提升,各频段生成的脑电特征样本均更加自然,同时将病患分类的准确率和敏感度提高到了98.85%和98.79%。