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Background: Epilepsy has a genetic predisposition, yet causal factors and the dynamics of the immune environment in epilepsy are not fully understood., Methods: We analyzed peripheral blood samples from epilepsy patients, identifying key genes associated with epilepsy risk through Mendelian randomization, using eQTLGen and genome-wide association studies. The peripheral immune environment's composition in epilepsy was explored using CIBERSORT. An epilepsy mouse model was established to validated the expression of key genes at the transcriptomic and proteomic levels through single-cell analysis. Relevant pathways were verified. Finally, we developed a predictive model for antiepileptic drug response in epilepsy patients., Results: We found that CDC25B, DNMT1, GZMA, MTX1, and SSH2 expression decreases epilepsy risk, whereas FGD3, RAF1, and SH3BP5L increase it. Epilepsy patients exhibited an altered peripheral immune profile, notably with increased activated mast cells and decreased CD4 memory activated T cells and γδ T cells. Eight genes were significantly related to this immune environment. In the animal model, FGD3, SSH2, and DNMT1 were upregulated at both mRNA and protein levels in the hippocampus. FGD3 and SSH2 are specifically elevated in microglia and are primarily associated with actin regulation. The trained predictive model was deployed on an online platform., Conclusions: This study elucidates key genes linked to epilepsy, delineates the epilepsy immune landscape, and highlights the interaction between these domains, providing insights into potential epilepsy mechanisms and treatments., (© 2025 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

The results of many epidemiological studies suggest a bidirectional causality may exist between epilepsy and Parkinson's disease (PD). However, the underlying molecular landscape linking these two diseases remains largely unknown. This study aimed to explore this possible bidirectional causality by identifying differentially expressed genes (DEGs) in each disease as well as their intersection based on two respective disease-related datasets. We performed enrichment analyses and explored immune cell infiltration based on an intersection of the DEGs. Identifying a protein-protein interaction (PPI) network between epilepsy and PD, and this network was visualised using Cytoscape software to screen key modules and hub genes. Finally, exploring the diagnostic values of the identified hub genes. NetworkAnalyst 3.0 and Cytoscape software were also used to construct and visualise the transcription factor-micro-RNA regulatory and co-regulatory networks, the gene-microRNA interaction network, as well as gene-disease association. Based on the enrichment results, the intersection of the DEGs mainly revealed enrichment in immunity-, phosphorylation-, metabolism-, and inflammation-related pathways. The boxplots revealed similar trends in infiltration of many immune cells in epilepsy and Parkinson's disease, with greater infiltration in patients than in controls. A complex PPI network comprising 186 nodes and 512 edges were constructed. According to node connection degree, top 15 hub genes were considered the kernel targets of epilepsy and PD. The area under curve values of hub gene expression profiles confirmed their excellent diagnostic values. This study is the first to analyse the molecular landscape underlying the epidemiological link between epilepsy and Parkinson's disease. The two diseases are closely linked through immunity-, inflammation-, and metabolism-related pathways. This information was of great help in understanding the pathogenesis, diagnosis, and treatment of the diseases. The present results may provide guidance for further in-depth analysis about molecular mechanisms of epilepsy and PD and novel potential targets., Competing Interests: Declarations. Conflict of interest: The authors declare that they have no competing interests. Ethical Approval: This study was conducted in silico based on the analysis of data obtained from online databases and no laboratory studies were conducted on humans or animals. Consent for Publication: All authors have read and agreed to the published version of the manuscript., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Accurate identification of high-frequency oscillation (HFO) is an important prerequisite for precise localization of epileptic foci and good prognosis of drug-refractory epilepsy. Exploring a high-performance automatic detection method for HFOs can effectively help clinicians reduce the error rate and reduce manpower. Due to the limited analysis perspective and simple model design, it is difficult to meet the requirements of clinical application by the existing methods. Therefore, an end-to-end bi-branch fusion model is proposed to automatically detect HFOs. With the filtered band-pass signal (signal branch) and time-frequency image (TFpic branch) as the input of the model, two backbone networks for deep feature extraction are established, respectively. Specifically, a hybrid model based on ResNet1d and long short-term memory (LSTM) is designed for signal branch, which can focus on both the features in time and space dimension, while a ResNet2d with a Convolutional Block Attention Module (CBAM) is constructed for TFpic branch, by which more attention is paid to useful information of TF images. Then the outputs of two branches are fused to realize end-to-end automatic identification of HFOs. Our method is verified on 5 patients with intractable epilepsy. In intravalidation, the proposed method obtained high sensitivity of 94.62%, specificity of 92.7%, and F1-score of 93.33%, and in cross-validation, our method achieved high sensitivity of 92.00%, specificity of 88.26%, and F1-score of 89.11% on average. The results show that the proposed method outperforms the existing detection paradigms of either single signal or single time-frequency diagram strategy. In addition, the average kappa coefficient of visual analysis and automatic detection results is 0.795. The method shows strong generalization ability and high degree of consistency with the gold standard meanwhile. Therefore, it has great potential to be a clinical assistant tool., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2021 Zimo Liu et al.)

Background: Stereoelectroencephalography (SEEG), as a minimally invasive method that can stably collect intracranial electroencephalographic information over long periods, has increasingly been applied in the diagnosis and treatment of intractable epilepsy in recent years. Over the past 20 years, with the advancement of materials science and computer science, the application scenarios of SEEG have greatly expanded. Bibliometrics, as a method of scientifically analyzing published literature, can summarize the evolutionary process in the SEEG field and offer insights into its future development prospects. Methods: This article selected all the literature records retrieved on November 4, 2024, from the Web of Science Core Collection (WoSCC). The search terms were as follows: "Stereo-electroencephalography" or "Stereo electroencephalography" or "Stereo-EEG" or "Stereo EEG" or "SEEG." The document types included were research articles and reviews. For analysis, VOSviewer, CiteSpace, and the R package "bibliometrix" were employed to analyze various aspects of the SEEG field, including authors, institutions, countries and regions, and research hotspots. Results: We reviewed a total of 1,383 non-duplicate literature records from 2002 to 2023, including 1,241 research articles, 116 review articles and 26 letters. Observing the annual publication trends, there has been an overall increase since 2002. The most influential journal in this field is Epilepsia. Other journals with considerable impact include Clinical Neurophysiology , Epileptic Disorders , Epilepsy Research , NeuroImage , and Epilepsy & Behavior. The top 5 most influential scholars are Bartolomei F, Tassi L, Nobili L, Russo GL, and Mc Gonigal A. As for the analysis of countries and regions, France occupies a leading position in this field with its early start, while China and the United States have also emerged as focal points since 2020. Research on SEEG has expanded beyond its initial use for localizing epileptic foci and thermo-coagulation treatments and have been employed as a medium to facilitate real-time prediction of epileptic seizures and enabling the exploration of brain network connectivity. Conclusion: As a minimally invasive tool for collecting intracranial electroencephalographic signals, SEEG continues to offer vast potential for development and application. Advances in electrode materials and robotic-assisted stereotactic techniques, have enabled SEEG to simultaneously sample multiple brain regions, acquire electrical signals from deep brain structures. These advantages significantly enhance the precision of epileptic focus localization in diagnosis and treatment, addressing the limitations of subdural electrodes. Through bibliometric analysis, this paper traces the developmental trajectory of SEEG and identifying key technological milestones, thereby providing a reference for scholarly research directions. [ABSTRACT FROM AUTHOR]

Epilepsy is a common chronic neurologic disorder, with about 30%‐40% of patients suffering from recurrent seizures despite receiving enough antiseizure medication. Growing evidence of extensive structural and functional research has revealed that epilepsy is a network disorder, and the network investigation is crucial in the presurgical evaluation. This narrative review is aimed at providing an overview of the conceptual development of epileptic surgery, the epileptic network formation, and the network‐oriented strategy in clinical practice. We searched the PubMed database before October 2022 with no language or regional restrictions for studies of network exploration in epilepsy patients. The study eligibility criteria were original connectivity or network research studies using neuroimaging or neurophysiological approaches, focusing on seizure localization, neuropsychological evaluation, neuromodulation, or surgical prognosis. The initial search revealed 2046 studies. The selection process presented 746 papers that meet the inclusion criteria. The results indicated that epilepsy was characterized by the alternation of brain networks, which account for the clinical, electrographic, and neurocognitive phenomena related to human seizures and changes across different disease stages. Hubs have garnered significant attention due to their extensive connections, which make them prone to facilitating the spread of seizures to various brain regions. Unlike the seizure onset zone, the removal of the majority of these ictogenic nodes demonstrated a significant correlation with positive outcomes. Therefore, performing a network analysis during the epilepsy presurgical evaluation seems essential and will influence surgical decisions. It is hoped that studying the network of epilepsy will enhance our ability to determine the most appropriate surgical strategy for patients with epilepsy, thus contributing to success in terms of effective seizure control with maximal sparing or even improvement of brain functioning. [ABSTRACT FROM AUTHOR]

• The study answered the potential causes of postoperative recurrence of MTLE. • This is the first article to explore the role of extratemporal structures in the prognosis of MTLE patients through SEEG. • The epileptogenic network in recurred patients with MTLE is associated with the orbitofrontal cortex and insula. • More HFOs in the medial temporal lobe have a better prognosis after removing the lesion. • Some MTLE patients have extratemporal HFO, but it is not related to prognosis. We investigated the network between the medial temporal lobe (MTL) and extratemporal structures in patients with mesial temporal lobe epilepsy (MTLE) in order to explain the recurrence of MTLE after surgery. This study contributes to our current understanding of MTLE with stereotactic electroencephalography (SEEG). We conducted a retrospective study of SEEG in 20 patients with MTLE in order to observe and analyze the intensity of interictal high-frequency oscillations (HFOs), as well as the dynamic course of coherence connectivity values of the MTL and extratemporal structures during the initial phase of the seizure. The results correlated with the patient prognosis. First, the presence of HFOs was observed during the interictal period in all 20 patients; these were localized to the MTL in 17 patients and the orbitofrontal cortex in seven patients and the insula in six patients. The better the prognosis, the greater the localization of the HFOs concentration in the MTL structures (p < 0.05). Second, significantly enhanced connectivity of MTL structures with the orbitofrontal cortex and insula was observed in most patients with MTLE, before and after the seizure onset (p < 0.05). Finally, the connectivity between extratemporal structures, such as the orbitofrontal cortex and insula, and MTL structures was significantly stronger in patients who had a worse prognosis than in other patients, before and after seizure onset (p < 0.05). The epileptogenic network in recurrent MTLE is not limited to MTL structures but is also associated with the orbitofrontal cortex and insula. This can be used as a potential indicator for predicting the prognosis of patients after surgery, providing an important avenue for future clinical evaluation. [ABSTRACT FROM AUTHOR]