Revue neurologique最新文献_第6页

Recent progress and challenges in gene therapy for pharmacoresistant focal epilepsy 耐药局灶性癫痫基因治疗的最新进展和挑战。

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.02.008

D.M. Kullmann

引用次数: 0

Pathophysiology of SUDEP: How far are we from understanding? 猝死症的病理生理学：我们离了解它还有多远？

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.03.010

E. Boglietti , D. Haddad , L. Bezin , S. Rheims

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Multiple sclerosis and epilepsy 多发性硬化症和癫痫。

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.03.008

H. Catenoix , W. Grabon , S. Rheims , S. Vukusic , R. Marignier

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Detection of brain somatic mutations from stereo-EEG electrodes in focal epilepsy: Current advances and future perspectives 局灶性癫痫的立体脑电图电极检测脑体细胞突变：当前进展和未来展望。

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.03.003

R. Checri , S. Baldassari , S. Baulac

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Targeted therapies in epilepsies 癫痫的靶向治疗

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.04.003

S. Auvin

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The different subtypes of temporal lobe seizures networks 颞叶癫痫网络的不同亚型。

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.03.004

F. Bartolomei , J. Makhalova , J. Benoit , S. Lagarde

{"title":"The different subtypes of temporal lobe seizures networks","authors":"F. Bartolomei , J. Makhalova , J. Benoit , S. Lagarde","doi":"10.1016/j.neurol.2025.03.004","DOIUrl":"10.1016/j.neurol.2025.03.004","url":null,"abstract":"<div><div>Temporal lobe epilepsies (TLEs) are among the forms of epilepsy most frequently encountered in surgical evaluations, characterized by a wide range of anatomical, functional, and electroclinical subtypes. Traditional classifications, such as mesial and lateral TLE, have been broadened by advances in stereoelectroencephalography (SEEG), revealing more complex forms such as mesio-lateral and temporal-plus seizures. These findings support the concept of epileptogenic networks, emphasizing interconnected regions rather than isolated focal areas in the genesis of seizures. Quantitative tools, such as the epileptogenicity index (EI), are improving the accuracy of SEEG interpretation, which is closely correlated with surgical results. Temporal-plus epilepsies, in particular, require full SEEG exploration due to their broader involvement in the network, necessitating tailored surgical approaches. A better understanding of TLEs subtypes and epileptogenic networks is an essential basis for advancing minimally invasive surgical techniques, including laser interstitial thermal therapy (LITT) and neuromodulation. These methods rely on the precise localization of epileptogenic networks. This network-based framework represents an important step towards optimizing surgical outcomes and advancing personalized epilepsy care.</div></div>","PeriodicalId":21321,"journal":{"name":"Revue neurologique","volume":"181 5","pages":"Pages 368-381"},"PeriodicalIF":2.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals 脑电图信号中间歇癫痫样放电的人工智能检测

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.04.001

E. Dessevres , M. Valderrama , M. Le Van Quyen

{"title":"Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals","authors":"E. Dessevres , M. Valderrama , M. Le Van Quyen","doi":"10.1016/j.neurol.2025.04.001","DOIUrl":"10.1016/j.neurol.2025.04.001","url":null,"abstract":"<div><h3>Introduction</h3><div>Over the past decades, the integration of modern technologies — such as electronic health records, cloud computing, and artificial intelligence (AI) — has revolutionized the collection, storage, and analysis of medical data in neurology. In epilepsy, Interictal Epileptiform Discharges (IEDs) are the most established biomarker, indicating an increased likelihood of seizures. Their detection traditionally relies on visual EEG assessment, a time-consuming and subjective process contributing to a high misdiagnosis rate. These limitations have spurred the development of automated AI-driven approaches aimed at improving accuracy and efficiency in IED detection.</div></div><div><h3>Methods</h3><div>Research on automated IED detection began 45 years ago, spanning from morphological methods to deep learning techniques. In this review, we examine various IED detection approaches, evaluating their performance and limitations.</div></div><div><h3>Results</h3><div>Traditional machine learning and deep learning methods have produced the most promising results to date, and their application in IED detection continues to grow. Today, AI-driven tools are increasingly integrated into clinical workflows, assisting clinicians in identifying abnormalities while reducing false-positive rates.</div></div><div><h3>Discussion</h3><div>To optimize the clinical implementation of automated AI-based IED detection, it is essential to render the codes publicly available and to standardize the datasets and metrics. Establishing uniform benchmarks will enable objective model comparisons and help determine which approaches are best suited for clinical use.</div></div>","PeriodicalId":21321,"journal":{"name":"Revue neurologique","volume":"181 5","pages":"Pages 411-419"},"PeriodicalIF":2.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Are antiseizure medications disease modifiers? 抗癫痫药物是疾病调节剂吗？

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.03.009

L. Tyvaert

{"title":"Are antiseizure medications disease modifiers?","authors":"L. Tyvaert","doi":"10.1016/j.neurol.2025.03.009","DOIUrl":"10.1016/j.neurol.2025.03.009","url":null,"abstract":"<div><div>The International League Against Epilepsy (ILAE) is proposing a new nomenclature, replacing the confusing term antiepileptic drugs (AEDs) with antiseizure medications (ASMs). This approach has been partly motivated by the emergence of new treatments with antiepileptogenic or, more precisely, disease-modifying effects. This implies an effect on the temporal course of the disease, even when treatment is discontinued. However, the terms ASM and disease-modifying are not mutually exclusive. In some cases, ASMs can have this property. however, demonstrating this disease-modifying effect, and hence the development of these molecules, remains complex and difficult. The disease-modifying effect of ASM seems to depend on its etiological or pathophysiological effect on the developing epileptic disease, but also on other characteristics: target on etiological or pathophysiological mechanisms of the disease, level of cerebral maturation, time after onset of an epileptogenic event, dosage and duration of treatment. The development of this new type of treatment requires a better definition of the target populations, a better understanding of the cascade of epileptogenic phenomena, and the definition of new diagnostic and prognostic markers for epilepsy.</div></div>","PeriodicalId":21321,"journal":{"name":"Revue neurologique","volume":"181 5","pages":"Pages 445-449"},"PeriodicalIF":2.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence applied to epilepsy imaging: Current status and future perspectives 人工智能在癫痫成像中的应用：现状与未来展望。

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.03.006

M. Berger , R. Licandro , K.-H. Nenning , G. Langs , S.B. Bonelli

{"title":"Artificial intelligence applied to epilepsy imaging: Current status and future perspectives","authors":"M. Berger , R. Licandro , K.-H. Nenning , G. Langs , S.B. Bonelli","doi":"10.1016/j.neurol.2025.03.006","DOIUrl":"10.1016/j.neurol.2025.03.006","url":null,"abstract":"<div><div>In recent years, artificial intelligence (AI) has become an increasingly prominent focus of medical research, significantly impacting epileptology as well. Studies on deep learning (DL) and machine learning (ML) – the core of AI – have explored their applications in epilepsy imaging, primarily focusing on lesion detection, lateralization and localization of epileptogenic areas, postsurgical outcome prediction and automatic differentiation between people with epilepsy and healthy individuals. Various AI-driven approaches are being investigated across different neuroimaging modalities, with the ultimate goal of integrating these tools into clinical practice to enhance the diagnosis and treatment of epilepsy. As computing power continues to advance, the development, research integration, and clinical implementation of AI applications are expected to accelerate, making them even more effective and accessible. However, ensuring the safety of patient data will require strict regulatory measures. Despite these challenges, AI represents a transformative opportunity for medicine, particularly in epilepsy neuroimaging. Since ML and DL models thrive on large datasets, fostering collaborations and expanding open-access databases will become increasingly pivotal in the future.</div></div>","PeriodicalId":21321,"journal":{"name":"Revue neurologique","volume":"181 5","pages":"Pages 420-424"},"PeriodicalIF":2.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143773093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The revolution has begun 革命已经开始

IF 2.8 4区医学

Revue neurologique Pub Date : 2025-05-01 DOI: 10.1016/j.neurol.2025.04.006

S. Dupont , S. Rheims

引用次数: 0