Scientific Institute IRCCS E.Medea, Epilepsy and Clinical Neurophysiology Unit, Conegliano 31015, Italy.
Clinical Neurological Sciences Department, Schulich School of Medicine and Dentistry, Western University, London N6A5C1, Canada.
Neuroimage Clin. 2024;44:103703. doi: 10.1016/j.nicl.2024.103703. Epub 2024 Nov 10.
Epilepsy is one of the most diffused neurological disorders, affecting 50 million people worldwide. Around 30% of patients have drug-resistant epilepsy (DRE), defined as failure of at least two tolerated antiseizure medications (ASMs) to achieve sustained seizure freedom. Brain surgery is an effective therapeutic approach in this group, hinging on the accurate localization of the epileptic focus. The latter task is complex and requires multimodal investigation methods. Epilepsy is also a network disorder and represents one of the best application scenarios of methods leveraging brain functional organization at large scales. Connectivity analysis represents a promising tool for improving surgical assessment, enabling better identification of candidates who could benefit the most from epilepsy surgery. The scalp electroencephalography (EEG) is the most relevant tool to characterize epileptic activity. The EEG has benefited significantly from technological advancement across the last decades. Firstly, electrical source imaging (ESI) allows the reconstruction of electrical activity detected by EEG at the cortex level; secondly, functional connectivity (FC) allows the assessment of functional dependencies across brain areas. The EEG has therefore expanded potential applications in the localization and characterization of the epileptogenic network for surgical planning. As the translation of these methods in clinical practice is little discussed in the literature, we reviewed the investigations using EEG-derived FC. We showed that the FC-informed identification of the epileptic networks improves the localization precision in focal epilepsy. We discussed the heterogeneity in the results and methodology preventing prompt research-to-clinic translation. We finally provided practical suggestions for promoting the applicability of FC-based research in real clinical practice, looking for future research.
癫痫是最常见的神经障碍之一，影响着全球 5000 万人。大约 30%的患者患有耐药性癫痫（DRE），定义为至少两种耐受的抗癫痫药物（ASMs）治疗失败，未能实现持续无癫痫发作。脑部手术是这一组患者的有效治疗方法，关键在于准确定位癫痫灶。这项任务很复杂，需要采用多模态的调查方法。癫痫也是一种网络障碍，代表了利用大脑大规模功能组织的方法的最佳应用场景之一。连接分析是一种很有前途的工具，可以提高手术评估的准确性，使我们能够更好地识别最有可能从癫痫手术中受益的患者。头皮脑电图（EEG）是描述癫痫活动的最相关工具。在过去几十年中，脑电图技术取得了重大进展。首先，电源成像（ESI）允许在皮层水平重建脑电图检测到的电活动；其次，功能连接（FC）允许评估大脑区域之间的功能依赖关系。因此，脑电图在癫痫灶的定位和癫痫网络的特征描述方面扩大了其潜在的应用。由于这些方法在临床实践中的转化在文献中讨论较少，我们综述了使用脑电图衍生的 FC 进行的研究。我们发现，FC 有助于识别癫痫网络，提高了局灶性癫痫的定位精度。我们讨论了结果和方法的异质性，这些因素阻碍了其迅速向临床的转化。最后，我们提出了一些实用的建议，以促进基于 FC 的研究在实际临床实践中的适用性，并寻找未来的研究方向。
