Cerulli Irelli Emanuele, Leodori Giorgio, Morano Alessandra, Di Bonaventura Carlo
Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy.
IRCCS Neuromed, 86077 Pozzilli, Italy.
Biomedicines. 2022 Sep 28;10(10):2428. doi: 10.3390/biomedicines10102428.
Idiopathic generalized epilepsy (IGE) represents a common form of epilepsy in both adult and pediatric epilepsy units. Although IGE has been long considered a relatively benign epilepsy syndrome, a remarkable proportion of patients could be refractory to treatment. While some clinical prognostic factors have been largely validated among IGE patients, the impact of routine electroencephalography (EEG) findings in predicting drug resistance is still controversial and a growing number of authors highlighted the potential importance of capturing the sleep state in this setting. In addition, the development of advanced computational techniques to analyze EEG data has opened new opportunities in the identification of reliable and reproducible biomarkers of drug resistance in IGE patients. In this manuscript, we summarize the EEG findings associated with treatment resistance in IGE by reviewing the results of studies considering standard EEGs, 24-h EEG recordings, and resting-state protocols. We discuss the role of 24-h EEG recordings in assessing seizure recurrence in light of the potential prognostic relevance of generalized fast discharges occurring during sleep. In addition, we highlight new and promising biomarkers as identified by advanced EEG analysis, including hypothesis-driven functional connectivity measures of background activity and data-driven quantitative findings revealed by machine learning approaches. Finally, we thoroughly discuss the methodological limitations observed in existing studies and briefly outline future directions to identify reliable and replicable EEG biomarkers in IGE patients.
特发性全身性癫痫(IGE)是成人和儿童癫痫治疗单元中常见的癫痫类型。尽管IGE长期以来被认为是一种相对良性的癫痫综合征,但仍有相当比例的患者对治疗无效。虽然一些临床预后因素在IGE患者中已得到充分验证,但常规脑电图(EEG)结果对预测耐药性的影响仍存在争议,越来越多的作者强调了在这种情况下捕捉睡眠状态的潜在重要性。此外,先进的计算技术用于分析EEG数据,为识别IGE患者耐药性的可靠且可重复的生物标志物带来了新机遇。在本手稿中,我们通过回顾考虑标准脑电图、24小时脑电图记录和静息状态协议的研究结果,总结了与IGE治疗耐药相关的脑电图结果。鉴于睡眠期间出现的全身性快速放电的潜在预后相关性,我们讨论了24小时脑电图记录在评估癫痫复发中的作用。此外,我们强调了通过先进的脑电图分析确定的新的有前景的生物标志物,包括基于假设的背景活动功能连接测量和机器学习方法揭示的数据驱动定量结果。最后,我们深入讨论了现有研究中观察到的方法学局限性,并简要概述了未来识别IGE患者可靠且可重复脑电图生物标志物的方向。