Feng Rui, Yang Jingwen, Huang Hao, Chen Zelin, Feng Ruiyan, Hameed N U Farrukh, Zhang Xudong, Hu Jie, Chen Liang, Lu Shuo
IEEE Trans Med Imaging. 2025 Jan;44(1):574-587. doi: 10.1109/TMI.2024.3453377. Epub 2025 Jan 2.
Refractory temporal lobe epilepsy (TLE) is one of the most frequently observed subtypes of epilepsy and endangers more than 50 million people world-wide. Although electroencephalogram (EEG) had been widely recognized as a classic tool to screen and diagnose epilepsy, for many years it heavily relied on identifying epileptic discharges and epileptogenic zone localization, which however, limits the understanding of refractory epilepsy due to the network nature of this disease. This work hypothesizes that the microstate dynamics based on resting-state scalp EEG can offer an additional network depiction of the disease and provide potential complementary evaluation tool for the TLE even without detectable epileptic discharges on EEG. We propose a novel framework for EEG microstate spatial-temporal dynamics (EEG-MiSTD) analysis based on machine learning to comprehensively model millisecond-changing whole-brain network dynamics. With only 100 seconds of resting-state EEG even without epileptic discharges, this approach successfully distinguishes TLE patients from healthy controls and is related to the lateralization of epileptic focus. Besides, microstate temporal and spatial features are found to be widely related to clinical parameters, which further demonstrate that TLE is a network disease. A preliminary exploration suggests that the spatial topography is sensitive to the following surgical outcomes. From such a new perspective, our results suggest that spatiotemporal microstate dynamics is potentially a biomarker of the disease. The developed EEG-MiSTD framework can probably be considered as a general tool to examine dynamical brain network disruption in a user-friendly way for other types of epilepsy.
难治性颞叶癫痫（TLE）是最常见的癫痫亚型之一，全球有超过5000万人受其影响。尽管脑电图（EEG）一直被广泛认为是筛查和诊断癫痫的经典工具，但多年来它严重依赖于识别癫痫放电和癫痫病灶定位，然而，由于该疾病的网络性质，这限制了对难治性癫痫的理解。这项研究假设，基于静息状态头皮脑电图的微状态动力学可以提供该疾病的额外网络描述，并为TLE提供潜在的补充评估工具，即使脑电图上没有可检测到的癫痫放电。我们提出了一种基于机器学习的脑电图微状态时空动力学（EEG-MiSTD）分析的新框架，以全面模拟毫秒级变化的全脑网络动力学。即使没有癫痫放电，仅用100秒的静息状态脑电图，这种方法就能成功地区分TLE患者和健康对照，并且与癫痫病灶的侧化有关。此外，发现微状态的时间和空间特征与临床参数广泛相关，这进一步证明TLE是一种网络疾病。初步探索表明，空间拓扑对以下手术结果敏感。从这个新的角度来看，我们的结果表明，时空微状态动力学可能是该疾病的一个生物标志物。所开发的EEG-MiSTD框架可能被视为一种通用工具，以用户友好的方式检查其他类型癫痫中动态脑网络的破坏情况。
