Abstract：
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.
摘要：
颞叶癫痫（TLE）是主要的成人局灶性癫痫综合征，以功能失调的内在脑动力学为特征。然而,这些患者癫痫发作的确切机制仍然难以捉摸.我们的研究包括116例TLE患者，而51例健康对照。采用微观状态分析，我们评估了TLE患者和健康对照者之间的大脑动态差异，以及耐药性癫痫（DRE）和药物敏感性癫痫（DSE）患者之间。我们基于微观状态构建了动态功能连接网络，并量化了它们的时空变异性。利用这些大脑网络特征，我们开发了机器学习模型来区分TLE患者和健康对照，以及DRE和DSE患者之间。与健康对照相比，TLE患者的时间动力学表现出明显的加速度，以及大脑网络的高度同步和不稳定。此外，DRE患者在微状态B的某些部分表现出明显较低的空间变异性，E和F动态功能连接网络，与DSE患者相比，DRE患者微状态E和G动态功能连接网络某些部分的时间变异性明显更高。基于这些时空度量的机器学习模型有效地将TLE患者与健康对照区分开，并将DRE与DSE患者区分开。在TLE患者中观察到的加速的微状态动力学和破坏的微状态序列反映了高度不稳定的内在脑动力学，潜在的潜在异常放电。此外,DRE患者大脑网络中高度同步和不稳定活动的存在意味着稳定的癫痫网络的建立，导致对抗癫痫药物的反应性差。基于时空度量的模型表现出稳健的预测性能，准确区分TLE患者与健康对照和DRE患者与DSE患者。
