Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.

癫痫网络定义为参与癫痫脑活动的大脑区域，已通过同步脑电图和功能磁共振成像（EEG-fMRI）记录中的功能连接进行了映射。这项技术可以定义与血间充血性癫痫放电（IED）相关的脑血流动力学变化，该变化是由血氧水平依赖性（BOLD）信号测量的，其与发作性事件一起构成了癫痫病的标志。考虑到癫痫活动的高度时变性质，对EEG-fMRI数据进行动态功能连接（dFC）分析显得特别合适，具有识别癫痫网络中特定连接的短暂特征的潜力。在本研究中，我们提出了一种新的方法，定义为dFC-EEG，将通过fMRI评估的dFC与同时头皮脑电图记录的信息相结合，以识别以与IED的发生相关的动态轮廓为特征的连接，从而形成动态癫痫子网。纳入了10例耐药性局灶性癫痫患者，其病因各异，并表现出广泛的（或多叶）BOLD激活，定义为涉及至少两个不同的簇，位于两个不同的叶和/或扩展到癫痫对侧的半球焦点。癫痫的焦点是从与IED相关的BOLD图定义的。发作间期癫痫活动的发生区域；即，通过将fMRI定义的焦点的时程视为主要回归因子的一般线性模型，确定了癫痫网络。使用滑动窗口方法评估了这些区域之间的dFC。然后将dFC时程与IED信号的滑动窗口方差（VarIED）相关联，以识别其动态与癫痫活动有关的连接；即动态癫痫子网。不出所料，鉴于每个人的临床情况截然不同，该子网的范围在患者之间变化很大，但相对于最初确定的9/10位患者的癫痫网络，该子网的范围减少了至少30％。动态子网的连接最常接近癫痫病的焦点，这反映在子网连接的侧向指数上，该指数高于原始癫痫病网络中的那个。此外，dFC时程与VarIED之间的相关性主要为正，建议加强与IED发生有关的动态子网。dFC和头皮IED的集成提供了对癫痫网络的更具体描述，确定了受IED强烈影响的连接。这些发现可能与手术前评估中癫痫发生区域的切除或断开有关，并有助于达到更好的手术后效果。这对于以广泛的病理性脑活动为特征的患者尤其重要，该活动挑战了外科手术干预。这些发现可能与手术前评估中癫痫发生区域的切除或断开有关，并有助于达到更好的手术后效果。这对于以广泛的病理性脑活动为特征的患者尤其重要，该活动挑战了外科手术干预。这些发现可能与手术前评估中癫痫发生区域的切除或断开有关，并有助于达到更好的手术后效果。这对于以广泛的病理性脑活动为特征的患者尤其重要，该活动挑战了外科手术干预。