The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures. The propagation of seizures was modelled as an epidemic process [susceptible-infected-recovered (SIR) model] on individual structural networks derived from presurgical diffusion tensor imaging in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the eigenvector centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network. We found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was equally or more effective than removal based on structural network characteristics both regarding reducing the EC of the hypothesized EZ and seizure spreading. The approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.

难治性癫痫患者癫痫手术的成功取决于对致痫区 (EZ) 的正确识别和切除区域的最佳选择。在这项研究中，我们开发了基于结构脑网络的个性化计算模型，以探索不同虚拟切除对癫痫发作传播的影响。癫痫发作的传播被建模为流行过程[易感感染恢复 (SIR) 模型]，来自 19 名患者的术前扩散张量成像的个体结构网络。虚拟切除的候选连接都是从临床假设的 EZ 到其他大脑区域的连接，从该 EZ 开始模拟癫痫发作。作为 SIR 模型的计算上可行的替代，我们还删除了最大程度降低假设 EZ 的特征向量中心性 (EC)（大值表示网络集线器）的连接，大幅降低意味着大效应。使用模拟退火发现了为获得最大效果而​​移除的最佳连接组合。为了比较，相同数量的连接被随机删除，或者基于量化网络中节点或连接重要性的措施。我们发现 90% 的效果（定义为假设 EZ 的 EC 减少）已经可以通过移除少于 90% 的连接来获得。因此，更小、更优化的虚拟切除实现了与实际手术几乎相同的效果，但成本却大大降低，平均节省了 27.49%（标准偏差：4.65%）的连接。此外，最大有效连接将假设的 EZ 连接到集线器。最后，优化切除与基于结构网络特征的切除相比，在减少假设 EZ 的 EC 和癫痫发作扩散方面同样有效或更有效。通过考虑患者个体脑结构网络的独特拓扑结构，使用减少的 EC 作为模拟癫痫发作传播的替代方法可以提出更严格的切除策略，同时在减少癫痫发作传播方面获得几乎最佳的效果。