Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Sci Rep. 2021 Sep 24;11(1):19025. doi: 10.1038/s41598-021-98046-0.
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.
癫痫手术在耐药性癫痫患者中的成功取决于正确识别致痫区（EZ）和最佳选择切除区域。在这项研究中，我们基于结构脑网络开发了个体化计算模型，以探索不同虚拟切除对癫痫发作传播的影响。在 19 名患者的术前弥散张量成像中，我们将癫痫发作建模为个体结构网络上的传染病过程[易感-感染-恢复（SIR）模型]。虚拟切除的候选连接是从临床假设的 EZ 开始的所有连接，癫痫发作就是从这里开始的，到其他大脑区域。作为 SIR 模型的计算可行替代方案，我们还删除了最大程度降低假设的 EZ 的特征向量中心度（EC）（较大的值表示网络枢纽）的连接，较大的降低意味着较大的影响。通过模拟退火找到最大效果的最佳连接组合。作为比较，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度量。我们发现，通过删除不到 90%的连接，就可以获得 90%的效果（定义为假设的 EZ 的 EC 降低）。因此，较小的、优化的虚拟切除可以达到与实际手术几乎相同的效果，但成本要低得多，平均节省 27.49%（标准偏差：4.65%）的连接。此外，最大有效的连接将假设的 EZ 与枢纽连接起来。最后，优化的切除与基于结构网络特征的切除一样有效，无论是在降低假设的 EZ 的 EC 还是在抑制癫痫发作传播方面。使用降低的 EC 作为模拟癫痫发作传播的替代方法的方法可以建议更严格的切除策略，同时通过考虑患者个体结构脑网络的独特拓扑结构，获得几乎最佳的降低癫痫发作传播的效果。
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