Epilepsy surgery: Evaluating robustness using dynamic network models
Epilepsy surgery: Evaluating robustness using dynamic network models
复制标题
DOI：
10.1063/5.0022171
复制
发表时间：
2020-11-01
期刊：
影响因子：
2.9
通讯作者：
Terry, John R.
中科院分区：
文献类型：
--
作者：
Junges, Leandro;Woldman, Wessel;Terry, John R.
癫痫是最常见的神经系统疾病之一，影响全球超过6500万人。超过三分之一的癫痫患者被认为是难治性的：他们对药物治疗没有反应。对于这个重要的人群，手术是一种潜在的变革性治疗方法。然而，只有一小部分难治性癫痫患者被认为适合手术，并且只有一半的病例可以实现长期无癫痫发作。最近，已经提出了几种计算方法来支持术前计划。通常，这些方法使用动态网络模型来探索手术切除的潜在影响。模型的网络组件由临床成像数据告知，并且此后被认为是静态的。这种假设严重忽视了大脑的可塑性，因此，手术后大脑网络的持续演变可能会影响切除术的长期成功。在这项工作中，我们使用一个简化的动态网络模型，它描述了癫痫发作的过渡，系统地探讨网络结构如何影响癫痫发作的倾向，无论是在虚拟切除术之前和之后。在将我们的发现扩展到更大的网络之前，我们在小型网络中说明了关键结果。我们展示了切除术后大脑网络的演变如何导致癫痫发作倾向的增加。我们的研究结果有效地确定了一个给定的切除可能的网络重新配置的鲁棒性，因此提供了一个潜在的策略，优化长期癫痫发作的自由。
Epilepsy is one of the most common neurological conditions affecting over 65 million people worldwide. Over one third of people with epilepsy are considered refractory: they do not respond to drug treatments. For this significant cohort of people, surgery is a potentially transformative treatment. However, only a small minority of people with refractory epilepsy are considered suitable for surgery, and long-term seizure freedom is only achieved in half the cases. Recently, several computational approaches have been proposed to support presurgical planning. Typically, these approaches use a dynamic network model to explore the potential impact of surgical resection in silico. The network component of the model is informed by clinical imaging data and is considered static thereafter. This assumption critically overlooks the plasticity of the brain and, therefore, how continued evolution of the brain network post-surgery may impact upon the success of a resection in the longer term. In this work, we use a simplified dynamic network model, which describes transitions to seizures, to systematically explore how the network structure influences seizure propensity, both before and after virtual resections. We illustrate key results in small networks, before extending our findings to larger networks. We demonstrate how the evolution of brain networks post resection can result in a return to increased seizure propensity. Our results effectively determine the robustness of a given resection to possible network reconfigurations and so provide a potential strategy for optimizing long-term seizure freedom.