Phenomenological network models: Lessons for epilepsy surgery
Phenomenological network models: Lessons for epilepsy surgery
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DOI：
10.1111/epi.13861
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发表时间：
2017-10-01
期刊：
影响因子：
5.6
通讯作者：
Leijten, Frans
中科院分区：
文献类型：
--
作者：
Hebbink, Jurgen;Meijer, Hil;Leijten, Frans
癫痫外科目前的观点是，成功的手术是在解剖学意义上切除病理皮层。这与癫痫研究的最新进展形成鲜明对比，癫痫被视为一种网络疾病。计算模型提供了一个框架来研究网络的影响，以及局部组织特性，并探索替代切除策略。在这里，我们研究，使用这样一个模型，连接癫痫发作的影响，以及这可能如何改变我们的传统观点癫痫手术。我们使用一个简单的网络模型，由四个相互连接的神经元群体。这些群体中的一个可以变得过度兴奋，模拟皮质的病理区域。使用模型模拟，手术对癫痫发作率的影响进行了研究。我们发现，在大多数情况下，去除过度兴奋的人群并不是降低癫痫发作率的最佳方法。移除位于网络中关键点的正常群体，即驱动程序，通常在降低癫痫发作率方面更有效。这项工作加强了网络结构和连接可能比定位病理节点更重要的想法。这可以解释为什么病变切除术可能并不总是足够的。
The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the driver, is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient.