Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.
Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.
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使用计算建模预测局灶性癫痫患者的神经外科结局。
DOI：
10.1093/brain/aww299
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发表时间：
2017-02
期刊：
影响因子：
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通讯作者：
Taylor PN
中科院分区：
文献类型：
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作者：
Sinha N;Dauwels J;Kaiser M;Cash SS;Brandon Westover M;Wang Y;Taylor PN
参见Eissa和Schevon（doi：）对本文的科学评论。在任何影像学检查都不明显的情况下，局灶性癫痫的神经外科治疗是高度不可预测的。Sinha等人提出了一种计算方法来预测手术前计划切除的结果，并研究癫痫网络的病理生理学，以描绘对发作至关重要的皮质区域。 请参阅Eissa和Schevon（doi：）对本文的科学评论。手术可能是难治性、难治性癫痫患者的最后手段。然而，对于这些患者中的许多人来说，手术无效的风险很大。预测谁可能从手术方法中受益对于能够更好地告知患者，进行原则性前瞻性临床试验，并最终更有效地为这些患者量身定制治疗方法至关重要。动态计算模型，与病人的数据，可以用来作出预测，并给出机械的见解。在这项研究中，我们开发了患者特定的癫痫皮层的动力学网络模型。我们从患者的非癫痫发作电图记录中推断网络连接矩阵，并使用这些连接矩阵作为我们模型中的网络结构。该模型模拟了该网络中每个节点的双稳态开关的动态特性，这意味着每个节点都以背景状态开始，但有能力过渡到共存的癫痫发作状态。节点中是否发生转移部分取决于节点输入的随机性，但也取决于节点从网络中其他连接节点接收的输入。通过使用这样的模型进行模拟，我们可以检测给定网络中节点的平均转换时间，因此将具有短转换时间的节点定义为高度致癫痫的节点。在一项回顾性研究中，我们发现在一些患者中，模型中具有高致痫性的区域与临床上确定为癫痫发作区的区域重叠。此外，发现在模型中切除这些区域降低了癫痫发作的总体可能性。在模型中去除这些区域后，我们预测了手术结果，并将其与实际患者结果进行了比较。在16例难治性癫痫患者的数据集上，我们的预测准确率为81.3%。有趣的是，在不成功的结果的患者中，所提出的计算方法能够建议替代切除部位。这里提出的模型提供了机械的见解，为什么手术可能是不成功的，在一些患者。这可以通过提供一种工具来探索各种手术选择，为现有的临床技术提供补充信息，从而帮助临床医生进行术前评估。
See Eissa and Schevon (doi:) for a scientific commentary on this article. Neurosurgical treatment of focal epilepsy is highly unpredictable in cases where a lesion is not apparent by any imaging modality. Sinha et al. propose a computational approach to predict the outcome of a planned resection prior to surgery and investigate the pathophysiology of epileptic networks to delineate cortical areas crucial for ictogenesis. See Eissa and Schevon (doi:) for a scientific commentary on this article. Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.