Treatment effects in epilepsy: a mathematical framework for understanding response over time
Treatment effects in epilepsy: a mathematical framework for understanding response over time
复制标题
DOI：
10.1101/2024.01.22.576627
复制
发表时间：
2024-01
期刊：
影响因子：
--
通讯作者：
Gwen Harrington;Peter Kissack;John R. Terry;W. Woldman;Leandro Junges
中科院分区：
文献类型：
--
作者：
Gwen Harrington;Peter Kissack;John R. Terry;W. Woldman;Leandro Junges
癫痫是一种以反复发作为特征的神经系统疾病，影响全球超过6500万人。治疗通常从使用抗癫痫药物开始，包括单药和多药治疗。然而，更多的侵入性治疗，如手术，电刺激和局部药物输送也可以考虑在试图使人癫痫发作免费。虽然很大一部分最终受益于这些治疗方案，但治疗反应往往会随着时间的推移而波动。这些时间变化背后的生理机制知之甚少，使预后成为治疗癫痫的最大挑战之一。在这项工作中，我们使用一个动态网络模型的癫痫发作过渡，以了解如何癫痫发作的倾向可能会随着时间的推移而变化的兴奋性的变化的结果。通过计算机模拟，我们探讨了治疗对动态网络特性的影响与其随时间推移的脆弱性之间的关系，这些脆弱性允许恢复到高癫痫发作倾向的状态。我们发现，对于小型网络，脆弱性可以完全由第一传递组件（FTC）的大小来表征。对于较大的网络，我们发现网络效率，不一致性和异质性（度方差）的措施与网络的鲁棒性增加兴奋性。这些结果为癫痫的治疗干预提供了一组潜在的预后标志物。这些标记物可用于支持个性化治疗策略的发展，最终有助于了解长期癫痫发作的自由。
Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, both mono- and poly-therapy. However more invasive therapies such as surgery, electrical stimulation and focal drug delivery may also be considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time.The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis one of the biggest challenges for treating epilepsy. In this work, we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. We show that, for small networks, vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom.