癫痫活动的唯象介观模型
Phenomenological Mesoscopic Models for Seizure Activity
摘要：
在这一章中，我们回顾了癫痫样活动的现象学模型。我们讨论了癫痫发作和抵消，preictal尖峰，尖峰和波复合物和癫痫持续状态的动力学机制，突出了模型的分叉结构，噪声的存在和多个相互作用的时间尺度的出现所发挥的作用。这些模型可用于构建大规模患者特定的大脑网络模型，作为计算机平台来测试临床假设和执行虚拟手术。他们提出了创新的治疗策略，如微创消融或刺激，充分利用系统的网络和动力学特性，甚至调制变量和参数，迫使系统在分叉图的安全区域。我们讨论的见解，从现象学模型，可以帮助促进我们了解癫痫发作的机制。
英文摘要：
In this chapter we review phenomenological models of seizure like activity. We discuss dynamical mechanisms for seizure onset and offset, preictal spikes, spike and wave complexes and status epilepticus, highlighting the role played by the bifurcation structure of the model, the presence of noise and the emergence of multiple interacting time-scales. These models can be used to build large-scale patient specific brain network models serving as in-silico platforms to test clinical hypothesis and perform virtual surgeries. They suggest innovative treatment strategies, such as minimally invasive ablations or stimulations that fully exploit the network and dynamical properties of the system, or even modulation of variables and parameters to force the system in safer regions of the bifurcation diagram. We discuss insights from phenomenological models that can help to foster our understanding of the mechanisms underlying epileptic seizures.
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