Tonic-clonic transitions in computer simulation
Tonic-clonic transitions in computer simulation
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DOI：
10.1097/wnp.0b013e3180336fc0
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发表时间：
2007-04-01
影响因子：
2.4
通讯作者：
Omurtag, Ahmet
中科院分区：
文献类型：
--
作者：
Lytton, William W.;Omurtag, Ahmet
网络模拟可以帮助识别在生物制剂中难以分离的癫痫活动的潜在机制。为了有用，模拟必须足够逼真，以进行可能的生物和临床预测。这种对足够详细的神经元的大型网络的要求提出了关于计算负载和获得具有大量自由参数和大量生成数据的见解的难度的挑战。作者通过模拟由1,000到3,000个具有多种内在和突触特性的神经元组成的中等大小的计算可管理网络来解决这些问题。对这些模拟的实验证明了癫痫样行为的存在，其形式为重复的高强度群体事件（阵挛性行为）或具有接近最大活动的闭锁（强直性行为）。内在神经元兴奋性并不总是网络癫痫样活动的预测因子，但可能矛盾地产生抗癫痫作用，这取决于其他参数的设置。几个模拟揭示了随机重合输入的重要性，将网络从低激活状态转换为高激活癫痫状态。最后，一个模拟的抗惊厥剂作用于兴奋性倾向于优先减少紧张性活动。
Network simulations can help identify underlying mechanisms of epileptic activity that are hard to isolate in biologic preparations. To be useful, simulations must be sufficiently realistic to make possible biologic and clinical prediction. This requirement for large networks of sufficiently detailed neurons raises challenges both with regard to computational load and the difficulty of obtaining insights with large numbers of free parameters and the large amounts of generated data. The authors have addressed these problems by simulating computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic and synaptic properties. Experiments on these simulations demonstrated the presence of epileptiform behavior in the form of repetitive high-intensity population events (clonic behavior) or latch-up with near maximal activity (tonic behavior). Intrinsic neuronal excitability is not always a predictor of network epileptiform activity but may paradoxically produce antiepileptic effects, depending on the settings of other parameters. Several simulations revealed the importance of random coincident inputs to shift a network from a low-activation to a high-activation epileptiform state. Finally, a simulated anticonvulsant acting on excitability tended to preferentially decrease tonic activity.