Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model.
Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model.
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DOI：
10.1098/rsif.2016.0968
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发表时间：
2017-03
期刊：
影响因子：
--
通讯作者：
Clayton RH
中科院分区：
文献类型：
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作者：
Lin YT;Chang ET;Eatock J;Galla T;Clayton RH
心脏电兴奋的数学模型越来越复杂，多尺度模型试图在时间和空间尺度上表示和连接生理行为。这些模型日益增加的复杂性使得评估长期（超过60秒）行为和确定模型输出对输入的敏感性在计算上都很昂贵。这在房颤（AF）模型中尤其重要，其中单个发作持续数秒到数天，而发作间等待时间可能是几分钟到几个月。窦性心律和房颤之间转换的潜在机制已经确定，但尚未得到很好的理解，并且很难使用最先进的模型长时间模拟房颤。在这项研究中，我们在一个新颖的、拓扑等效的左心房表面几何结构上实现了一个moe型元胞自动机。我们使用该模型来模拟AF的随机起始和自发终止，由肺静脉附近的自发激活爆发引起。心房电活动的简化表示减少了计算成本，因此允许我们在概率设置中研究心房颤动机制。我们计算了大数（大约）。105)模型的样本路径，以推断不同模型参数下AF发作的随机起始和终止率。通过生成模型输出的统计分布，我们演示了如何将微观水平模型内输入的不确定性传播到宏观水平。最后，我们研究了模型中的自发终止，并发现其对过去AF轨迹的复杂依赖，其机制值得进一步研究。
Mathematical models of cardiac electrical excitation are increasingly complex, with multiscale models seeking to represent and bridge physiological behaviours across temporal and spatial scales. The increasing complexity of these models makes it computationally expensive to both evaluate long term (more than 60 s) behaviour and determine sensitivity of model outputs to inputs. This is particularly relevant in models of atrial fibrillation (AF), where individual episodes last from seconds to days, and interepisode waiting times can be minutes to months. Potential mechanisms of transition between sinus rhythm and AF have been identified but are not well understood, and it is difficult to simulate AF for long periods of time using state-of-the-art models. In this study, we implemented a Moe-type cellular automaton on a novel, topologically equivalent surface geometry of the left atrium. We used the model to simulate stochastic initiation and spontaneous termination of AF, arising from bursts of spontaneous activation near pulmonary veins. The simplified representation of atrial electrical activity reduced computational cost, and so permitted us to investigate AF mechanisms in a probabilistic setting. We computed large numbers (approx. 105) of sample paths of the model, to infer stochastic initiation and termination rates of AF episodes using different model parameters. By generating statistical distributions of model outputs, we demonstrated how to propagate uncertainties of inputs within our microscopic level model up to a macroscopic level. Lastly, we investigated spontaneous termination in the model and found a complex dependence on its past AF trajectory, the mechanism of which merits future investigation.