From channel protein to body surface ECG: Investigating electrocardiographic effects of ion channel mutations using computational heart models

从通道蛋白到体表心电图：使用计算心脏模型研究离子通道突变的心电图效应

• 批准号: 78886816
• 负责人: Dr. Sebastian Ley
• 金额: --
• 依托单位: Klinik für Diagnostische und Interventionelle Radiologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2009
• 资助国家: 德国
• 起止时间: 2008-12-31 至 2012-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/78886816?language=en
• 关键词: channel; protein; body; surface; ECG

Mutations that impact on cardiac electrophysiology have been of scientific interest throughout the last decade. By altering specific ion channel characteristics these mutations are associated with a high risk for the development of ventricular arrhythmias and sudden death. Although DNA analysis has enhanced our understanding of the underlying processes, inter-individual differences impede to quantitatively predict the effects of the mutated channels on the ECG. This project is focused on identifying and discriminating genotypes of mutations in a cardiac channelopathy called Long QT syndrome (LQTS) using a computational approach. Geometrical datasets of affected patients are constructed based on MRI. Together with detailed mathematical models of cardiac electrophysiology, the excitation conduction on the individual cardiac anatomy is simulated. A forward calculation allows the determination of electrical signals on the body surface and the extraction of standard ECG leads. For verification and optimization of the computational model, this calculated signals on the body surface is compared to 64 channel ECG recordings from patients. Effects of the sympathetic nervous system are included into the computer model of the LQTS as heart rate and adrenergic regulation influence ion channel characteristics and thereby cardiac activity. Especially the differences of the T wave morphology between the genotypes of LQTS will be used to derive ECG electrode positions that allow a new ECG-based discrimination of the mutation genotype.

在过去的十年中，影响心脏电生理学的突变一直受到科学界的关注。通过改变特定的离子通道特性，这些突变与室性心律失常和猝死的高风险发展相关。尽管DNA分析增强了我们对潜在过程的理解，但个体间差异阻碍了定量预测突变通道对心电图的影响。该项目的重点是使用计算方法识别和区分称为长QT综合征（LQTS）的心脏通道病中的突变基因型。基于MRI构建受影响患者的几何数据集。结合心脏电生理学的详细数学模型，模拟了个体心脏解剖结构上的激励传导。正向计算允许确定身体表面上的电信号并提取标准ECG导联。为了验证和优化计算模型，将体表上的该计算信号与来自患者的64通道ECG记录进行比较。交感神经系统的作用被包括在LQTS的计算机模型中，因为心率和肾上腺素能调节影响离子通道特性，从而影响心脏活动。特别是LQTS基因型之间的T波形态的差异将被用于推导ECG电极位置，其允许新的基于ECG的突变基因型的区分。