Modeling Autonomic Regulation of the Cardiovascular System

模拟心血管系统的自主调节

• 批准号: 1022688
• 负责人: Mette Olufsen
• 金额: $ 25万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1022688&HistoricalAwards=false
• 关键词: Modeling; Autonomic; Regulation; Cardiovascular; System

The autonomic nervous system is complex with many interacting components. This is why analysis of separate elements does not give a satisfactory view of the syncope mechanisms. In this study, the aim is to achieve a better understanding of the control mechanisms and their dynamics via patient specific mathematical modeling where knowledge of the individual elements and their dynamics is integrated. To this end, a dynamic cardiovascular system model coupled with a control model is developed that allows the investigators to predict the autonomic nervous system's ability to adjust the heart and vessel properties to maintain blood pressure and pumping function of the heart at reference levels. Several control models are considered including a detailed cellular model allowing prediction of the afferent baroreflex firing-rate based on analysis of ionic currents, a lumped model predicting efferent responses (changes in heart and vascular properties) as a function of sympathetic and parasympathetic outflow, and a coarse model directly predicting efferent responses. For the latter model, the applicability of receding horizon control theory is investigated. These models are composed of nonlinear dynamical systems whose solution poses considerable computational challenges. Their application to clinical data involves computational and conceptual complications due to the inherent noise in the model and data. To ensure high fidelity of our model, the investigators employ methodologies allowing computation of parameter sensitivity, identifiability, and estimation. Sensitivity and identifiability analyses are used to formulate guidelines for model calibration including the selection of parameters best suited for estimation. In particular, the nonlinear Kalman filter based approach is considered for the parameter estimation problem. This method possesses several desirable properties, among them: it takes explicitly into account noise in the model and data, it is an efficient and simple to implement computational tool, and it can take into account a priori information. A simple change of the body from supine to sitting or standing position requires activation of a series of control mechanisms to maintain homeostasis. Upon postural change, the baroreceptors register a fall in arterial blood pressure and reduced filling of the heart. Activation of the autonomic nervous system then adjusts the heart and vessel properties to increase pressure and pump function of the heart back toward their reference level. This regulation can be disrupted in patients with peripheral and central nervous system diseases. Such patients usually experience dizziness and syncope due to altered function of the autonomic nervous system. These defects are often observed in patients with diabetes, hypertension, and other neurological diseases of which Parkinson?s disease is the most dominating. This project brings to bear tools from mathematical modeling, analysis, and computation on questions related to this phenomenon, with the goal of gaining insights into the dynamics involved and a better understanding of how this regulatory system functions. Additionally, the project involves interdisciplinary collaborations with experimentalists and includes the participation of students, providing considerable opportunities for broader impacts.

自主神经系统是复杂的，有许多相互作用的组件。 这就是为什么单独元素的分析不能给出令人满意的晕厥机制的观点。 在这项研究中，目的是通过患者特定的数学建模，其中集成了各个元素及其动态的知识，以更好地理解控制机制及其动态。 为此，一个动态的心血管系统模型加上控制模型的开发，使研究人员能够预测自主神经系统的能力，以调整心脏和血管的属性，以保持血压和泵功能的心脏在参考水平。 几个控制模型被认为包括一个详细的细胞模型，允许预测的传入压力反射发射率的基础上分析的离子电流，预测传出反应（心脏和血管特性的变化）作为交感神经和副交感神经流出的函数的集总模型，和一个粗糙的模型直接预测传出反应。 对于后者，滚动时域控制理论的适用性进行了研究。 这些模型是由非线性动力系统，其解决方案带来了相当大的计算挑战。 由于模型和数据中的固有噪声，它们在临床数据中的应用涉及计算和概念上的复杂性。 为了确保我们的模型的高保真度，研究人员采用的方法，允许计算参数的灵敏度，可识别性和估计。 敏感性和可识别性分析用于制定模型校准指南，包括选择最适合估计的参数。 特别是，非线性卡尔曼滤波器为基础的方法被认为是参数估计问题。 该方法具有几个理想的属性，其中包括：它明确考虑了模型和数据中的噪声，它是一种高效且易于实现的计算工具，并且它可以考虑先验信息。身体从仰卧位到坐姿或站姿的简单改变需要激活一系列控制机制来维持体内平衡。 当姿势改变时，压力感受器记录动脉血压的下降和心脏充盈的减少。 自主神经系统的激活然后调整心脏和血管特性以增加心脏的压力和泵送功能回到其参考水平。 患有外周和中枢神经系统疾病的患者的这种调节可能会被破坏。 这类患者通常会因自主神经系统功能改变而出现头晕和晕厥。 这些缺陷经常在糖尿病、高血压和其他神经系统疾病患者中观察到，其中帕金森？的疾病是最主要的。 这个项目带来了承担工具，从数学建模，分析和计算有关的问题，这一现象，以获得洞察所涉及的动态和更好地了解这个调节系统的功能的目标。 此外，该项目涉及与实验人员的跨学科合作，并包括学生的参与，为更广泛的影响提供了相当多的机会。