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.
