A quantitative physiological model of the autonomic nervous system for simulation of heart rate variability under normal and pathophysiological conditions

Abstract

Cardiovascular system is a self regulatory system; instantaneous heart rate goes on newlinechanging under steady state conditions. Heart rate regulation is the primary mechanism newlineby which the body ensures adequate blood flow in the body. This dynamic process is newlineorchestrated by the Autonomic Nervous system (ANS). It is achieved by closely newlinecontrolling the neurotransmitter kinetics at the sympathetic and parasympathetic sites. newlineANS innervates all vital organs which are under involuntary control. Heart rate newlinevariability is a noninvasive index which indicates the integrity of the ANS. newlineA perusal of literature shows that there is a fairly good qualitative understanding of newlineHeart Rate Variability (HRV) and its underpinnings. But a quantitative physiological newlinemodel capable of simulating heart rate variability and explaining its properties is yet to newlinecome. The present study is the development of a computational model of the autonomic newlinenervous system for simulating heart rate variability in response to simultaneous newlinestimulation at the sympathetic and parasympathetic inputs. A quantitative model of this newlinecomplex physiological process in normal and pathophysiological states may help us to newlinedesign innovative diagnostic methods and targeted treatment strategies. The model is used to investigate the neurobiological basis of the autonomic dysfunction newlineobserved in Alzheimer s disease. This study indicates the possible use of personalized newlinemodels in the management of Alzheimer s disease. The developed physiological ANS newlinemodel is compared with a nonlinear network oscillator model to evaluate the newlinecapabilities and limitations of the developed model. The physiological model being newlinerooted in the actual neurobiological reactions and since it uses experimentally newlinedetermined rate constants; the model captures inherent properties of heart rate newlinevariability. The explicit nature of the model frame work allows further refinement newlinethrough incorporating more reactions and modifying the existing ones. newline