The significance of dynamic nonlinearities in the autonomic regulation of heart rate

Abstract

The beat-to-beat regulation of heart rate (HR) is achieved through a beautifully elaborate but not well-understood system of dynamic interactions between sympathetic and parasympathetic (vagal) components of the autonomic nervous system. While numerous investigators consistently have reported dramatic nonlinearities in the HR response to concurrent activation of sympathetic and vagal nerves, a quantitative characterization of such dynamic nonlinearities has been lacking. Therefore, in the present study performed in 8 anesthetized Japanese white rabbits, we surgically isolated vagal and cardiac sympathetic efferent nerves such that we were able to stimulate them simultaneously according to mutually independent, band-limited Gaussian white-noise stimuli as we measured the HR response. Under two-input, single-output system assumptions we took advantage of LYSIS 6.2 software to compute first- and second-order nonlinear self kernels relating input nerve stimulation to HR response, as well as compute the nonlinear cross-kernel that quantified the dynamic interaction between concurrent sympathetic and vagal stimulation on HR response. Our results demonstrated nonlinear kernels that significantly enhanced the predictive validity of our model to predict HR response over results obtained using linear kernels alone. We conclude that dynamic nonlinearities play a quantitatively significant role in mediating the HR response to autonomic activation.