Mathematical models of cardiovascular control by the autonomic nervous system

This PhD thesis develops an integrated mathematical model for autonomic nervous system control on cardiovascular activity. The model extensively covers cardiovascular neural pathways including a wide range of afferent sensory neurons, central processing by autonomic premotor neurons, efferent outputs via preganglionic and postganglionic autonomic neurons and dynamics of neurotransmitters at cardiovascular effectors organs. We performed over 500 cardiovascular experiments using clinical autonomic tests on 72 subjects ranging from 11 to 82 years old and collected typical cardiovascular signals such as electrocardiogram, arterial pulse, arterial blood pressure, respiration pattern, galvanic skin response and skin temperature. After statistical evaluation in the time and frequency domains, the data were especially used to resolving a constrained optimization task. Results bring evidences supporting the hypothesis that Mayer waves result from a rhythmic sympathetic discharge of pacemaker-like sympathetic premotor neurons. Simulation also shows that vagally-mediated tachycardia, observed during vagal maneuvers on some subjects could be related to the secretion of vasoactive neurotransmitters by the vagal nerve. We additionally identified model parameters for estimating the resting sympathetic and parasympathetic tone which are believed to be linked to some pathological states. Results show higher vagal tone on young subjects with a decreasing trend with aging, what agrees with the data from heart rate variability studies. Tonic sympathetic activity was found to possibly emerge from pacemaker premotor neurons, but also from activation of chemoreceptors to a lesser extent. The thesis opens perspectives for future work including validating the markers of autonomic tone provided by our model against data from experiments with pharmacological blockers and invasive neural activity recordings.