  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.
