Multifiber renal SNA recordings predict mean arterial blood pressure in unanesthetized rat

The goal of this analysis was to quantify the relationship between renal sympathetic nerve activity (SNA) and mean arterial blood pressure (MAP). We previously recorded renal SNA and MAP in conscious rats during a stressful behavioral stimulus and during a nonstressful stimulus. We then formulated a set of two linear, first-order differential equations that uses our SNA recordings after a time delay (the input) to predict fluctuations in MAP (the output). Our model has four parameters: 1) the cardiovascular time constant T that characterizes the frequency response function between the effector elements controlled by the sympathetic nerves and the cardiovascular system (1-5 s); 2) the effector time constant T-e determined by the coupling between the sympathetic nervous system and the effecters (0.0-0.6 s); 3) the efferent time delay tau(e) between a change in SNA and a change in MAP (0.4-0.6 s); and 4) a proportionality constant C between fluctuations in SNA and fluctuations in MAP (0.3-3.4 mmHg/nV). The parameters of the model were determined that minimize the residual error between the simulated time series and the actual data time series for a stressful stimulus. Then we tested the ability of the transfer function to predict the MAP response to a nonstressful stimulus. In five of seven rats tested, the model's predictions were good, with mean cross correlation coefficients for the predicted trials between 0.62 and 0.83. We show that multifiber renal SNA recordings can reliably predict changes in MAP in the unanesthetized rat. Thus the overall sympathetic drive to the cardiovascular system is indexed by renal SNA, although the vasomotor effecters driven by renal SNA control only similar to 20% of the blood flow.