Input-size dependence of the baroreflex neural arc transfer characteristics during Gaussian white noise inputs.

Abstract

Although Gaussian white noise (GWN) inputs offer a theoretical framework for identifying higher-order nonlinearity, an actual application to the data of the neural arc of the carotid sinus baroreflex did not succeed in fully predicting the well-known sigmoidal nonlinearity. In the present study, we assumed that the neural arc can be approximated by a cascade of a linear dynamic (LD) component and a nonlinear static (NS) component. We analyzed the data obtained using GWN inputs with a mean of 120 mmHg and standard deviations (SDs) of 10, 20, and 30 mmHg for 15 min each in anesthetized rats (n = 7). We first estimated the linear transfer function from carotid sinus pressure to sympathetic nerve activity (SNA) and then plotted the measured SNA against the linearly predicted SNA. The predicted and measured data pairs exhibited an inverse sigmoidal distribution when grouped into 10 bins based on the size of the linearly predicted SNA. The sigmoidal nonlinearity estimated via the LD-NS model showed a midpoint pressure (104.1 ± 4.4 mmHg for SD of 30 mmHg) lower than that estimated by a conventional stepwise input (135.8 ± 3.9 mmHg, P < 0.001). This suggests that the NS component is more likely to reflect the nonlinearity observed during pulsatile inputs that are physiological to baroreceptors. Furthermore, the LD-NS model yielded higher R² values compared with the linear model and the previously suggested second-order Uryson model in the testing dataset.NEW & NOTEWORTHY We examined the input-size dependence of the baroreflex neural arc transfer characteristics during Gaussian white noise inputs. A linear dynamic-static nonlinear model yielded higher R² values compared with a linear model and captured the well-known sigmoidal nonlinearity of the neural arc, indicating that the nonlinear dynamics contributed to determining sympathetic nerve activity. Ignoring such nonlinear dynamics might reduce our ability to explain underlying physiology and significantly limit the interpretation of experimental data.