A derivative-sigmoidal model reproduces operating point-dependent baroreflex neural arc transfer characteristics.

A cascade model comprised of a derivative filter followed by a nonlinear sigmoidal component reproduces the input size dependence of transfer gain in the baroreflex neural arc from baroreceptor pressure input to efferent sympathetic nerve activity (SNA). We examined whether the same model could predict the operating point dependence of the baroreflex neural arc transfer characteristics estimated by a binary white noise input. In eight anesthetized rabbits, we isolated bilateral carotid sinuses from the systemic circulation and controlled intracarotid sinus pressure (CSP). We estimated the linear transfer function from CSP to SNA while varying mean CSP among 70, 100, 130, and 160 mmHg (P(70), P(100), P(130), and P(160), respectively). The transfer gain at 0.01 Hz was significantly smaller at P(70) (0.61 +/- 0.26) and P(160) (0.60 +/- 0.25) than at P(100) (1.32 +/- 0.42) and P(130) (1.36 +/- 0.45) (in arbitrary units/mmHg; means +/- SD; P < 0.05). In contrast, transfer gain values above 0.5 Hz were similar among the protocols. As a result, the slope of increasing gain between 0.1 and 0.5 Hz was significantly steeper at P(70) (17.6 +/- 3.6) and P(160) (14.1 +/- 4.3) than at P(100) (8.1 +/- 4.4) and P(130) (7.4 +/- 6.6) (in dB/decade; means +/- SD; P < 0.05). These results were consistent with those predicted by the derivative-sigmoidal model, where the deviation of mean input pressure from the center of the sigmoidal nonlinearity reduced the transfer gain mainly in the low-frequency range. The derivative-sigmoidal model functionally reproduces the dynamic SNA regulation by the arterial baroreflex over a wide operating range.