Key result
A simplified second-order nonlinear model predicted arterial pressure changes 15% better than a linear model in response to Gaussian white noise carotid sinus pressure data.
Effect estimate: 15% better prediction
A nonlinear dynamic model of the total baroreflex arc predicts arterial pressure changes better than a linear model, demonstrating that nonlinearity mainly arises from the neural arc.
Supports nonlinear baroreflex modeling in animals; leaves open translation to human arterial pressure prediction.
The total baroreflex arc – the open‐loop system relating carotid sinus pressure (CSP) to arterial pressure (AP) – exhibits nonlinear behaviors such as saturation, thresholding, and steady‐state responses to pulsatile changes. However, few studies have quantitatively characterized the nonlinear system dynamics. The aim was to develop a nonlinear dynamic model of the total baroreflex arc. Five normal rats were studied according to a protocol approved by the Animal Subjects Committee of the National Cerebral and Cardiovascular Center. Under anesthesia, the vagal and aortic arch depressor nerves were sectioned; the carotid sinus regions were isolated and attached to a servo‐controlled piston pump; and AP and sympathetic nerve activity (SNA) were measured. CSP was perturbed using a Gaussian white noise signal. A second‐order Volterra model was established using nonparametric identification. The second‐order kernel was mainly diagonal, but the main diagonal was not the same as the first‐order kernel. A simplified second‐order nonlinear model was accordingly developed using nonparametric identification. This model predicted AP changes 15% better than a linear model in response to Gaussian white noise CSP data not used in model development. Linear and nonlinear models of the neural arc – the system relating CSP to SNA – and peripheral arc – the system relating SNA to AP – were likewise established. These two models indicated that the nonlinearity of the total baroreflex arc mainly arises from the neural arc rather than the peripheral arc.
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Moslehpour et al. (2015) studied Normal rats (n=5). Second-order nonlinear model vs. Linear model was evaluated on Prediction of arterial pressure (AP) changes (15% better prediction). A simplified second-order nonlinear model predicted arterial pressure changes 15% better than a linear model in response to Gaussian white noise carotid sinus pressure data.
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