Key result
A linear dynamic-static nonlinear model yielded higher R2 values compared with a linear model and captured the sigmoidal nonlinearity of the baroreflex neural arc.
Why the study?
The input-size dependence of the baroreflex neural arc transfer characteristics during Gaussian white noise inputs needed to be examined.
Incorporating nonlinear dynamics in modeling the baroreflex neural arc improves the explanation of underlying physiology compared to linear models.
Nonlinear baroreflex modeling may improve sympathetic activity predictions; leaves open clinical translation in autonomic regulation.
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 R 2 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.
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Kawada et al. (2023) studied this question. Linear dynamic-static nonlinear model vs. Linear model was evaluated on R2 values and sigmoidal nonlinearity of the neural arc. A linear dynamic-static nonlinear model yielded higher R2 values compared with a linear model and captured the sigmoidal nonlinearity of the baroreflex neural arc.
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