Simulating Skin Sympathetic Nerve Activity: A Mathematical Model

Altered neuro vegetative regulation may importantly impact on cardiovascular diseases, and can lead to impaired cardiovascular homeostasis. This has long been recognized, particularly in conditions like hypertension. Understanding such neurovegetative dysregulation might have important implications for the management for different cardiovascular diseases. Recently, skin sympathetic nerve activity has been measured noninvasively, and suggested as a very accessible parameter to quantify autonomic tone and its impact on a patient’s cardiovascular system. Moreover, its average value (aSKNA) has been proposed as a novel marker in clinical settings, including the screening of some cardiovascular diseases. In this study, we designed a model featuring the most relevant cardiovascular physiology to simulate heart rate changes and skin sympathetic nerve activity. To validate the model, we collected data while healthy subjects performing Valsalva manoeuvre (VM) to assess a patient's neurovegetaive regulation. The test can significantly decrease venous return and arterial blood pressure and in turn activate the sympathetic nervous system. Although the underlying mechanism is complex and in part potentially nonlinear, VM is modelled in a dedicated way to focus on the baroreflex and assuming a linear relationship between sympathetic tone and aSKNA. This approach aims to reduce model complexity while retaining the essential physiological mechanisms. Ethics approval was obtained at the Natural & Engineering Sciences Ethics committee of the University of Twente. A total of 41 healthy volunteers were enrolled in the experiment, where each subject participated in two measurements, performing two VMs in each. A burst analysis was utilized firstly to identify if the manoeuvre was performed correctly. Finally, a total of 37 measurements were analyzed. The average root mean square error between the predicted and measured aSKNA is 0.46μV and the Pearson correlation coefficient between them is 0.85. Figure 1. shows an example of the comparison between the predicted and observed aSKNA. In conclusion, the model is expected to improve the understanding of autonomic cardiac regulation during VM, as evidenced by the promising agreement between the simulation and measured aSKNA. It has the potential for further development in estimating autonomic cardiac tone in patients with autonomic dysregulation during Valsalva manoeuvre. This provides insights into the mechanism of relevant disease and presents opportunities for the detection of autonomic dysfunction.