Incorporating neurophysiological concepts in mathematical thermoregulation models.

Area of Science:

• Physiology
• Computational Biology
• Thermoregulation

Background:

• Skin blood flow (SBF) is crucial for human thermoregulation, especially during thermal challenges.
• Existing numerical models for SBF regulation often lack explicit incorporation of thermal reception neurophysiology.
• A gap exists in modeling SBF control that accurately reflects the neurophysiological pathways involved in thermoregulation.

Purpose of the Study:

• To test a novel skin blood flow (SBF) model integrating thermal reception neurophysiology.
• To evaluate the SBF model's performance within a numerical thermoregulation model (ThermoSEM) for skin temperature simulation.
• To validate the neurophysiological SBF model against experimental data from transient thermal challenges.

Main Methods:

• Developed a new SBF model based on experimental data of thermal reception and neurophysiological pathways.
• Integrated the SBF model into the ThermoSEM thermoregulation model.
• Quantified prediction error using root-mean-squared-residual (RMSR) comparing model simulations with SBF and temperature measurements from young males.

Main Results:

• The neurophysiological SBF model predicted SBF with high accuracy (RMSR < 0.27).
• Simulated skin temperature (Tskin) results were within 0.37°C of measured mean skin temperature.
• Model validation confirmed its ability to predict SBF and skin temperature under thermal challenges.

Conclusions:

• Mathematical models can effectively capture thermal reception and neurophysiological pathways for SBF control.
• Human thermoregulation models can integrate neurophysiologically-based SBF control functions without compromising performance.
• A neurophysiological approach to thermoregulation modeling offers advantages over engineering approaches due to better alignment with physiology.