In humans skin blood flow (SBF) plays a major role in body heat loss. Therefore the accuracy of models ofhuman thermoregulation depends for a great deal on their ability to predict skin blood flow. Most SBFmodelsuse body temperatures directly for calculation of skin perfusion. However, humans do not sensetemperature directly, yet the information is coded into neuron fire rates. The aim of this study was toinvestigate whether SBF can be adequately modelled through simulation of temperature sensitive neuronsand neuro-physiological pathways of excitation and inhibition. Methods: In this study a mathematical modelfor SBF was developed based on physiological knowledge on neural thermo-sensitivity and neuralpathways. The model was fitted on human experimental data. Mean squared residuals (MSR) wereestimated through k-fold cross-validation. Results: The model adequately explains the variance of themeasurements (r^2=0.91). Furthermore the averaged MSR is close to the natural variation in themeasurements (AMSR=0.087 vs. sigma^2=0l.080) indicating a small bias. Conclusion: In this study wedeveloped a model for skin perfusion based on physiological evidence on thermo-reception and neuralpathways. Given the highly explained variance this study shows that a neuro-physiological approach isapplicable for modelling skin blood flow in thermoregulation.
|Title of host publication||Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation, 24-26 October 2010, Valencia, Spain|
|Place of Publication||Spain, Valencia|
|Publisher||SciTePress Digital Library|
|Publication status||Published - 2010|