Electronic Thesis and Dissertation Repository


Doctor of Philosophy


Medical Biophysics


Dr. Dwayne Jackson, and Dr. Daniel Goldman


The effect of the sympathetic nervous system (SNS) on blood flow distribution within skeletal muscle microvasculature is conditional upon regional activation of SNS receptors. Due to a lack of appropriate experimental models and techniques, no study has systematically evaluated the effect of SNS receptor activation in continuously branching skeletal muscle arteriolar trees. In line with previous work, we hypothesize that there will be a spatially-dependent distribution of sympathetic receptor activation along the arteriolar tree. Specifically, we anticipate a progressive decrease of adrenergic activation and a progressive increase of peptidergic and purinergic activation with increasing arteriolar order. We developed a novel rat gluteus maximus (GM) muscle preparation which provided access to a large vascular network, from which we developed an experimental method for collecting cell velocity profiles in fast-flowing arterioles. Using these data, we derived an empirical relationship between velocity ratio (VMax/VMean) and arteriolar diameter, collected novel data on cell free layer width and estimated wall shear rates, and derived a wall shear rate equation from experimental data that can be used for calculating wall shear rates in skeletal muscle microvasculature. We evaluated SNS receptor activation (α1R, α2R, NPY1R, and P2X1R) in continuously branching arteriolar trees in the rat GM, as a function of network topology. A computational flow model estimated the total flow, resistance, and red blood cell flow heterogeneity. For the first time, we highlight effects of SNS receptor activation on network hemodynamics, where proximal arterioles responded most to adrenergic activation, while distal arterioles responded most to Y1R and P2X1R activation. Our data highlight the functional consequences of topologically-dependent SNS receptor activation. The tools developed in this thesis are beneficial for computing hemodynamic parameters from in vivo data, as well as providing input variables to and validation of computational flow models.