Complexity-Based Analysis of Microvascular Blood Flow in Human Skin

The maintenance of an adequate microvascular perfusion sufficient to meet the metabolic demands of the tissue is dependent on neural, humoral and local vaso-mechanisms that determine vascular tone and blood flow patterns within a microvascular network. It has been argued that attenuation of these flow patterns may be a major contributor to disease risk. Thus, quantitative information on the in vivo spatio-temporal behaviour of microvascular perfusion is important if we are to understand network functionality and flexibility in cardiovascular disease. Time and frequency-domain analysis has been extensively used to describe the dynamic characteristics of Laser Doppler flowmetry (LDF) signals obtained from superficial microvascular networks such as that of the skin. However, neither approach has provided definitive and consistent information on the relative contribution of the oscillatory components of flowmotion (endothelial, neurogenic, myogenic, respiratory and cardiac) to a sustained and adequate microvascular perfusion; nor advance our understanding of how such processes are collectively modified in disease. More recently, non-linear complexity-based approaches have begun to yield evidence of a declining adaptability of microvascular flow patterns as disease severity increases. In this chapter we review the utility and application of these approaches for the quantitative, mechanistic exploration of microvascular (dys)function.