1. Multiscale, multidomain analysis of microvascular flow dynamics
- Author
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Geraldine F. Clough, Marjola Thanaj, and Andrew J. Chipperfield
- Subjects
Physiology ,Computer science ,030204 cardiovascular system & hematology ,Machine learning ,computer.software_genre ,Microcirculation ,03 medical and health sciences ,0302 clinical medicine ,Physiology (medical) ,Laser-Doppler Flowmetry ,Multiple time ,Humans ,In patient ,Skin ,Nutrition and Dietetics ,business.industry ,General Medicine ,Blood flow ,Laser Doppler velocimetry ,Microvascular Network ,Regional Blood Flow ,Healthy individuals ,Microvessels ,Artificial intelligence ,business ,computer ,030217 neurology & neurosurgery ,Microvascular flow - Abstract
New findings What is the topic of this review? We describe a range of techniques in the time, frequency and information domains and their application alone and together for the analysis of blood flux signals acquired using laser Doppler fluximetry. What advances does it highlight? This review highlights the idea of using quantitative measures in different domains and scales to gain a better mechanistic understanding of the complex behaviours in the microcirculation. Abstract To date, time- and frequency-domain metrics of signals acquired through laser Doppler fluximetry have been unable to provide consistent and robust measures of the changes that occur in the microcirculation in healthy individuals at rest or in response to a provocation, or in patient cohorts. Recent studies have shown that in many disease states, such as metabolic and cardiovascular disease, there appears to be a reduction in the adaptive capabilities of the microvascular network and a consequent reduction in physiological information content. Here, we introduce non-linear measures for assessing the information content of fluximetry signals and demonstrate how they can yield deeper understanding of network behaviour. In addition, we show how these methods may be adapted to accommodate the multiple time scales modulating blood flow and how they can be used in combination with time- and frequency-domain metrics to discriminate more effectively between the different mechanistic influences on network properties.
- Published
- 2020