Faculty
Schulich School of Medicine & Dentistry
Supervisor Name
Daniel Goldman & Jefferson Frisbee
Keywords
Laser Doppler Flowmetry, microvascular skin perfusion, blood flow, vasoregulation, local thermal hyperemia, time-frequency analysis, wavelet transform
Description
Microvascular mechanisms that regulate the microcirculation can be observed within blood flow as coupled oscillations operating over a wide range of different frequencies and time scales. Abnormally discordant interactions between regulation mechanisms can cause derangement of microvascular flow patterns, which has been suggested to be indicative of increased cardiovascular disease risk. Based on the premise that interactions between microvascular mechanisms are key to understanding flow regulation and disease progression, wavelet-based phase coherence was used to characterize the relationship between cardiac, respiratory, myogenic, neurogenic, and metabolic regulation across different microvascular beds and physiological conditions.
Steady state cutaneous microvascular blood flow data was collected using laser Doppler flowmetry in two locations: the forearm and forehead. At both locations, blood flux data was recorded under two hemodynamic steady states: at rest (30° C) and after local skin warming (45° C). The wavelet transform was used to calculate wavelet phase coherence between forehead and forearm blood flux, which was compared for low vs. high temperature hemodynamic states using frequency ranges associated with specific regulation mechanisms.
Metabolic, neurogenic, and endothelial NO-dependent regions demonstrated significant difference in time-averaged wavelet phase coherence, with greater coherence in the high temperature state. Time-localized wavelet phase coherence was relatively consistent across the 15-minute data sample. Study results favorably demonstrate the ability of wavelet-based phase coherence to differentiate between physiological conditions and observe disease progression; characterizing the relationship between different regulation mechanisms with coupling functions via Bayesian Inference are of interest in future work.
Acknowledgements
Thank you to my supervisors Dr. Daniel Goldman and Dr. Jefferson Frisbee for all of their support. Thank you to Brayden Halvorson and Dr. Stephanie Frisbee for providing the data.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Document Type
Poster
Included in
Microvascular Regulation between Two Hemodynamic Steady States in the Forearm and Forehead using Wavelet Phase Coherence
Microvascular mechanisms that regulate the microcirculation can be observed within blood flow as coupled oscillations operating over a wide range of different frequencies and time scales. Abnormally discordant interactions between regulation mechanisms can cause derangement of microvascular flow patterns, which has been suggested to be indicative of increased cardiovascular disease risk. Based on the premise that interactions between microvascular mechanisms are key to understanding flow regulation and disease progression, wavelet-based phase coherence was used to characterize the relationship between cardiac, respiratory, myogenic, neurogenic, and metabolic regulation across different microvascular beds and physiological conditions.
Steady state cutaneous microvascular blood flow data was collected using laser Doppler flowmetry in two locations: the forearm and forehead. At both locations, blood flux data was recorded under two hemodynamic steady states: at rest (30° C) and after local skin warming (45° C). The wavelet transform was used to calculate wavelet phase coherence between forehead and forearm blood flux, which was compared for low vs. high temperature hemodynamic states using frequency ranges associated with specific regulation mechanisms.
Metabolic, neurogenic, and endothelial NO-dependent regions demonstrated significant difference in time-averaged wavelet phase coherence, with greater coherence in the high temperature state. Time-localized wavelet phase coherence was relatively consistent across the 15-minute data sample. Study results favorably demonstrate the ability of wavelet-based phase coherence to differentiate between physiological conditions and observe disease progression; characterizing the relationship between different regulation mechanisms with coupling functions via Bayesian Inference are of interest in future work.