Impact of Changes in Tissue Optical Properties on Near-infrared Diffuse Correlation Spectroscopy Measures of Skeletal Muscle Blood Flow
Miles F Bartlett,
Dennis M. Hueber,
Michael D Nelson
Journal of Applied Physiology, 2021
Abstract | paper
Near-infrared diffuse correlation spectroscopy (DCS) is increasingly used to study relative changes in skeletal muscle blood flow. However, most diffuse correlation spectrometers assume that tissue optical properties-such as absorption (μa) and reduced scattering (μ's) coefficients-remain constant during physiological provocations, which is untrue for skeletal muscle. Here, we interrogate how changes in tissue μa and μ's affect DCS calculations of blood flow index (BFI). We recalculated BFI using raw autocorrelation curves and μa/μ's values recorded during a reactive hyperemia protocol in 16 healthy young individuals. First, we show that incorrectly assuming baseline μa and μ's substantially affects peak BFI and BFI slope when expressed in absolute terms (cm2/s, P < 0.01), but these differences are abolished when expressed in relative terms (% baseline). Next, to evaluate the impact of physiologic changes in μa and μ's, we compared peak BFI and BFI slope when μa and μ's were held constant throughout the reactive hyperemia protocol versus integrated from a 3-s rolling average. Regardless of approach, group means for peak BFI and BFI slope did not differ. Group means for peak BFI and BFI slope were also similar following ad absurdum analyses, where we simulated supraphysiologic changes in μa/μ's. In both cases, however, we identified individual cases where peak BFI and BFI slope were indeed affected, with this result being driven by relative changes in μa over μ's. Overall, these results provide support for past reports in which μa/μ's were held constant but also advocate for real-time incorporation of μa and μ's moving forward.NEW & NOTEWORTHY We investigated how changes in tissue optical properties affect near-infrared diffuse correlation spectroscopy (NIR-DCS)-derived indices of skeletal muscle blood flow (BFI) during physiological provocation. Although accounting for changes in tissue optical properties has little impact on BFI on a group level, individual BFI calculations are indeed impacted by changes in tissue optical properties. NIR-DCS calculations of BFI should therefore account for real-time, physiologically induced changes in tissue optical properties whenever possible.
High Confidence Generalization for Reinforcement Learning
Thirty-eighth International Conference on Machine Learning (ICML), 2021.
Abstract | pdf | Video
We present several classes of reinforcement learn-ing algorithms that safely generalize toMarkovdecision processes(MDPs) not seen during train-ing. Specifically, we study the setting in whichsome set of MDPs is accessible for training. Forvarious definitions of safety, our algorithms giveprobabilistic guarantees that agents can safely gen-eralize to MDPs that are sampled from the samedistribution but are not necessarily in the train-ing set. These algorithms are a type ofSeldo-nianalgorithm (Thomas et al., 2019), which is aclass of machine learning algorithms that returnmodels with probabilistic safety guarantees foruser-specified definitions of safety.