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Leif Østergaard

Model-based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

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Model-based inference from microvascular measurements : Combining experimental measurements and model predictions using a Bayesian probabilistic approach. / Rasmussen, Peter M; Smith, Amy F; Sakadžić, Sava; Boas, David A; Pries, Axel R; Secomb, Timothy W; Østergaard, Leif.

In: Microcirculation, Vol. 24, No. 4, 05.2017.

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Rasmussen, Peter M ; Smith, Amy F ; Sakadžić, Sava ; Boas, David A ; Pries, Axel R ; Secomb, Timothy W ; Østergaard, Leif. / Model-based inference from microvascular measurements : Combining experimental measurements and model predictions using a Bayesian probabilistic approach. In: Microcirculation. 2017 ; Vol. 24, No. 4.

Bibtex

@article{cb94a8d61bcf44039c1c54e58047410a,
title = "Model-based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach",
abstract = "OBJECTIVE: In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors.METHODS: We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty.RESULTS: We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than 500 unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration.CONCLUSION: Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large data sets acquired as part of microvascular imaging studies.",
keywords = "Journal Article",
author = "Rasmussen, {Peter M} and Smith, {Amy F} and Sava Sakad{\v z}i{\'c} and Boas, {David A} and Pries, {Axel R} and Secomb, {Timothy W} and Leif {\O}stergaard",
note = "{\textcopyright} 2016 John Wiley & Sons Ltd.",
year = "2017",
month = may,
doi = "10.1111/micc.12343",
language = "English",
volume = "24",
journal = "Microcirculation",
issn = "1073-9688",
publisher = "JohnWiley & Sons Ltd.",
number = "4",

}

RIS

TY - JOUR

T1 - Model-based inference from microvascular measurements

T2 - Combining experimental measurements and model predictions using a Bayesian probabilistic approach

AU - Rasmussen, Peter M

AU - Smith, Amy F

AU - Sakadžić, Sava

AU - Boas, David A

AU - Pries, Axel R

AU - Secomb, Timothy W

AU - Østergaard, Leif

N1 - © 2016 John Wiley & Sons Ltd.

PY - 2017/5

Y1 - 2017/5

N2 - OBJECTIVE: In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors.METHODS: We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty.RESULTS: We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than 500 unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration.CONCLUSION: Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large data sets acquired as part of microvascular imaging studies.

AB - OBJECTIVE: In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors.METHODS: We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty.RESULTS: We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than 500 unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration.CONCLUSION: Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large data sets acquired as part of microvascular imaging studies.

KW - Journal Article

U2 - 10.1111/micc.12343

DO - 10.1111/micc.12343

M3 - Journal article

C2 - 27987383

VL - 24

JO - Microcirculation

JF - Microcirculation

SN - 1073-9688

IS - 4

ER -