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

Rasmussen, Peter M. and Smith, Amy F. and Sakadžić, Sava and Boas, David A. and Pries, Axel R. and Secomb, Timothy W. and Østergaard, Leif Model-based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach. (2017) Microcirculation, 24 (4). e12343. ISSN 1073-9688

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Official URL: https://doi.org/10.1111/micc.12343

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.

Item Type:Article
Additional Information:Thanks to Wiley editor. The definitive version is available at : http://onlinelibrary.wiley.com/doi/10.1111/micc.12343/abstract;jsessionid=D041B650DB3DB4011D644513A5799323.f04t01
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Other partners > Harvard Medical School - HMS (USA)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Other partners > Aarhus Universitetshospital (DENMARK)
Other partners > Charité - Universitätsmedizin Berlin (GERMANY)
Other partners > Harvard-MIT Health Sciences and Technology - HST (USA)
Other partners > University of Arizona (USA)
Laboratory name:
Funders:
Danish Ministry of Science, Innovation, and Education (DENMARK) - VELUX Foundation (DENMARK) - National Institutes of Health - NIH (USA) - European Commission
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Deposited On:17 Oct 2017 10:30

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