title: Model-based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Department of Clinical Medicine, Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Peter M
authors.initials: PM
authors.lastname: Rasmussen
authors.affiliation: Institut de Mécanique des Fluides de Toulouse (IMFT) - Université de Toulouse, CNRS-INPT-UPS, Toulouse, France.
authors.forename: Amy F
authors.identifier: 0000-0003-0964-1632
authors.initials: AF
authors.lastname: Smith
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
authors.forename: Sava
authors.initials: S
authors.lastname: Sakadžić
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.|Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Boas
authors.affiliation: Department of Physiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
authors.forename: Axel R
authors.initials: AR
authors.lastname: Pries
authors.affiliation: Department of Physiology, University of Arizona, Tucson, AZ, USA.
authors.forename: Timothy W
authors.initials: TW
authors.lastname: Secomb
authors.affiliation: Department of Clinical Medicine, Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark.|Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Leif
authors.initials: L
authors.lastname: Østergaard
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002540:Cerebral Cortex / Q000098:blood supply; D006439:Hemodynamics / Q000502:physiology*; D051379:Mice; D008833:Microcirculation / Q000502:physiology*; D020678:Microscopic Angioscopy / Q000379:methods*; D055806:Microvessels / Q000033:anatomy & histology / Q000502:physiology; D008954:Models, Biological; D015233:Models, Statistical; D051381:Rats; D012039:Regional Blood Flow / Q000502:physiology; D013152:Splanchnic Circulation / Q000502:physiology
keywords: Bayesian analysis; flow simulation; microcirculatory measurements; network-oriented analysis