Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature

<p>The dataset contains in 5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at capillary bifurcations in the somatosensory cortex of the mouse.</p> <p>Further notes on the simulation results:<br> - The realistic microvascular networks are from the mouse parietal cortex and have first been published in Blinder et al., 2013, Nature Neuroscience (<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>).<br> - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>).<br> - MVN1 and MVN2 stands for microvascular network 1 and 2, respectively.<br> - wRBCs and wpPs stands for 'with red blood cells' and 'with passive particles'. These terms describe two different numerical models. Further information will be added upon publication of the underlying manuscript.</p> <p><br> <strong>NOTE: </strong>Further information on the precise simulation setup and the in vivo RBC velocity measurements will be available as soon as the manuscript has been accepted for publication.<br>  </p> <p><strong>File format: </strong>pickle (Python)<br>  </p> <p><strong>Files 1 - 4 </strong>(Time-averaged simulation results):<br> Filenames: MVN1_wpPs.tar.bz2, MVN2_wpPs.tar.bz2, MVN1_wRBCs.tar.bz2, MVN2_wRBCs.tar.bz2</p> <p>Each compressed folder contains two files:<br> <br> edgesDict.pkl: dictionary with edge/vessel related data: </p> <ul> <li>flow: Flow rate in vessel [µm^3/ms]</li> <li>length: Vessel length [µm] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [µm]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> <p>verticesDict.pkl: dictionary with vertex/bifurcation related data:</p> <ul> <li>index: Index of the current vertex </li> <li>coords: Coordinates to describe the position of the vertex [µm]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul> <p> </p> <p><strong>File 5</strong> (in vivo RBC velocity measurements):<br> Filename: measurementDict.pkl</p> <p>keys:</p> <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> <p><br> Data structure: list of list,<br> e.g. daughter vessel 1:<br> [[bif.1 - measure.1, bif.1 - measure.2, bif.1 - measure.3], [bif.2 - measure.1, bif.2 - measure.2, bif.2 - measure.3],...]<br> bif.: bifurcation, measure.: measurement.<br> The order of bifurcations is the same for 'divergent_d1' and 'divergent_d2' (and for 'convergent_m1' and 'convergent_m2'). </p> <p> </p

Number of available unique paths from DA+A to V+AV for the five ALs averaged over the RBC trajectories from 3 MVNs.

<p>Number of available unique paths from DA+A to V+AV for the five ALs averaged over the RBC trajectories from 3 MVNs.</p

Pre-processing of the microvascular networks and the approach to assign boundary conditions.

<p>(A) Histogram-based upscaling approach for microvascular network 1. The grey and the black histogram show the original and the final diameter distribution, respectively. The mean, the standard deviation (std), the maximum value (max) and the minimum value (min) of all capillary diameters are stated in grey and black for the original and the final capillary diameter distribution, respectively. The red curve is the goal beta distribution. (B) Summary of the pressure measurements in the pial vasculature available in literature and the fit we used to assign the pressure boundary conditions at the pial arterioles. At the pial venules we uniformly prescribed a pressure of 10 mmHg. Data from: Harper [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005392#pcbi.1005392.ref007" target="_blank">7</a>], Werber [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005392#pcbi.1005392.ref008" target="_blank">8</a>], Hudetz [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005392#pcbi.1005392.ref044" target="_blank">44</a>], Shapiro [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005392#pcbi.1005392.ref009" target="_blank">9</a>]. (C) Schematic illustration of the steps of the hierarchical boundary condition approach. On the left the three different components of the full compound network are shown. The red and green spheres in the realistic implant represent the pial and capillary in- and outflows, respectively.</p

Pearson’s correlation coefficient for the relative end point frequencies and five trajectory characteristics averaged over 3 MVNs.

<p>Pearson’s correlation coefficient for the relative end point frequencies and five trajectory characteristics averaged over 3 MVNs.</p

Validation of the simulation results with literature data.

<p>Validation of the simulation results with literature data.</p