DOI: 10.5281/zenodo.5115639
title: Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.

description: <strong>DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.</strong> Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: 0000-0002-0689-9366) <strong>1. Related references:</strong><br> The data set is published in context with the manuscript: <br> [1]<em> The severity of microstrokes depends on local vascular topology and baseline perfusion</em>. <br> F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208 The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in:<br> [2]<em> The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow</em>. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426 The bi-phasic blood flow model for realistic MVNs has first been published in:<br> [3]<em> Depth-dependent flow and pressure characteristics in cortical microvascular networks</em>. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392 <em>For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3].</em> <strong>2. Requirements (software):</strong><br> <em>All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”).</em> <strong>3. Content:</strong><br> <em>All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "</em>tar –jxf ARCHIVE_NAME"<br> <br> <strong>3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files):</strong><br> <strong>SimulationResults_Baseline.tar.bz2:</strong><br> Folders: MVN1, MVN2<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (<em>generated from prepare_Figure4.py</em>), data_spatial_distribution_AVfactor.pkl (<em>generated from plot_Figure4.py</em>) <strong>SimulationResults_SingleCapillaryOcclusions.tar.bz2:</strong><br> Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (<em>only folders:</em> 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out) <strong>SimulationResults_MultiCapillaryOcclusions.tar.bz2:</strong><br> Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9<br> Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl <strong>3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts):</strong><br> <em>For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses.</em> <strong>helperFunctions.py</strong>: various functions used by the other analysis scripts <strong>plot_Figure1_and_Figure1-supplement_1_a-d.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/* <strong>plot_Figure2_and_Figure2_supplement_1_a-d.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl<br> <strong>Output:</strong> Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/* <strong>plot_Figure3.py:<br> Input:</strong> SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl <br> <strong>Output</strong>: Figures/Figure_3/* <strong>prepare_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl,<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl <strong>plot_Figure4.py </strong>(<em>long execution time!):</em><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/*<br> <strong>Output:</strong> SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/* <strong>plot_Figure5.py:</strong><br> <strong>Input:</strong> SimulationResults_Baseline/MVN*/* <br> <strong>Output:</strong> Figures/Figure_5/* <strong>plot_Figure6.py:</strong><br> Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/* <strong>4. Attributes stored in python dictionaries:</strong><br> <br> <strong>4a. Baseline:</strong><br> <strong>verticesDict: </strong><em>contains all relevant information and data stored at vertices.</em> index: index of vertex pressure: time averaged pressure at vertex [mmHg] inflowE: list of edges delivering blood to the vertex (inflows of the vertex) outflowE: list of edges removing blood from the vertex (outflows of the vertex) coords: coordinates of the vertex [µm] pBC: pressure boundary conditions [mmHg], None for internal vertices corticalDepth: depth from cortical surface [µm] nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary <strong>edgesDict:</strong> <em>contains all relevant information and data stored at edges.</em> diameter: effective vessel diameter [µm]. See [3] for details. htd: time averaged discharge hematocrit [-]. connectivity: tuple of vertex indices which are connected by the edge. mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>] length: tortuous vessel length [µm] See [1] and [3] for details. tissueVolume: topological tissue volume supplied by vessel [µm<sup>3</sup>]. See [1] for details. nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1]. htt: time averaged tube hematocrit [-] RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate. sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values. points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex. Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py <strong>pathsDict_allPaths_from_DA_to_AV_mainBranch: </strong><em>contains all flow path from DA main brain to AV main branch. For details see [1]</em>. startPoint: list of vertex indices of the end point of the DA endPoint: list of vertex indices of the end point of the AV allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint. <strong>data_spatial_distribution_AVfactor:</strong> <em>contains information on the spatial distribution of venule-sided capillaries (AV-factor &gt; 0.5). For details see [1].</em> edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed. resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl) shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries. shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor &lt; 0.5) for all discretization points along all venule sided capillaries. <strong>4b. Occlusion scenarios (both single- and multi-capillary occlusions):</strong> <strong>verticesDict:</strong> index: index of vertex coords: coordinates of the vertex [µm] pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1]. <strong>edgesDict:</strong> htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. flow: time averaged flow rate [µm<sup>3</sup> ms<sup>-1</sup>] for the simulation where edge n has been occluded. For details see [1]. RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1]. htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. connectivity: tuple of vertex indices which are connected by the edge. diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions). <strong>pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased:</strong> <em>contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --&gt;</em><em> function convert_pathsDict_to_unique_vertexSequence.</em> startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded. endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded. allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.
description: The data set is published in context with the manuscript: [1] The severity of microstrokes depends on local vascular topology and baseline perfusion. F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208 We refer to [1] regarding further information (e.g. methods, funding information, author contributions, ...).