Data set: Blood flow simulation and uncertainty quantification in extensive microvascular networks: Application to brain cortical networks

1/8 Data files with simulation results underlying figures and summary statistics presented in tables and figures in PM Rasmussen. Blood flow simulation and uncertainty quantification in extensive microvascular networks: Application to brain cortical networks. Microcirculation, 2025. Corresponding author Peter Mondrup Rasmussen, Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark. pmr@cfin.au.dk. ORCID: 0000-0001-6849-0788 Data set DOI https:// doi.org/10.5281/zenodo.15373376 Data set license Data files: CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/ R analysis scripts: GNU GPL v3 license, https://www.gnu.org/licenses/gpl-3.0.txt 1. References The data set is published together with the article: [1] PM Rasmussen. Blood flow simulation and uncertainty quantification in extensive microvascular networks: Application to brain cortical networks. Microcirculation 2025. The data set contains derivatives of the following Zenodo sources: [2] Franca Schmid. (2017). Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks [Data set]. Zenodo. https://doi.org/10.5281/zenodo.758632 [3] Schmid, F., Conti, G., Jenny, P., & Weber, B. (2021). Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various singleand multi-capillary occlusion scenarios. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5115639 The data set contains values that has been extracted from: [4] Figure 1 in Taylor ZJ, Hui ES, Watson AN, Nie X, Deardorff RL, Jensen JH, et al. Microvascular basis for growth of small infarcts following occlusion of single penetrating arterioles in mouse cortex. J Cereb Blood Flow Metab. 2016. 2. Content Uncompress zenodoDeposit.tar.gz. The directory R contains R code for creating summary figures and tables from the data files. Run the script main.R. Install required R packages if needed – package list provided at the top of the script. The directory data contains .txt files with data underlying summary figures and tables. Please refer to the descriptions of individual files in the following sections. The directory also contains a subdirectory png, with various network plots used for creating figure panels in R. Source data for these network plots are available in the .txt files. Rev.: 2025.09.04. 2/8 3. Segment data File naming: segmentExports_{NW1/NW2}.{ref/cal}.{Data/Vitro/Vivo/Esl}{“”/.ABC}.{“”,mu*}.txt Data content: Metrics in columns, segments in rows, column headers in first row. Column Metric Physical unit / notes nw Network name, see [1] NA model Model name, see [1] Table 2 NA segIdx Segment index Integer: {1, … , numberOfSegments} segType Segment type Integer: {0,1,2,3,4,5} corresponding to {SA , SV, DA+A, V+AV, C, UNK }, see [1]. segDia Segment diameter µm segLen Segment Length µm layer Analysis layer AL1-AL6, see [1]. segGen Segment generation, see [1] Figure 1 Segment generation relative to boundary node, see [1]. segPressure Mid-segment pressure mmHg segFlowB Segment blood flow rate (µm)^3/s segHct Segment discharge hematocrit Fraction (between 0 and 1) segShearStress Segment shear stress dyn/(cm)^2 segVelB Segment blood velocity mm/s segVelC Segment RBC velocity mm/s DAR Direction agreement rate % Notes: Segment metrics for the calibration models, *.cal.*.ABC.txt, are averages across 1000 MCMC iterations, see [1]. SegVelC for the reference models, *.ref.*.*.txt is NaN, see [1]. The *.ref.Vitro.ABC.mu*.txt files contain segment metrics for various pressure deviance offsets, see Figure 12 in File S3 in [1]. 3/8 4. Path data File naming: pathsExports_{NW1/NW2}.{ref/cal}.{Data/Vitro/Vivo/Esl}{“”/.ABC}.txt Data content: Metrics in columns, paths in rows, column headers in first row. Column Metric Physical unit / notes nw Network name, see [1] NA model Model name, see [1] Table 2 NA idxPath Path index Integer: {1, … , numberOfPaths} layer Analysis layer assigned to path, see [1] AL1-AL6, see [1] flowB Blood flow rate along path (µm)^3/s flowC RBC flow rate along path (µm)^3/s dpSA Pressure drop across vessel category SA mmHg dpA Pressure drop across vessel category DA+A mmHg dpC Pressure drop across vessel category C mmHg dpV Pressure drop across vessel category AV+V mmHg dpSV Pressure drop across vessel category SV mmHg zCapIn Capillary input depth µm zCapOut Capillary output depth µm darSA Direction agreement rate averaged across path SA segments % darA Direction agreement rate averaged across path DA+A segments % darC Direction agreement rate averaged across path C segments % darV Direction agreement rate averaged across path AV+V segments % darSV Direction agreement rate averaged across path SV segments % Notes: Paths of calibration models, *.cal.*. ABC.txt, were first accumulated across 1000 MCMC samples, see [1]. Path flow rates and pressure drops represent path averages across 1000 MCMC samples for the respective sets of accumulated paths. FlowC for the reference models, *.ref.*.*.txt, is 0, see [1]. 4/8 5. Data used for velocity fits File naming: {emp/ref}VelCData.txt Data content: Metrics in columns, data points in rows, column headers in first row. Column Metric Physical unit / notes segDia Segment diameter µm segVelC Segment RBC velocity mm/s segType Segment type, see [1] Art or Ven Notes: Data in the empVelCData.txt was extracted from Figure 1 in Taylor ZJ, Hui ES, Watson AN, Nie X, Deardorff RL, Jensen JH, et al. Microvascular basis for growth of small infarcts following occlusion of single penetrating arterioles in mouse cortex. J Cereb Blood Flow Metab. 2016. Data in refVelCData.txt was derived from the reference data set, refData, from vessels categories Art (resp. Ven) corresponding to vessel categories [0,2] (resp. [1,3]) with diameter <30 µm and in AL1, see [1]. 5/8 6. Calibration fits and mean squared errors File naming: modelFit_{NW1/NW2}.cal.{Vitro/Vivo/Esl}.ABC.txt Data content: Metrics in columns, data points in rows, column headers in first row. Column Metric Physical unit / notes nw Network name, see [1] NA model Model name, see [1] Table 2 NA segIdx Segment index Integer segType Segment type Integer: {0,1,2,3} corresponding to {SA, SV, DA+A, V+AV }, see [1]. metric Predicted metric String {velC} yTarget Target RBC velocity mm/s yHat Predicted RBC velocity mm/s Notes: Predicted RBC velocities are averages across 1000 MCMC samples. File naming: modelFitMse_{NW1/NW2}.cal.{Vitro/Vivo/Esl}.ABC.txt Data content: Metrics in columns, data points in rows, column headers in first row. Column Metric Physical unit / notes nw Network name, see [1] NA model Model name, see [1] Table 2 NA mseVelC Mean squared RBC velocity error (mm/s)^2 Notes: Rows contain mean squared RBC velocity error (averaged across vessel segments) for individual MCMC samples, 1000 rows in total. 6/8 7. Iteration stats, hematocrit iterations File naming: iterStat.{NW1/NW2}.cal.{Vitro/Vivo/Esl}.ABC.{“”,w10}.txt Data content: Metrics in columns, data points in rows, column headers in first row. Column Metric Physical unit / notes nIter Iterations at convergence Integer nBifurSubThr Number of bifurcations below threshold φ3 (0.9) at convergence. See Section 3.3 in File S1 in [1]. nBifurSupThr Number of bifurcations above threshold φ3 (0.9) at convergence. See Section 3.3 in File S1 in [1]. Notes: None. 7/8 8. Segment blood flow rates and hematocrits in individual hematocrit iterations File naming: NW1.cal.ESL.ABC{“ ”, .w 1 0}_{flow,hct}Iterations_idxSample42.txt Data content: Metric identifier {segFlowB, segHct} in the first column and values in remaining columns (number of segments +1 columns in total). Individual iterations in rows. Blood flow rates (segFlowB) are in (µm)^3/s and discharge hematocrits (segHct) are between 0 and 1. Notes: None. 8/8 9. Segment information and node information File naming: segmentInfo_{NW1/NW2/NW1.noTrim/NW2.noTrim}.txt Data content: Metrics in columns, values in rows, column headers in first row. Column Metric Physical unit / notes nw Network name, see [1] NA segIdx Segment index Integer: {1, … , numberOfSegments} segType Segment type String: {SA, SV, DA+A, V+AV, C, UNK}, see [1] segStartNode Segment start node Integer segEndNode Segment end node Integer segDia Segment diameter µm segLen Segment length µm layer Analysis layer AL1-AL6, see [1]. segGen Segment generation, see [1] Figure 1 Segment generation relative to boundary node, see [1]. segIdxOrig Segment index in source [2] Integer. Segment (edge) index in source [2]. Notes: noTrim are non-trimmed network variants, see Figure 1 in File S3 in [1]. File naming: nodeInfo_{NW1/NW2/ NW1.noTrim/NW2.noTrim}.txt Data content: Metrics in columns, values in rows, column headers in first row. Column Metric Physical unit / notes nw Network name, see [1] NA nodeIdx Node index Integer: {1, … , numberOfNodes} layer Analysis layer of node AL1-AL6, see [1]. segType Segment type, only provided for boundary nodes. String: {SA, SV, DA+A, V+AV, C, UNK} for boundary nodes. Empty string for interior nodes. bndNode If node is a boundary node. Integer: 1 if boundary node. 0 otherwise. bndNodeABC If boundary node was governed by the adaptive method for pressure boundary conditions, see [1]. Integer: 1 if boundary node and the adaptive method was used for this node. 0 otherwise. refNodeABC If interior node was used as a reference node in the adaptive method for pressure boundary conditions, see [1]. Integer: 1 if interior node and the node was used as a reference node in the adaptive method. groupIdxABC Group membership of boundary nodes and reference nodes in the adaptive method for pressure boundary conditions, see [1]. Integer: Positive integer corresponding to group index. 0 otherwise. posX Node X coordinate µm posY Node Y coordinate µm posZ Node Z coordinate µm Notes: noTrim are non-trimmed network variants, see Figure 1 in File S3 in [1].