Mouse Cerebral Microvascular Blood Flow Simulations with RBC Tracking

Three simulated cerebral microvascular networks from mouse parietal cortex provide averaged flow, pressure, and discrete red blood cell trajectories. Data is stored as a graph structure with detailed vessel-level attributes, including diameter, hematocrit, and vessel type classification. The simulations use a numerical model with discrete RBC tracking, enabling analysis of hemodynamics at the capillary scale.

Use Cases

• Validating computational fluid dynamics models for blood flow based on vessel diameter, flow rate, and pressure fields.
• Analyzing red blood cell distribution and trajectories to study microvascular hematocrit heterogeneity.
• Classifying vessel types (e.g., arteriole, capillary, venule) using the nkind integer code for network topology studies.
• Reconstructing 3D vessel geometry and tortuosity using the edge 'points' coordinate lists.

Strengths

• Contains results for 3 distinct microvascular networks, enabling comparative analysis.
• Provides discrete tracking of individual red blood cell trajectories, a detailed level of simulation output.
• Includes a vessel type classification (nkind) with 6 categories, adding biological context to the graph structure.

Limitations

• Key metadata such as row count, dataset size, and specific last update date are not provided across sources.
• Data is stored in Python pickle files, which may present compatibility and security challenges for some users.
• The dataset is limited to 3 networks from a specific brain region (mouse parietal cortex), which may limit generalizability.

Provenance

Source

ETH Zurich (Author: Franca Schmid)

Collection Method

Numerical simulation results based on microvascular networks from Blinder et al., 2013, using a model described in Schmid et al., 2017.

Geography

Mouse parietal cortex, embedded in a tissue volume of approximately 1 cubic mm.