Validations / Blood Flow / NGV Portal

BloodFlow

Validations

BloodFlow

Validations

Blood flow validation are of two types, standalone validation of spatial structure of the flow on the vasculature w.r.t biological measurements, and validation of flow response under time dependent vasodilatation from astrocytes activities.

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We validated our findings by conducting a rigorous comparison of our simulation outputs with two commonly referenced entities prevalent in the existing literature and experimental data: blood flow and blood velocity.

▬▬ Blood flow and velocity values

We consider only the blood vessels in the internal part of the vasculature. In this way, we can minimize the effect due to the boundary conditions, and benefit from the contributions from all nodes in the vasculature

To fit with the method used in the literature, we do average values of simulated flow/velocity by averaging over all segments throughout the entire time series. These simulation values are represented by the blue boxplots.

Square markers within the scatter plots represent the values reported in previous studies Villringer et al. and Gutiérrez-Jiménez et al.. These markers were color-coded according to the animal species for which these parameters were computed. Gray rectangles depict the extent of flow and velocity values observed for rats where no average data (see Shih et al.) were available in the literature.

Figure: Validation of our simulation results against existing literature data. Blood flow (A), expressed in units of μm³.s⁻¹, and Blood velocity (B), expressed in units of μm.s⁻¹ were evaluated for both capillaries and large vessels.

▬▬ Blood flow and velocity distribution

Insets offer clearer views of the distribution characteristics in the blood flow distribution for capillaries ranging in diameter from 4 to 6 μm.

Figure: (A) Blood flow distribution for capillaries ranging in diameter from 4 to 6 μm, (B) Blood flow distribution for large vessels depicting a diameter ≥ 14 μm.

▬▬ Blood flow and velocity distribution

The zoomed-in portion offers a clearer view of the distribution's characteristics in the blood flow distribution for large vessels ranging in diameter superior to 14 μm.

Figure: (A) Velocity distribution for capillaries ranging in diameter from 4 to 6 μm, (B) Velocity distribution for large vessels depicting a diameter ≥ 14 μm. The zoomed-in portion offers a clearer view of the distribution's characteristics.

We present a refined validation of the simulation outputs, focusing on an in-depth analysis of flow values in capillaries categorized by their diameters.

▬▬ Capillary diameter ranging from 1 to 3 μm

Figure: Blood flow distribution for capillary diameter (A) 1-2 μm, (B) 2-3 μm.

▬▬ Capillary diameter ranging from 3 to 5 μm

Figure: Blood flow distribution for capillary diameter (A) 3-4 μm, (B) 4-5 μm.

▬▬ Capillary diameter ranging from 5 to 7 μm

Figure: Blood flow distribution for capillary diameter (A) 3-4 μm, (B) 4-5 μm.

The source code is available for public access on GitHub at https://github.com/BlueBrain/AstroVascPy. This Python-based package, AstroVascPy, has been designed for scalability across various vascular network datasets represented as point graphs.

It incorporates the influence of astrocytic endfeet on blood vessel radii. Notably, this tool can effectively replicate the dynamic changes in vessel radius resulting from vasodilation in diverse vascular networks.

You can run and modify the model using the following Google Collab Notebook.

First, you need a Google account to use Google Colab.
Second, you need to copy the notebook to your Google Drive (click on File -> Save a copy in Drive).
Finally, please follow the instructions in the Google Colab Notebook to run it. Since the vasculature data is not public, the model here runs on a synthetic vasculature.

You can also download the notebook from Google Colab to run it locally.
If you do so, you will need to git clone the Astrovascpy package here: https://github.com/BlueBrain/astrovascpy and locally one can run source setup.sh to install the AstroVascPy solver (+ all its dependencies) and set the environment.
For the local installation (workstation), please install conda before running the command above.
Remark: Run this command every time before using the solver in order to set the environment correctly.
In brief, to find out how to run the model locally, follow the steps described in the README.md file