A computational fluid dynamics approach to determine white matter permeability.

Area of Science:

• Biomedical Engineering
• Computational Neuroscience
• Fluid Dynamics

Background:

• Glioblastomas are aggressive brain tumors with poor prognosis.
• Surgical, chemotherapeutic, and radiotherapeutic treatments are limited by tumor invasiveness.
• Convection-enhanced delivery (CED) shows promise but requires accurate pre-operative drug distribution prediction.

Purpose of the Study:

• To develop a computational method for determining hydraulic permeability in brain white matter.
• To provide a key parameter for modeling drug delivery in CED treatments.
• To enhance the predictability of therapeutic agent distribution in brain tumors.

Main Methods:

• A 2D brain-like structure model was created using electron microscopy data for axon diameter distribution.
• An outward packing method and disc shrinking technique were used to arrange axons within physiological extracellular space (ECS) parameters.
• Navier-Stokes equations were solved using ANSYS computational fluid dynamics (CFD) to simulate fluid flow.
• Homogenization techniques identified the representative volume element (RVE) size for permeability calculation.

Main Results:

• Hydraulic permeability was computed for white matter structures.
• The method successfully modeled fluid flow and pressure fields.
• The computed hydraulic permeability values aligned well with existing experimental data.

Conclusions:

• The proposed method provides a reliable way to compute hydraulic permeability in brain white matter.
• Accurate hydraulic permeability is essential for optimizing CED treatment planning.
• This approach can improve the efficacy of targeted drug delivery for glioblastoma and other brain pathologies.