Unified model of brain tissue microstructure dynamically binds diffusion and osmosis with extracellular space...

Area of Science:

• Neuroscience
• Biophysics
• Computational Biology

Background:

• Brain extracellular space (ECS) dynamics are crucial for physiological and pathological processes.
• Understanding how osmosis and diffusion influence ECS geometry is essential for modeling brain function.
• Existing models often lack the ability to dynamically link these factors.

Purpose of the Study:

• To present a universal model of brain tissue microstructure.
• To dynamically link osmosis and diffusion with brain extracellular space geometrical parameters.
• To accurately describe and predict the time-dependent changes in tortuosity.

Main Methods:

• Developed a multiscale technique for computationally effective osmolarity modeling in brain tissue.
• Utilized a level set method to capture the evolution of ECS dynamics.
• Employed a homogenization technique to derive a coarse-grained model linked to cellular and ECS geometry.

Main Results:

• The model robustly describes and predicts nonlinear time dependency of tortuosity changes with high precision.
• Achieved accurate analytical approximation of tortuosity based on time, space, osmolarity differences, and cell membrane water permeability.
• Validated model predictions against previously published experimental data in various media.

Conclusions:

• The developed model provides a unified framework for studying ECS dynamics.
• It offers a platform for investigating physiological conditions (e.g., aging, sleep-wake cycles) and pathological states (e.g., stroke, neoplasia).
• The model is valuable for predictive pharmacokinetic modeling, including drug biodistribution and efficacy assessments.