Sheet and void porous media models for brain interstitial space

Area of Science:

• Neuroscience
• Biophysics
• Computational Biology

Background:

• The brain's interstitial space (ISS) functions as a porous medium.
• Previous models of ISS diffusion struggle to replicate observed molecular diffusion patterns, specifically tortuosity values.
• Existing models fail to account for complex ISS geometries and their impact on diffusion.

Purpose of the Study:

• To develop and analyze novel models of the brain's interstitial space (ISS) to better understand small molecule diffusion.
• To investigate the relationship between ISS geometry, porosity, and tortuosity.
• To explain experimentally observed diffusion characteristics and porosity changes during different brain states.

Main Methods:

• Monte Carlo simulations were employed to model diffusion within two distinct ISS geometric models: the corner cubic void (CCV) and the edge tunnel void (ETV).
• These models incorporate 'dead spaces' to increase geometrical tortuosity.
• The CCV model's performance was analyzed across a range of porosities, and its relationship with void-to-sheet volume ratio was examined.

Main Results:

• The CCV model demonstrates a linear relationship between the square of tortuosity and the void-to-sheet volume ratio within normal porosity ranges, successfully generating experimentally observed tortuosities.
• The ETV model exhibits a quartic functional relationship and requires interstitial viscosity to match observed tortuosity.
• The CCV model's ability to predict tortuosity makes it suitable for analyzing brain state-dependent porosity shifts.

Conclusions:

• The CCV model provides a more accurate representation of brain interstitial space (ISS) geometry and its effect on molecular diffusion compared to simpler models.
• This model successfully explains experimentally observed diffusion tortuosity and can be applied to understand dynamic changes in brain porosity, such as those occurring between sleep and wakefulness.
• The findings highlight the importance of complex geometric modeling in understanding brain physiology and function.