Year: 2026
Type: preprint
Abstract: Background: Brain tissue is a highly complex, heterogeneous, porous medium containing neurons, glial cells, blood vessels, and fluids. The interstitial spaces surrounding cells, referred to as the extracellular space (ECS), are critical transport pathways whose disruption is believed to play a role in neurological diseases. Therefore, understanding ECS fluid dynamics and the factors modulating flow there, such as permeability, is essential. Furthermore, because brain morphology, scale, and cellular density vary across species, understanding how permeability varies between species is critical for accurate physiological modeling. Methods: In our study, we investigated ECS permeability by performing computational fluid dynamics (CFD) simulations on three-dimensional electron microscopy data from rat, mouse, and human. We artificially varied the ECS volume fraction to investigate associated permeability variations, since volume fraction changes with arousal state. We also used a mesoscale computational model to simulate the waste clearance from the brain tissue.Results: The mean permeabilities of the ECS in rat, mouse, and human brain tissues, averaged across sampled locations, were approximately 18, 34, and 10 nm2, respectively. We compared our computational results with those predicted by the Kozeny-Carman equation, which consistently underestimated permeability. We developed an empirical relation to describe the variation of permeability with volume fraction within the range relevant to brain tissue.Conclusion: Our results show that the ECS permeability is of the same order of magnitude, approximately 10 nm2 across all species but varied several-fold across datasets. Hence, for models incorporating ECS permeability, a reasonable range of ECS permeability values for rats, mice, and humans is 10 nm2 - 100 nm2.
Source: Research Square
Institution: University of Rochester
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7223836849