A network model of glymphatic flow under different experimentally-motivated parametric scenarios

Flow of cerebrospinal fluid (CSF) through perivascular spaces (PVSs) in the brain delivers nutrients, clears metabolic waste, and causes edema formation. Brain-wide imaging cannot resolve PVSs, and high-resolution methods cannot access deep tissue. However, theoretical models provide valuable insight. We model the CSF pathway as a network of hydraulic resistances, using published parameter values. A few parameters (permeability of PVSs and the parenchyma, and dimensions of PVSs and astrocyte endfoot gaps) have wide uncertainties, so we focus on the limits of their ranges by analyzing different parametric scenarios. We identify low-resistance PVSs and high-resistance parenchyma as the only scenario that satisfies three essential criteria: that the flow be driven by a small pressure drop, exhibit good CSF perfusion throughout the cortex, and exhibit a substantial increase in flow during sleep. Our results point to the most important parameters, such as astrocyte endfoot gap dimensions, to be measured in future experiments. [Display omitted] •We model the CSF pathway as a network of hydraulic resistances•Predictions are bracketed by analyzing parametric scenarios for unknown parameters•Low-resistance PVSs and high-resistance parenchyma produce realistic flows•Astrocyte endfoot gap size is among the important parameters to be measured Neuroscience; Systems neuroscience; In silico biology