Parameter sensitivity in solute transport models of glymphatic flows

Fluid flow through and around the brain is critical for clearing metabolic waste and maintaining homeostasis. In particular, fluid flow through perivascular spaces, annular channels surrounding cerebral blood vessels, is believed to be important for providing brain tissue with fluid and maintaining small advecttion-dominated regions in brain tissue that aid in clearance. Deep within the brain, these spaces fall below the resolution of current noninvasive imaging techniques, making computational models essential for studying transport dynamics. However, such models depend on parameters like channel size and permeability, which vary with brain state and are difficult to measure precisely. We represent thousands of perivascular spaces and adjacent tissue using a network model, where velocities are set according to the hydraulic resistance of channels and the time-dependent 1D advection-diffusion equation is solved semi-analytically via Laplace transform. Simulations are repeated 1000 times while systematically varying key parameters to find which parameters have the strongest impact on fluid and solute transport. Simulated solute distributions are compared with dye-injection experiments in anesthetized mice to identify parameter sets that reproduce observed flow. We find that the interface between perivascular spaces and brain tissue is a key determinant of realistic solute transport. At this boundary, fluid flows through gaps in the perivascular wall separating the brain tissue from the perivascular channel, and the size and distribution of gaps strongly affects transport. Smaller gaps between cells promote slower but more uniform fluid perfusion into brain tissue, enhancing solute delivery to deeper brain regions. We propose that accurate experimental measurements of these gap structures are needed to develop high-fidelity models.