Abstract The brain lacks a conventional lymphatic system to remove metabolic waste. It has been proposed that directional fluid movement through the arteriolar paravascular space (PVS) promotes metabolite clearance. We performed simulations to examine if arteriolar pulsations and dilations can drive directional CSF flow in the PVS and found that arteriolar wall movements do not drive directional CSF flow. We propose an alternative method of metabolite clearance from the PVS, namely fluid exchange between the PVS and the subarachnoid space (SAS). In simulations with compliant brain tissue, arteriolar pulsations did not drive appreciable fluid exchange between the PVS and the SAS. However, when the arteriole dilated, as seen during functional hyperemia, there was a marked exchange of fluid. Simulations suggest that functional hyperemia may serve to increase metabolite clearance from the PVS. We measured blood vessels and brain tissue displacement simultaneously in awake, head-fixed mice using two-photon microscopy. These measurements showed that brain deforms in response to pressure changes in PVS, consistent with our simulations. Our results show that the deformability of the brain tissue needs to be accounted for when studying fluid flow and metabolite transport.
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Mapping glymphatic solute transportation through the perivascular space of hippocampal arterioles with 14 Tesla MRI
Abstract The perivascular space (PVS) plays a crucial role in facilitating the clearance of waste products and the exchange of cerebrospinal fluid and interstitial fluid in the central nervous system. While optical imaging methods identify the glymphatic transport of fluorescent tracers through PVS of surface-diving arteries, their limited depth penetration impedes the study of glymphatic dynamics in deep brain regions. In this study, we introduced a novel high-resolution dynamic contrast-enhanced MRI mapping approach based on single-vessel multi-gradient-echo methods. This technique allowed the differentiation of penetrating arterioles and venules from adjacent parenchymal tissue voxels and enabled the detection of Gd-enhanced signals coupled to PVS of penetrating arterioles in the deep cortex and hippocampus. By directly infusing Gd into the lateral ventricle, we eliminated delays in cerebrospinal fluid flow and focused on PVS Gd transport through PVS of hippocampal arterioles. The study revealed significant PVS-specific Gd signal enhancements, shedding light on glymphatic function in deep brain regions. These findings advance our understanding of brain-wide glymphatic dynamics and hold potential implications for neurological conditions characterized by impaired waste clearance, warranting further exploration of their clinical relevance and therapeutic applications.
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- Award ID(s):
- 2123970
- PAR ID:
- 10527442
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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Abstract BackgroundPerivascular spaces (PVSs) carry cerebrospinal fluid (CSF) around the brain, facilitating healthy waste clearance. Measuring those flows in vivo is difficult, and often impossible, because PVSs are small, so accurate modeling is essential for understanding brain clearance. The most important parameter for modeling flow in a PVS is its hydraulic resistance, defined as the ratio of pressure drop to volume flow rate, which depends on its size and shape. In particular, the local resistance per unit length varies along a PVS and depends on variations in the local cross section. MethodsUsing segmented, three-dimensional images of pial PVSs in mice, we performed fluid dynamical simulations to calculate the resistance per unit length. We applied extended lubrication theory to elucidate the difference between the calculated resistance and the expected resistance assuming a uniform flow. We tested four different approximation methods, and a novel correction factor to determine how to accurately estimate resistance per unit length with low computational cost. To assess the impact of assuming unidirectional flow, we also considered a circular duct whose cross-sectional area varied sinusoidally along its length. ResultsWe found that modeling a PVS as a series of short ducts with uniform flow, and numerically solving for the flow in each, yields good resistance estimates at low cost. If the second derivative of area with respect to axial location is less than 2, error is typically less than 15%, and can be reduced further with our correction factor. To make estimates with even lower cost, we found that instead of solving for the resistance numerically, the well-known resistance of a circular duct could be scaled by a shape factor. As long as the aspect ratio of the cross section was less than 0.7, the additional error was less than 10%. ConclusionsNeglecting off-axis velocity components underestimates the average resistance, but the error can be reduced with a simple correction factor. These results could increase the accuracy of future models of brain-wide and local CSF flow, enabling better prediction of clearance, for example, as it varies with age, brain state, and pathological conditions.more » « less
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