Dataset Information

Perivascular spaces around arteries exceed perivenous spaces in the mouse brain.

ABSTRACT: The perivascular space (PVS) surrounds cerebral blood vessels and plays an important role in clearing waste products from the brain. Their anatomy and function have been described for arteries, but PVS around veins remain poorly characterized. Using in vivo 2-photon imaging in mice, we determined the size of the PVS around arteries and veins, and their connection with the subarachnoid space. After infusion of 70 kD FITC-dextran into the cerebrospinal fluid via the cisterna magna, labeled PVS were evident around arteries, but veins showed less frequent labeling of the PVS. The size of the PVS correlated with blood vessel size for both pial arteries and veins, but not for penetrating vessels. The PVS around pial arteries and veins was separated from the subarachnoid space by a thin meningeal layer, which did not form a barrier for the tracer. In vivo, FITC-dextran signal was observed adjacent to the vessel wall, but minimally within the wall itself. Post-mortem, there was a significant shift in the tracer's location within the arterial wall, extending into the smooth muscle layer. Taken together, these findings suggest that the PVS around veins has a limited role in the exchange of solutes between CSF and brain parenchyma.

SUBMITTER: Smets NG

PROVIDER: S-EPMC11291892 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Perivascular spaces around arteries exceed perivenous spaces in the mouse brain.

Smets Nina G NG van der Panne Shakira A SA Strijkers Gustav J GJ Bakker Erik N T P ENTP

Scientific reports 20240726 1

The perivascular space (PVS) surrounds cerebral blood vessels and plays an important role in clearing waste products from the brain. Their anatomy and function have been described for arteries, but PVS around veins remain poorly characterized. Using in vivo 2-photon imaging in mice, we determined the size of the PVS around arteries and veins, and their connection with the subarachnoid space. After infusion of 70 kD FITC-dextran into the cerebrospinal fluid via the cisterna magna, labeled PVS wer ...[more]

PMID: 39085283

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Project description:Background: Flow of cerebrospinal fluid (CSF) through brain perivascular spaces (PVSs) is essential for the clearance of interstitial metabolic waste products whose accumulation and aggregation is a key mechanism of pathogenesis in many diseases. The PVS geometry has important implications for CSF flow as it affects CSF and solute transport rates. Thus, the size and shape of the perivascular spaces are essential parameters for models of CSF transport in the brain and require accurate quantification. Methods: We segmented two-photon images of pial (surface) PVSs and the adjacent arteries and characterized their sizes and shapes of thousands of cross sections from 14 PVS segments in 9 mice. Based on the analysis, we propose an idealized model that approximates the cross-sectional size and shape of pial PVSs, closely matching their area ratios and hydraulic resistances. Results: PVS size only approximately scales with vessel size, and the ratio of PVS-to-vessel area varies widely across the thousands of cross sections analyzed. The hydraulic resistance per unit length of the PVS scales with the PVS cross-sectional area, and we found a power-law fit that predicts resistance as a function of the area. Three idealized geometric models were compared to PVSs imaged in vivo, and their accuracy in reproducing hydraulic resistances and PVS-to-vessel area ratios were evaluated. The area ratio was obtained across thousands of different cross sections, and we found that the distribution peaks for the original PVS and its closest idealized fit (polynomial fit) were 1.12 and 1.21, respectively. The peak of the hydraulic resistance distribution is 1.73 x 10 15 Pa-s/m 5 and 1.44 x 10 15 Pa-s/m 5 for the segmentation and its closest idealized fit, respectively. Conclusions: Brief summary and potential implicationsPVS hydraulic resistance can be reasonably predicted as a function of the PVS area. The proposed polynomial-based fit most closely captures the shape of the PVS with respect to area ratio and hydraulic resistance. Idealized PVS shapes are convenient for modeling, which can be used to better understand how anatomical variations affect clearance and drug delivery transport.

Project description:BackgroundFlow of cerebrospinal fluid (CSF) through brain perivascular spaces (PVSs) is essential for the clearance of interstitial metabolic waste products whose accumulation and aggregation is a key mechanism of pathogenesis in many diseases. The PVS geometry has important implications for CSF flow as it affects CSF and solute transport rates. Thus, the size and shape of the perivascular spaces are essential parameters for models of CSF transport in the brain and require accurate quantification.MethodsWe segmented two-photon images of pial (surface) PVSs and the adjacent arteries and characterized their sizes and shapes of cross sections from 14 PVS segments in 9 mice. Based on the analysis, we propose an idealized model that approximates the cross-sectional size and shape of pial PVSs, closely matching their area ratios and hydraulic resistances.ResultsThe ratio of PVS-to-vessel area varies widely across the cross sections analyzed. The hydraulic resistance per unit length of the PVS scales with the PVS cross-sectional area, and we found a power-law fit that predicts resistance as a function of the area. Three idealized geometric models were compared to PVSs imaged in vivo, and their accuracy in reproducing hydraulic resistances and PVS-to-vessel area ratios were evaluated. The area ratio was obtained across different cross sections, and we found that the distribution peaks for the original PVS and its closest idealized fit (polynomial fit) were 1.12 and 1.21, respectively. The peak of the hydraulic resistance distribution is [Formula: see text] Pa s/m[Formula: see text] and [Formula: see text] Pa s/m[Formula: see text] for the segmentation and its closest idealized fit, respectively.ConclusionsPVS hydraulic resistance can be reasonably predicted as a function of the PVS area. The proposed polynomial-based fit most closely captures the shape of the PVS with respect to area ratio and hydraulic resistance. Idealized PVS shapes are convenient for modeling, which can be used to better understand how anatomical variations affect clearance and drug transport.

Dataset Information

Perivascular spaces around arteries exceed perivenous spaces in the mouse brain.

Publications

Perivascular spaces around arteries exceed perivenous spaces in the mouse brain.

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