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Perivascular spaces include a variety of passageways around arterioles, capillaries and venules in the brain, along which a range of substances can move. Although perivascular spaces were first identified over 150 years ago, they have come to prominence recently owing to advances in knowledge of their roles in clearance of interstitial fluid and waste from the brain, particularly during sleep, and in the pathogenesis of small vessel disease, Alzheimer disease and other neurodegenerative and inflammatory disorders. Experimental advances have facilitated in vivo studies of perivascular space function in intact rodent models during wakefulness and sleep, and MRI in humans has enabled perivascular space morphology to be related to cognitive function, vascular risk factors, vascular and neurodegenerative brain lesions, sleep patterns and cerebral haemodynamics. Many questions about perivascular spaces remain, but what is now clear is that normal perivascular space function is important for maintaining brain health. Here, we review perivascular space anatomy, physiology and pathology, particularly as seen with MRI in humans, and consider translation from models to humans to highlight knowns, unknowns, controversies and clinical relevance. [ABSTRACT FROM AUTHOR]

Perivascular mural cells including vascular smooth cells (VSMCs) and pericytes are integral components of the vascular system. In the central nervous system (CNS), pericytes are also indispensable for the blood-brain barrier (BBB), blood-spinal cord barrier and blood-retinal barrier, and play key roles in maintaining cerebrovascular and neuronal functions. However, the functional specifications of pericytes between CNS and peripheral organs have not been resolved at the genetic and molecular levels. Hence, the generation of reliable CNS pericyte-specific models and genetic tools remains very challenging. Here, we report a new CNS pericyte marker in mice. This putative cation-transporting ATPase 13A5 ( Atp13a5 ) marker was identified through single cell transcriptomics, based on its specificity to brain pericytes. We further generated a knock-in model with both tdTomato reporter and Cre recombinase. Using this model to trace the distribution of Atp13a5- positive pericytes in mice, we found that the tdTomato reporter reliably labels the CNS pericytes, including the ones in spinal cord and retina but not peripheral organs. Interestingly, brain pericytes are likely shaped by the developing neural environment, as Atp13a5- positive pericytes start to appear around murine embryonic day 15 (E15) and expand along the cerebrovasculature. Thus, Atp13a5 is a specific marker of CNS pericyte lineage, and this Atp13a5- based model is a reliable tool to explore the heterogeneity of pericytes and BBB functions in health and diseases. Significance Statement Pericyte is a key component of the blood-brain barrier (BBB) and highly implicated in neurological and neurodegenerative diseases. However, current genetic tools for brain pericytes often come with limitations, due to the lack of specificity to the pericytes in the brain or central nervous system (CNS), as well as the overlap with other cell types, particularly vascular smooth muscle cells. Here, we identified that Atp13a5 is a CNS-specific pericyte marker based on mouse single-cell transcriptomics, and further validate it using a knock-in model carrying Atp13a5 -driven tdTomato reporter and Cre recombinase. The success of the Atp13a5 -based model opens new possibility of genetic manipulations targeting only CNS pericytes in vivo and studying their biology and functions in health and diseases more specifically., (Copyright © 2024 Guo et al.)

Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. High angular resolution diffusion imaging (HARDI) samples diffusivity at a large number of spherical angles, to better resolve neural fibers that mix or cross. Here, we implemented a framework for advanced mathematical analysis of mouse 5-shell HARDI ( b =1000, 3000, 4000, 8000, 12000 s/mm 2 ), also known as hybrid diffusion imaging (HYDI). Using q -ball imaging (QBI) at ultra-high field strength (7 Tesla), we computed diffusion and fiber orientation distribution functions (dODF, fODF) to better detect crossing fibers. We also computed a quantitative anisotropy (QA) index, and deterministic tractography, from the peak orientation of the fODFs. We found that the signal to noise ratio (SNR) of the QA was significantly higher in single and multi-shell reconstructed data at the lower b -values ( b =1000, 3000, 4000 s/mm 2 ) than at higher b -values ( b =8000, 12000 s/mm 2 ); the b =1000 s/mm 2 shell increased the SNR of the QA in all multi-shell reconstructions, but when used alone or in <5-shell reconstruction, it led to higher angular error for the major fibers, compared to 5-shell HYDI. Multi-shell data reconstructed major fibers with less error than single-shell data, and was most successful at reducing the angular error when the lowest shell was excluded ( b =1000 s/mm 2 ). Overall, high-resolution connectivity mapping with 7T HYDI offers great potential for understanding unresolved changes in mouse models of brain disease.