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MRI of Whole Rat Brain Perivascular Network Reveals Role for Ventricles in Brain Waste Clearance
Investigating the mechanisms by which metabolic wastes are cleared from nervous tissue is important for understanding natural function and the pathophysiology of several neurological disorders including Alzheimer’s disease. Recent evidence suggests clearance may be the function of annular spaces aro...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685961/ https://www.ncbi.nlm.nih.gov/pubmed/31391474 http://dx.doi.org/10.1038/s41598-019-44938-1 |
Sumario: | Investigating the mechanisms by which metabolic wastes are cleared from nervous tissue is important for understanding natural function and the pathophysiology of several neurological disorders including Alzheimer’s disease. Recent evidence suggests clearance may be the function of annular spaces around cerebral blood vessels, called perivascular spaces (PVS), through which cerebrospinal fluid (CSF) is transported from the subarachnoid space into brain parenchyma to exchange with interstitial fluid (also known as the glymphatic system). In this work, an MRI-based methodology was developed to reconstruct the PVS network in whole rat brain to better elucidate both PVS uptake and clearance pathways. MR visible tracer (Gd-albumin) was infused in vivo into the CSF-filled lateral ventricle followed by ex vivo high-resolution MR imaging at 17.6 T with an image voxel volume two orders of magnitude smaller than previously reported. Imaged tracer distribution patterns were reconstructed to obtain a more complete brain PVS network. Several PVS connections were repeatedly highlighted across different animals, and new PVS connections between ventricles and different parts of the brain parenchyma were revealed suggesting a possible role for the ventricles as a source or sink for solutes in the brain. In the future, this methodology may be applied to understand changes in the PVS network with disease. |
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