Perivascular Spaces (PVS), also known as Virchow-Robin spaces, seen on structural brain MRI, are important fluid drainage conduits and are associated with small vessel disease (SVD). Computational quantification of visible PVS may enable efficient analyses in large datasets and increase sensitivity to detect associations with brain disorders. We assessed the associations of computationally-derived PVS parameters with vascular factors and white matter hyperintensities (WMH), a marker of SVD.

Computational quantification of brain perivascular space morphologies: Associations with vascular risk factors and white matter hyperintensities. A study in the Lothian Birth Cohort 1936

Ballerini, Lucia
;
2020-01-01

Abstract

Perivascular Spaces (PVS), also known as Virchow-Robin spaces, seen on structural brain MRI, are important fluid drainage conduits and are associated with small vessel disease (SVD). Computational quantification of visible PVS may enable efficient analyses in large datasets and increase sensitivity to detect associations with brain disorders. We assessed the associations of computationally-derived PVS parameters with vascular factors and white matter hyperintensities (WMH), a marker of SVD.
2020
Ageing, MRI, Perivascular spaces, White matter hyperintensities, Aged, Cardiovascular Diseases, Cerebral Small Vessel Diseases, Female, Glymphatic System, Humans, Magnetic Resonance Imaging, Male, Risk Factors, White Matter
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12071/32514
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