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. Community dwelling individuals (n=700) from the Lothian Birth Cohort 1936 who had multimodal brain MRI at age 72.6 years (SD=0.7). We assessed PVS computationally in the centrum semiovale and deep corona radiata on T2-weighted images. The computationally calculated measures were the total PVS volume and count per subject, and the mean individual PVS length, width and size, per subject. We assessed WMH by volume and visual Fazekas scores. We compared PVS visual rating to PVS computational metrics, and tested associations between each PVS measure and vascular risk factors (hypertension, diabetes, cholesterol), vascular history (cardiovascular disease and stroke), and WMH burden, using generalized linear models, which we compared using coefficients, confidence intervals and model fit. In 533 subjects, the computational PVS measures correlated positively with visual PVS ratings (PVS count r=0.59; PVS volume r=0.61; PVS mean length r=0.55; PVS mean width r=0.52; PVS mean size r=0.47). PVS size and width were associated with hypertension (OR 1.22, 95% CI [1.03 to 1.46] and 1.20, 95% CI [1.01 to 1.43], respectively), and stroke (OR 1.34, 95% CI [1.08 to 1.65] and 1.36, 95% CI [1.08 to 1.71], respectively). We found no association between other PVS measures and diabetes, hypercholesterolemia or cardiovascular disease history. Computational PVS volume, length, width and size were more strongly associated with WMH (PVS mean size versus WMH Fazekas score β=0.66, 95% CI [0.59 to 0.74] and versus WMH volume β=0.43, 95% CI [0.38 to 0.48]) than computational PVS count (WMH Fazekas score β=0.21, 95% CI [0.11 to 0.3]; WMH volume β=0.14, 95% CI [0.09 to 0.19]) or visual score. Individual PVS size showed the strongest association with WMH. Computational measures reflecting individual PVS size, length and width were more strongly associated with WMH, stroke and hypertension than computational count or visual PVS score. Multidimensional computational PVS metrics may increase sensitivity to detect associations of PVS with risk exposures, brain lesions and neurological disease, provide greater anatomic detail and accelerate understanding of disorders of brain fluid and waste clearance.
Highlights
Perivascular spaces (PVS), sometimes known as Virchow–Robin spaces, are fluid-filled compartments surrounding the small perforating brain microvessels
Perivascular Spaces (PVS) have been reported to increase in number on Magnetic Resonance Imaging (MRI), based on visual scores, with age, with other brain features of small vessel disease (SVD) (Wardlaw et al, 2013) with vascular risk factors, especially hypertension, in common brain disorders including stroke, mild cognitive impairment, and dementia including of vascular subtype (Debette et al, 2019; Francis et al, 2019) many individual studies have reported associations between increased numbers of PVS and white matter hyperintensities (WMH), a recent meta-analysis (Francis et al, 2019) found no clear PVS-WMH association in adjusted analysis, possibly reflecting variation in populations, SVD lesion burden or PVS assessment methods (Debette et al, 2019; Francis et al, 2019)
We evaluate associations between each of five new PVS measures and important vascular risk factors, vascular disease history, and WMH burden on brain MRI
Summary
Perivascular spaces (PVS), sometimes known as Virchow–Robin spaces, are fluid-filled compartments surrounding the small perforating brain microvessels. The computational method developed by Ballerini and colleagues (Ballerini et al, 2018) was able to assess PVS in the centrum semiovale in two small independent older age cohorts (age 64–72 years): individuals with a clinical diagnosis of dementia (n = 20), and patients who previously had minor stroke (n = 48), in whom there was good agreement between PVS visual rating and computational measures (Ballerini et al, 2016, 2018) We evaluate this PVS computational method in a large community-dwelling older age cohort scanned at age 73 years. We evaluate associations between each of five new PVS measures and important vascular risk factors (hypertension, diabetes, plasma cholesterol), vascular disease history, and WMH burden on brain MRI
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