Edinburgh Imaging

06 Jan 20. Featured Paper

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

Link to paper on Elsevier, NeuroImage: Clinical



Lucia Ballerini, Tom Booth, Maria del C. Valdés Hernández, Stewart Wiseman, Ruggiero Lovreglio, Susana Muñoz Maniega, Zoe Morris, Alison Pattie, Janie Corley, Alan Gow, Mark E. Bastin, Ian J. Deary, Joanna Wardlaw



Background & purpose: Perivascular Spaces (PVS), also known as Virchow-Robin spaces, seen on structural brain MRI, are important fluid drainage conduits & are associated with small vessel disease (SVD).

Computational quantification of visible PVS may enable efficient analyses in large datasets & increase sensitivity to detect associations with brain disorders.

We assessed the associations of computationally-derived PVS parameters with vascular factors & white matter hyperintensities (WMH), a marker of SVD.

Participants: Community dwelling individuals (n = 700) from the Lothian Birth Cohort 1936 who had multimodal brain MRI at age 72.6 years (SD = 0.7).

Methods: We assessed PVS computationally in the centrum semiovale & deep corona radiata on T2-weighted images.

The computationally calculated measures were the total PVS volume & count per subject, & the mean individual PVS length, width & size, per subject.

We assessed WMH by volume & visual Fazekas scores.

We compared PVS visual rating to PVS computational metrics, & tested associations between each PVS measure & vascular risk factors (hypertension, diabetes, cholesterol), vascular history (cardiovascular disease & stroke), & WMH burden, using generalized linear models, which we compared using coefficients, confidence intervals & model fit.

Results: 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 & width were associated with hypertension (OR 1.22, 95% CI [1.03 to 1.46] & 1.20, 95% CI [1.01 to 1.43], respectively), & stroke (OR 1.34, 95% CI [1.08 to 1.65] & 1.36, 95% CI [1.08 to 1.71], respectively).

We found no association between other PVS measures & diabetes, hypercholesterolemia or cardiovascular disease history.

Computational PVS volume, length, width & size were more strongly associated with WMH (PVS mean size versus WMH Fazekas score β = 0.66, 95% CI [0.59 to 0.74] & 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.

Conclusions: Computational measures reflecting individual PVS size, length & width were more strongly associated with WMH, stroke & 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 & neurological disease, provide greater anatomic detail & accelerate understanding of disorders of brain fluid & waste clearance.



  • Ageing

  • MRI

  • Perivascular spaces

  • White matter hyperintensities