Normative Population-Derived Data for MRI Manifestations of Cerebral Small Vessel Disease

Abstract

BACKGROUND: Cerebral small vessel disease (SVD) is manifested on magnetic resonance imaging (MRI) by white matter hyperintensities, lacunes, microbleeds, and atrophy. While these manifestations can be part of normal aging, a high burden has been associated with cognitive impairment and vascular events. Distinguishing between normal versus abnormal SVD lesion burden in clinical practice remains complex. Our objective is to establish age- and sex-specific normative data for MRI manifestations of SVD, to support clinical assessment in individual patients. METHODS: We used 11 465 MRI scans from 5402 participants of the Rotterdam Study, the Netherlands, an ongoing prospective population-based cohort since 1990, to develop percentile curves for white matter hyperintensities and brain parenchymal fraction and probability curves for the prevalence and count of lacunes and microbleeds, across ages 45 to 100 years, stratified by sex. RESULTS: Participants were primarily White (≈97%), with a mean age at first scan of 64.7 (range, 45.7-97.9) years, and 55.7% being female participants. For all SVD MRI manifestations, the curves demonstrated nonlinear trends, with accelerating burden with advancing age (eg, doubling of white matter hyperintensity fraction every 10 years). Regarding brain parenchymal fraction, a decline was seen earlier in male participants (≈45 years) than female participants (≈60 years) and was more pronounced in male participants over time. Female participants had slightly higher white matter hyperintensity fractions compared with male participants across all ages. Lacunes and microbleeds were more frequently found in male participants than in female participants, and microbleeds were more prevalent than lacunes. CONCLUSIONS: We provide comprehensive normative data for different MRI manifestations of SVD, presented as percentile and probability curves by age, stratified by sex. This can aid clinicians to actually quantify the SVD burden on an individual patient's MRI scans and detect patterns of abnormality.