Arterial spin‐labeling spatial coefficient of variation is associated with cerebral small vessel disease

Background Cerebral small vessel disease (SVD) is the most common pathology to co‐occur with Alzheimer’s disease but pathophysiologic mechanisms leading to SVD are poorly understood. The arterial spin‐labeling (ASL) spatial coefficient of variation (sCoV), a proxy marker of arterial transit time (ATT), may better reflect SVD‐related hemodynamic disturbances than ASL‐derived cerebral blood flow (CBF). We investigated whether grey matter CBF and grey matter sCoV each related to neuroimaging markers of SVD, including white matter hyperintensities (WMHs), basal ganglia enlarged perivascular spaces (ePVS), cerebral microbleeds (CMBs), and lacunes. Methods Vanderbilt Memory and Aging Project participants free of clinical dementia and stroke at study entry (n = 312, 73±7 years, 39% mild cognitive impairment, 41% female) underwent multimodal 3T brain magnetic resonance imaging to quantify ASL parameters and SVD burden. Pseudo‐continuous ASL assessed CBF in total grey matter. Grey matter sCoV was calculated as σ(CBFGrey Matter)/μ(CBFGrey Matter). WMHs and ePVS volumes were quantified using automated methods and ePVS volumes were standardized to basal ganglia volume. CMBs and lacune counts were quantified by an expert rater. Ordinary least squares regression models cross‐sectionally related grey matter CBF and sCoV individually to log‐transformed burden of each SVD marker. Models were adjusted for age, sex, race/ethnicity, education, cognitive status, Framingham Stroke Risk Profile (minus age), intracranial volume, grey matter volume, and apolipoprotein E‐ε4 status. Results Grey matter CBF was unrelated to all SVD outcomes (p‐values>0.25). Higher grey matter sCoV was associated with greater WMHs (β = 2.1, p = 0.001), ePVS (β = 0.004, p = 0.0003), and lacunar infarcts (β = 1.4, p = 0.0006) but not CMBs (β = 1.0, p = 0.06). Conclusions Our findings align with previous results showing that grey matter sCoV is a valuable marker of SVD. In contrast to previous work, we found an association between higher grey matter sCoV and higher basal ganglia ePVS burden. Potentially, our use of volumetric ePVS quantification rather than qualitative ePVS burden increased our ability to detect this association. As sCoV is a proxy marker of ATT, future work should consider quantifying sCoV or ATT in addition to CBF to better characterize SVD‐related hemodynamic disturbances. Funding: R01‐AG034962, K24‐AG046373, F31‐AG079640, P20‐AG068082