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Vascular cognitive impairment is common after stroke, in memory clinics, medicine for the elderly services, and undiagnosed in the community. Vascular disease is said to be the second most common cause of dementia after Alzheimer disease, yet vascular dysfunction is now known to predate cognitive decline in Alzheimer disease, and most dementias at older ages are mixed. Neuroimaging has a major role in identifying the proportion of vascular versus other likely pathologies in patients with cognitive impairment. Here, we aim to provide a pragmatic but evidence-based summary of the current state of potential imaging biomarkers, focusing on magnetic resonance imaging and computed tomography, which are relevant to diagnosing, estimating prognosis, monitoring vascular cognitive impairment, and incorporating our own experiences. We focus on markers that are well-established, with a known profile of association with cognitive measures, but also consider more recently described, including quantitative tissue markers of vascular injury. We highlight the gaps in accessibility and translation to more routine clinical practice., Competing Interests: Disclosures Drs Clancy, Maniega, Quinn, Jochems, and Valdés Hernández, A.K. Kancheva, and J.M. Wardlaw report academic research grants as detailed in the Sources of Funding. J.M. Wardlaw chairs the European Stroke Organisation Guideline on small vessel disease. Dr Quinn reports grants from Bristol Myers Squibb and Shionogi and advises on ESO Guidelines.

Background: Cerebrovascular reactivity (CVR) is inversely related to white matter hyperintensity severity, a marker of cerebral small vessel disease (SVD). Less is known about the relationship between CVR and other SVD imaging features or cognition. We aimed to investigate these cross-sectional relationships., Methods: Between 2018 and 2021 in Edinburgh, we recruited patients presenting with lacunar or cortical ischemic stroke, whom we characterized for SVD features. We measured CVR in subcortical gray matter, normal-appearing white matter, and white matter hyperintensity using 3T magnetic resonance imaging. We assessed cognition using Montreal Cognitive Assessment. Statistical analyses included linear regression models with CVR as outcome, adjusted for age, sex, and vascular risk factors. We reported regression coefficients with 95% CIs., Results: Of 208 patients, 182 had processable CVR data sets (median age, 68.2 years; 68% men). Although the strength of association depended on tissue type, lower CVR in normal-appearing tissues and white matter hyperintensity was associated with larger white matter hyperintensity volume (B NAWM =-0.0073 [95% CI, -0.0133 to -0.0014] %/mm Hg per 10-fold increase in percentage intracranial volume), more lacunes (B NAWM =-0.00129 [95% CI, -0.00215 to -0.00043] %/mm Hg per lacune), more microbleeds (B NAWM =-0.00083 [95% CI, -0.00130 to -0.00036] %/mm Hg per microbleed), higher deep atrophy score (B NAWM =-0.00218 [95% CI, -0.00417 to -0.00020] %/mm Hg per score point increase), higher perivascular space score (B NAWM =-0.0034 [95% CI, -0.0066 to -0.0002] %/mm Hg per score point increase in basal ganglia), and higher SVD score (B NAWM =-0.0048 [95% CI, -0.0075 to -0.0021] %/mm Hg per score point increase). Lower CVR in normal-appearing tissues was related to lower Montreal Cognitive Assessment without reaching convention statistical significance (B NAWM =0.00065 [95% CI, -0.00007 to 0.00137] %/mm Hg per score point increase)., Conclusions: Lower CVR in patients with SVD was related to more severe SVD burden and worse cognition in this cross-sectional analysis. Longitudinal analysis will help determine whether lower CVR predicts worsening SVD severity or vice versa., Registration: URL: https://www.isrctn.com; Unique identifier: ISRCTN12113543., Competing Interests: Disclosures Drs Morgan and Stringer receive funding from Siemens Healthineers. The other authors report no conflicts.

Background and Purpose- Perivascular spaces (PVS) around venules may help drain interstitial fluid from the brain. We examined relationships between suspected venules and PVS visible on brain magnetic resonance imaging. Methods- We developed a visual venular quantification method to examine the spatial relationship between venules and PVS. We recruited patients with lacunar stroke or minor nondisabling ischemic stroke and performed brain magnetic resonance imaging and retinal imaging. We quantified venules on gradient echo or susceptibility-weighted imaging and PVS on T2-weighted magnetic resonance imaging in the centrum semiovale and then determined overlap between venules and PVS. We assessed associations between venular count and patient demographic characteristics, vascular risk factors, small vessel disease features, retinal vessels, and venous sinus pulsatility. Results- Among 67 patients (69% men, 69.0±9.8 years), only 4.6% (range, 0%-18%) of venules overlapped with PVS. Total venular count increased with total centrum semiovale PVS count in 55 patients after accounting for venule-PVS overlap (β=0.468 [95% CI, 0.187-0.750]) and transverse sinus pulsatility (β=0.547 [95% CI, 0.309-0.786]) and adjusting for age, sex, and systolic blood pressure. Conclusions- Despite increases in both visible PVS and suspected venules, we found minimal spatial overlap between them in patients with sporadic small vessel disease, suggesting that most magnetic resonance imaging-visible centrum semiovale PVS are periarteriolar rather than perivenular.