Stroke
September 10, 2026 · via Europe PMC
Arterial Spin Labeling Hemodynamic Indices Relate to Cognitive and Small Vessel Disease Trajectories Over 11 Years.
Robb WH, Santner AM, Zhang P, +8 more, Jefferson AL.
AI summary — for doctors, generated from the abstract only
An arterial spin labeling marker of arterial transit time (ASL-sCoV) tracked small vessel disease burden and cognitive decline better than cerebral blood flow.
What was studied
A longitudinal analysis of 667 older adults without dementia or stroke in the Vanderbilt Memory and Aging Project, relating baseline ASL-sCoV and cerebral blood flow to small vessel disease markers and cognition over up to 11 years.
Key findings
- Higher ASL-sCoV was cross-sectionally associated with white matter hyperintensities and enlarged perivascular spaces
- Higher ASL-sCoV was associated with accelerated decline in executive function, processing, language and visuospatial performance
- Cerebral blood flow associations were not significant after correction; specific statistics not fully captured in the abstract
Clinical relevance
ASL-sCoV may be a useful research marker of perfusion deficits related to small vessel disease; its role in clinical decision-making is not established.
Who should care
Limitations
Observational cohort from a single region; mean follow-up was about 5 years, and parts of the results are truncated in the abstract.
Slow arterial transit on ASL MRI may flag brains at risk for small vessel disease progression.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Hemodynamic insufficiency may contribute to the development of neuroimaging signs of cerebral small vessel disease (SVD). The arterial spin labeling (ASL) spatial coefficient of variation (ASL-sCoV) is a proxy marker of arterial transit time and may better capture SVD-related hemodynamic disturbances compared with ASL-derived cerebral blood flow. We investigated ASL-derived hemodynamic indices in relation to cognition and neuroimaging markers of SVD over an 11-year follow-up period.
Methods
Participants without dementia or stroke were enrolled in the Vanderbilt Memory and Aging Project, a longitudinal observational cohort study in Nashville, TN. Participants underwent serial multimodal 3T brain magnetic resonance imaging to quantify SVD burden and neuropsychological assessment from 2012 to 2024 (4.9±3.5 years mean follow-up). Pseudo-continuous ASL assessed cerebral blood flow and ASL-sCoV in total gray matter. Baseline ASL-sCoV and cerebral blood flow were individually related to the cross-sectional burden and longitudinal trajectory of each SVD neuroimaging marker (white matter hyperintensities, enlarged perivascular spaces, cerebral microbleeds, and lacunar infarcts) and cognitive performances using linear and linear mixed-effects regression models. Models were adjusted for demographics, cognitive status, Framingham Stroke Risk Profile (minus age), apolipoprotein E-ε4 status, intracranial volume (for SVD outcomes), and follow-up time (for longitudinal models).
Results
Among participants (n=667, 68±9 years, 18% mild cognitive impairment, 51% female), higher ASL-sCoV was cross-sectionally associated with SVD markers, including white matter hyperintensities and enlarged perivascular spaces (pFDR-valuespFDR=0.03) and accelerated decline in executive function, information processing, language, and visuospatial performances (PpFDR-values>0.05) or longitudinally (pFDR-values>0.06).
Conclusions
ASL-sCoV may serve as an important hemodynamic measure for identifying perfusion deficits tied to SVD burden and progression. Readily derived from existing ASL data sets, ASL-sCoV could provide substantial insight into the longitudinal clinical consequences of SVD.
DOI
10.1161/strokeaha.125.054879
PMID
42717860
PMCID
PMC13564205
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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