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Updated: Nov 7, 2025

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions
Published on: April 23, 2021
White matter abnormalities are key components of cerebrovascular disease impacting cognitive decline
Prashanthi Vemuri1, Jonathan Graff-Radford2, Timothy G Lesnick3
1Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Abstract:
While cerebrovascular disease can be observed in vivo using MRI, the multiplicity and heterogeneity in the mechanisms of cerebrovascular damage impede accounting for these measures in ageing and dementia studies. Our primary goal was to investigate the key sources of variability across MRI markers of cerebrovascular disease and evaluate their impact in comparison to amyloidosis on cognitive decline in a population-based sample. Our secondary goal was to evaluate the prognostic utility of a cerebrovascular summary measure from all markers. We included both visible lesions seen on MRI (white matter hyperintensities, cortical and subcortical infarctions, lobar and deep microbleeds) and early white matter damage due to systemic vascular health using diffusion changes in the genu of the corpus callosum. We identified 1089 individuals aged ≥60 years with concurrent amyloid-PET and MRI scans from the population-based Mayo Clinic Study of Aging. We divided these into discovery and validation datasets. Using the discovery dataset, we conducted principal component analyses and ascertained the main sources of variability in cerebrovascular disease markers. Using linear regression and mixed effect models, we evaluated the utility of these principal components and combinations of these components for the prediction of cognitive performance along with amyloidosis. Our main findings were (i) there were three primary sources of variability among the CVD measures-white matter changes are driven by white matter hyperintensities and diffusion changes; number of microbleeds (lobar and deep); and number of infarctions (cortical and subcortical); (ii) Components of white matter changes and microbleeds but not infarctions significantly predicted cognition trajectories in all domains with greater contributions from white matter; and (iii) The summary vascular score explained 3-5% of variability in baseline global cognition in comparison to 3-6% variability explained by amyloidosis. Across all cognitive domains, the vascular summary score had the least impact on memory performance (∼1%). Though there is mechanistic heterogeneity in the cerebrovascular disease markers measured on MRI, these changes can be grouped into three components and together explain variability in cognitive performance equivalent to the impact of amyloidosis on cognition. White matter changes represent dynamic ongoing damage, predicts future cognitive decline across all domains and diffusion measurements help capture white matter damage due to systemic vascular changes. Therefore, measuring and accounting for white matter changes using diffusion MRI and white matter hyperintensities along with microbleeds will allow us to capture vascular contributions to cognitive impairment and dementia.
Insights
Cerebrovascular disease markers, particularly white matter changes, significantly impact cognitive decline in older adults, comparable to amyloidosis. Diffusion MRI and white matter hyperintensities capture this vascular contribution to cognitive impairment.
Area of Science:
- Neuroimaging
- Gerontology
- Vascular Neurology
Background:
- Cerebrovascular disease (CVD) mechanisms are heterogeneous, complicating their assessment in aging and dementia.
- Magnetic Resonance Imaging (MRI) visualizes CVD, but variability in markers hinders interpretation.
Purpose of the Study:
- Investigate sources of variability in MRI-assessed CVD markers.
- Evaluate the impact of CVD on cognitive decline compared to amyloidosis.
- Assess the prognostic utility of a combined CVD measure.
Main Methods:
- Principal component analysis (PCA) on MRI markers (white matter hyperintensities, infarcts, microbleeds, diffusion changes) in 1089 older adults (≥60 years).
- Linear regression and mixed-effects models to predict cognitive performance using CVD components and amyloid levels.
- Discovery and validation datasets used for robust analysis.
Main Results:
- Three primary CVD variability components identified: white matter changes (hyperintensities/diffusion), microbleeds, and infarcts.
- White matter changes and microbleeds, but not infarcts, predicted cognitive trajectories.
- A summary vascular score explained 3-5% of cognitive variability, similar to amyloidosis (3-6%).
Conclusions:
- Despite mechanistic heterogeneity, three CVD components capture significant cognitive variability, comparable to amyloidosis.
- White matter changes, especially via diffusion MRI, are key indicators of ongoing vascular damage and predict cognitive decline.
- Integrating white matter changes (diffusion MRI, hyperintensities) and microbleeds improves understanding of vascular contributions to cognitive impairment and dementia.
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