Cerebral microbleed burden is associated with domain-specific cognitive impairment pattern in acute stroke

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Authors: Yun-He Xia, Xiao Hu, Sun-hong Yan, Zi-Jie Wang, Fang-Jian Zhu, Chu Chen, Juan Li, Yue Sun, Chuanqin Fang, Li, Qi
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Abstract

Cerebral microbleeds (CMBs) represent a key imaging marker of cerebral small vessel disease (CSVD). This study aimed to investigate the relationship between CMB burden and domain-specific cognitive impairment in acute stroke patients. We enrolled 163 acute stroke patients stratified by Montreal Cognitive Assessment (MoCA) scores into cognitively impaired (MoCA ≤22, n = 79) and preserved (MoCA > 22, n = 84) groups. Demographic, clinical, laboratory, and neuroimaging data were collected. Multivariable logistic regression was used to identify factors associated with global cognitive impairment, while linear regression models were applied to evaluate the associations between CMB burden and specific cognitive domains. Patients with cognitive impairment were older, less educated, and exhibited a higher CSVD burden (all p < 0.05). In multivariate analyses, only age was independently associated with global cognitive impairment (OR = 1.082, p = 0.002). However, linear regression revealed that total CMB number was inversely correlated with visuospatial/executive function (β = −0.156) and memory recall (β = −0.201) after adjustment for confounding factors. Location-specific analysis demonstrated that mixed CMBs (involving both lobar and deep regions) were independently associated with visuospatial/executive deficits (β = −0.255), while deep CMBs specifically impacted abstract reasoning (β = −0.220). CMB burden, particularly mixed and deep distributions, is independently associated with specific cognitive domain impairments in acute stroke patients, highlighting the importance of CMB assessment for cognitive risk stratification.

Fields of Study & Subjects

Intracerebral and Subarachnoid Hemorrhage Research Cerebrovascular and genetic disorders Neurological Disease Mechanisms and Treatments

Publication details

Journal / source
Figshare
Publication date
1 January 2026
Document type
Dataset