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Updated: Nov 2, 2025

Longitudinal In Vivo Imaging of the Cerebrovasculature: Relevance to CNS Diseases
Published on: December 6, 2016
Classification differentiates clinical and neuroanatomic features of cerebral small vessel disease
Kun-Hsien Chou1,2, Pei-Lin Lee1, Li-Ning Peng3,4,5
1Institute of Neuroscience, National Yang Ming Chiao Tung University College of Medicine, Taipei 112, Taiwan.
Abstract:
Age-related cerebral small vessel disease involves heterogeneous pathogenesis, such as arteriosclerosis/lipohyalinosis and cerebral amyloid angiopathy. MRI can visualize the brain lesions attributable to small vessel disease pathologies, including white-matter hyperintensities, lacune and cerebral microbleeds. However, these MRI markers usually coexist in small vessel disease of different aetiologies. Currently, there is no available classification integrating these neuroimaging markers for differentiating clinical and neuroanatomic features of small vessel disease yet. In this study, we tested whether our proposed stratification scheme could characterize specific clinical, neuroanatomic and potentially pathogenesis/aetiologies in classified small vessel disease subtypes. Cross-sectional analyses from a community-based non-demented non-stroke cohort consisting of ≥50 years old individuals were conducted. All participants were scanned 3T brain MRI for small vessel disease detection and neuroanatomic measurements and underwent physical and cognitive assessments. Study population were classified into robust and four small vessel disease groups based on imaging markers indicating (i) bleeding or non-bleeding; (ii) specific location of cerebral microbleeds; and (iii) the severity and combination of white-matter hyperintensities and lacune. We used whole-brain voxel-based morphometry analyses and tract-based spatial statistics to evaluate the regional grey-matter volume and white-matter microstructure integrity for comparisons among groups. Among the 735 participants with eligible brain MRI images, quality screening qualified 670 for grey-matter volume analyses and 617 for white-matter microstructural analyses. Common and distinct patterns of the clinical and neuroimaging manifestations were found in the stratified four small vessel disease subgroups. Hierarchical clustering analysis revealed that small vessel disease type 4 had features distinct from the small vessel disease types 1, 2 and 3. Abnormal white-matter microstructures and cognitive function but preserved physical function and grey-matter volume were found in small vessel disease type 4. Among small vessel disease types 1, 2 and 3, there were similar characteristics but different severity; the clinical features showed both physical frail and cognitive impairment and the neuroanatomic features revealed frontal-subcortical white-matter microstructures and remote, diffuse cortical abnormalities. This novel stratification scheme highlights the distinct clinical and neuroanatomic features of small vessel disease and the possible underlying pathogenesis. It could have potential application in research and clinical settings.
Insights
A new classification for small vessel disease (SVD) using MRI markers differentiates subtypes. SVD Type 4 shows distinct white matter and cognitive issues, unlike Types 1-3 with broader physical and cognitive impairments.
Area of Science:
- Neurology
- Radiology
- Neuroimaging
Background:
- Age-related cerebral small vessel disease (SVD) has complex causes like arteriosclerosis and amyloid angiopathy.
- MRI reveals SVD lesions (white-matter hyperintensities, lacunes, microbleeds), but these often coexist, complicating diagnosis.
- Current classifications lack integration of neuroimaging markers to differentiate SVD subtypes.
Purpose of the Study:
- To test a novel stratification scheme for classifying SVD subtypes based on MRI markers.
- To determine if this classification can characterize distinct clinical, neuroanatomic, and etiological features of SVD.
- To explore potential applications in research and clinical settings.
Main Methods:
- Cross-sectional study of 735 non-demented, non-stroke individuals aged ≥50 years.
- 3T brain MRI for SVD detection; classification into robust and four SVD groups based on lesion type, location, and severity.
- Voxel-based morphometry and tract-based spatial statistics for grey-matter volume and white-matter microstructure analysis.
Main Results:
- Hierarchical clustering identified SVD Type 4 as distinct from Types 1-3.
- SVD Type 4 exhibited abnormal white matter microstructure and cognitive deficits with preserved grey matter and physical function.
- SVD Types 1-3 showed varying severity but shared features of physical frailty, cognitive impairment, and frontal-subcortical/cortical abnormalities.
Conclusions:
- The proposed stratification scheme effectively distinguishes SVD subtypes based on neuroimaging.
- The classification highlights unique clinical and neuroanatomic profiles, suggesting different underlying pathologies.
- This approach offers a valuable tool for SVD research and clinical practice.
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