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.

Brain Communications
|June 16, 2021
PubMed

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.