Classification differentiates clinical and neuroanatomic features of cerebral small vessel disease

Abstract

Age-related cerebral small vessel disease involves heterogeneous pathogenesis such as arteriosclerosis/lipohyalinosis and cerebral amyloid angiopathy. MRI can visualise 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 etiologies. Currently, there is no available classification integrating these neuroimaging markers for differentiating clinical and neuroanatomic features of small vessel disease yet. In the present study, we tested whether our proposed stratification scheme could characterize specific clinical, neuroanatomic and potentially pathogenesis/etiologies 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 (1) bleeding or non-bleeding, (2) specific location of cerebral microbleeds, and (3) 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 type 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 type 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.

Imaging Markers of Subcortical Vascular Dementia in Patients With Multiple-Lobar Cerebral Microbleeds

“…The enrolled subjects with multiple lobar CMBs had a > 80% prevalence of hypertension history, high CAA-SVD scores reflexive of heavy CAA pathological loads, and a > 80% prevalence of mixed CMBs, and consequently, advanced HA and CAA co-pathologies indeed existed in our cohort. We believe that the subjects included in our study belong to an intermediate between SVD type 3 and 4 in the SVD imaging spectrum ( 43 ). Although mixed CMBs are considered to be driven mostly by HA, both HA and CAA can cause cerebral hypoperfusion and enhance amyloid production ( 44 ), explaining the synergistic effects of HA (Hypertensive Angiopathy) and CAA on the cumulative changes in the vasculopathic and neurodegenerative processes in cognitive deterioration ( 8 , 45 ).…”

Cerebral small vessel disease phenotype and 5-year mortality in asymptomatic middle-to-old aged individuals

“…We applied the previously established analytical framework to estimate volumetric information of multiple tissue types for each individual 7 , 15 . All the following analyses were conducted with Statistical Parametric Mapping (SPM12, version 7487, Wellcome Institute of Neurology, University College London, UK, http://www.fil.ion.ucl.ac.uk/spm/ ) and Matlab R2016a (The Mathworks, Inc., Natick, MA, USA) using default settings.…”

“…1 b) 7 . Common and distinct patterns of the clinical and neuroimaging manifestations were found in the four stratified CSVD subgroups 7 . This MRI-based stratification scheme highlights the distinct features of SVD and the possible underlying pathogenesis.…”

“…Recently, we developed a stratification scheme to classify a community-based, asymptomatic middle-to-old aged population into non-CSVD and four CSVD subtypes based on the following criteria in order: (1) bleeding or non-bleeding, (2) CMB locations, and (3) the severity and combination of WMH and lacune (Fig. 1 b) 7 . Common and distinct patterns of the clinical and neuroimaging manifestations were found in the four stratified CSVD subgroups 7 .…”

“…This MRI-based stratification scheme highlights the distinct features of SVD and the possible underlying pathogenesis. Stratified CSVD type 1, 2, and 3 might represent different severity of arteriosclerosis/lipohyalinosis CSVD, while CSVD type 4 suggests CAA 7 . However, whether this classification could differentiate longitudinal outcomes was not studied yet.…”