Background: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is the most common heritable cerebral small vessel disease (cSVD). Objective, quantitative measures are needed to track disease severity and progression. This study uses the SHIVA-AI neuroimaging analysis platform to quantify and compare key cSVD MRI biomarkers in individuals from the US CADASIL Consortium. Methods: We analyzed cross-sectional MRI data from the Consortium, a 12-site, 5-year natural history study following ~400 gene-positive carriers (PC) and 100 gene-negative family controls (NC) with 3 visits. As PC have a baseline mRS score of ≤3, the study is mainly pre-manifest. This preliminary dataset includes all participants with verified gene status and quality-controlled neuroimaging data. Using SHIVA-AI, we automatically quantified the burden of 3 cSVD biomarkers: total white matter hyperintensity (WMH) volume; number of cerebral microbleed (CMB) clusters; mean perivascular space (PVS) volume (Figure 1). To allow direct comparison, each biomarker was log-transformed and converted into an age-adjusted Z-score. We used linear regression models, adjusting for sex, site, and education, to estimate baseline differences and age-related cross-sectional associations between PC and NC groups. Results: After preliminary analysis, PC carry a significantly higher burden of specific cSVD markers than NC. The results are striking for WMH and CMB (Figure 2). PC (N=113) showed a substantially higher baseline WMH burden than NC (N=19) (group difference −log10 (p)=10.13). In fact, a significant interaction effect (−log10 (p)=4.41) indicates a significantly stronger cross-sectional association of WMH with age in the PC group. The PC group (N=134) had significantly more CMB clusters than NC (N=23) (group difference −log10 (p)=2.10). However, the cross-sectional association with age did not differ between groups. We observed no significant difference in either baseline PVS volume or change with age between PC (N=207) and NC (N=29). Conclusion: SHIVA-AI provides robust, automated, quantitative cSVD BM revealing significant group differences between CADASIL gene-positive individuals and NC. These tools capture the greater baseline disease burden in WMH and CMB and detect differential cross-sectional associations with age. SHIVA-AI biomarkers are sensitive measures of disease status and powerful potential endpoints for future clinical trials.
Misiura et al. (Thu,) studied this question.