Statistical Analysis

CMBs and lacunes were treated as counts and categorical variables. For categorical data, we classify CMB and lacunes as: presence vs. absence and 1 vs. 0, ≥2 vs. 0, and by location (strictly lobar vs. no, strictly deep vs. no, and mixed vs. no).WMH volumes were logarithmically transformed due to skewed distribution and were divided into tertiles (second tertile vs. first tertile and third tertile vs. first tertile). We chose to present the results with ePVS as count variable in this study because the numbers of participants with no ePVS were too few in binary category. SVD markers were treated as determinants and ePVS as outcomes. In our secondary analysis, we divided our study subjects into two groups i.e., NCI group which included NCI and cognitive impairment group which included CIND and Dementia. In order to analyze the association between location and severity of SVD markers with ePVS counts, negative binomial regression was constructed with rate ratios (RR) and 95% confidence intervals (CI). All models were adjusted for age, gender, hypertension, hyperlipidemia, and diabetes. Results were considered significant at p < 0.05. In view of multiple testing performed between SVD and ePVS, we used Bonferroni correction to obtain revised statistical significance level of 0.05/2 ~ 0.025. All the data were analyzed using SPSS software package (version 25).