117 Distinct brain MRI phenotypes and their association with long-term dementia risk 6 6.3.5 Statistical analysis 6.3.5.1 Identification of subgroups with different brain MRI phenotypes All brain MRI markers were normalized as z-scores (after multiplication by 100 and natural log transformation when not normally distributed). Variables that were not normally distributed were WMH volumes, solidity, number of WMH, and lateral ventricle volume fraction. Binary variables (presence of microbleeds, infarcts, and enlarged perivascular spaces) were used as −2 and 2 to approximate the z-score distributions of continuous variables. Hierarchical clustering was performed by applying Ward’s method in R version 4.1.0 (R Core Team, 2021) and packages factoextra,16 cluster,17 and dendextend18 on 32 brain MRI markers. Hierarchical clustering groups participants together based on similarities in brain MRI markers. The approach starts with every participant as a separate cluster and then repeatedly merging of the 2 closest clusters, subsequently updating the distance matrix. Thus, each cluster is the result of the merge of 2 subclusters, resulting in a hierarchical tree (dendrogram, Figure 6.2). At each level of the dendrogram, clusters are joined and the number of clusters therefore decreases. This is repeated until only 1 cluster, representing the total group of participants, remains. An optimal number of clusters need to be determined for further analysis. In an optimally clustered data set, the clusters have a high within-cluster cohesion, while having a high separation between different clusters. The optimal dendrogram cutoff, that is, the optimal number of clusters, was determined using the Dunn index (supplementary figure S.6.8.2., links. lww.com/WNL/D459) and the heatmap (Figure 6.2). The Dunn index is the ratio of the smallest distance between observations in different clusters over the largest between cluster distance and should be maximal. After this procedure, a number of subgroups remained, representing the subgroups with different brain MRI phenotypes. Brain MRI markers and cardiovascular risk factors were compared between subgroups with chi-squared test for binary variables and 1-way analysis of variances for continuous variables by using SPSS version 25 (Chicago, IL). For these analyses, WMH volumes, number of WMH, solidity, ventricle volume fraction, and time to follow-up were log transformed due to a non-normal distribution. A p value <0.05 was considered statistically significant. 6.3.5.2 Sensitivity analysis To assess the robustness of the hierarchical clustering model, we reran the analysis with 2 random subsets of this dataset.
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