Structural connectivity-based subtyping of healthy individuals using elastic net subspace clustering

Individuals vary in behavior and cognition within a diseased or even "normal" population. Subtyping is critical for characterizing and understanding individual variations in behavior. Neuroimaging-based subtyping has emerged recently and shown great potential for clustering individuals with distinct neural patterns across sub-clusters. However, due to the high dimensional nature of neuroimaging data and relatively limited sample size, commonly used clustering methods in most existing studies such as k-mean may undermine the subtyping results due to the lack of power. Subspace clustering method with elastic net regularization is superior in the sense of jointly learning the sparse affinity matrix and its clustering to preserve reliable high dimension information during clustering limited number of samples. The current study aimed to introduce the Elastic Net subspace clustering to subtype brain structural connectivity matrices robustly. We have included 105 healthy young subjects and constructed structural connectivity matrices based on diffusion tensor imaging as input features for clustering. By calculating and optimizing two indices, "Silhouette Coefficient" and "Calinski-Harabasz" index, optimal parameters were selected to balance the low rank sparse subspace clustering and least square regression and determine the optimal number of clusters. Then the stability of clustering results was tested by subsampling all subjects and clustering each subgroup 3000 times, and the across- and within-subject clustering error rates were estimated. After receiving two robust clusters with high stability, we further explored and found different neural connectivity patterns between clusters. Results suggest that neurosubtyping has the potential to reveal underlying distinct neural patterns speaking to the variations in behavior and neurobehavioral relationships.