Classification of Normal and Pathological Brain Networks Based on Similarity in Graph Partitions

We consider a task of classifying normal and pathological brain networks. These networks (called connectomes) represent macroscale connections between predefined brain regions, hence, the nodes of connectomes are uniquely labeled and the set of labels (brain regions) is the same across different brains. We make use of this property and hypothesize that connectomes obtained from normal and pathological brains differ in how brain regions cluster into communities. We develop an algorithm that computes distances between brain networks based on similarity in their partitions and uses these distances to produce a kernel for a support vector machine (SVM) classifier. We demonstrate how the proposed model classifies brain networks of carriers and non-carriers of an allele associated with an increased risk of Alzheimer's disease. The obtained classification quality is ROC AUC 0.7 which is higher than that of the baseline.