Using Network Alignment for Analysis of Connectomes: Experiences from a Clinical Dataset

Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes is therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages, the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network and this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a graph alignment (GA) problem. In this work, we first define the problem formally, then we test some existing state of the art aligners on diffusion MRI-derived brain networks, and we compare the performances. The results confirm that GA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.