Clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates

Abstract

Recently, the use of connectional brain templates (CBTs) has revolutionized the field of neurological disorder diagnosis through providing integral representation maps of a population-driven brain connectivity and effective identification of atypical changes in brain connectivity. Ideally, a reliable CBT should satisfy the following criteria: (1) centeredness as it occupies the center of the brain network population, and (2) discriminativeness as it allows to identify differences in brain connectivity between populations with different brain states (e.g., healthy and disordered). Existing state-of-the-art methods for connectional brain template (CBT) estimation from a population of multi-view brain networks (also called brain multigraphs) learn the integration process in a dichotomized manner, where different learning steps are pieced in together independently. Hence, such frameworks are inherently agnostic to the cumulative estimation error from step to step. This is a key limitation that we addressed by capitalizing on the power of deep learning frameworks residing in learning an end-to-end deep mapping using a single objective function to optimize to transform input data into target output data. In this paper, we propose to learn a many-to-one deep learning mapping by designing a clustering-based multi-graph integrator network (MGINet). Our MGINet inputs population of brain multigraphs (many) and outputs a single CBT graph (one). We first propose to tease apart brain multigraph data heterogeneity by first clustering similar samples together using multi-kernel manifold learning. In this way, we are optimally learning to disentangle the heterogeneity of our population and facilitating the integration task for our MGINet. Next, for each cluster, we first integrate the multigraph of each subject into a single graph, then merge the generated graphs into a cluster-specific CBT. Finally, we simply average the cluster-specific CBTs into a final CBT. Our experimental results show that our MGINet largely outperforms state-of-the-art methods in terms of centeredness and representativeness of the estimated CBT using both autistic and healthy brain multigraph datasets. Our clustering-based MGINet (cMGINet) source code is available at https://github.com/basiralab/cMGINet in Python.