Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping

Abstract

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: centeredness, biomarker-reproducibility, node-level similarity, globallevel similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.

Predicting Brain Multigraph Population from a Single Graph Template for Boosting One-Shot Classification

“…However, to the best of our knowledge and as revealed by this recent GNN in network neuroscience review paper [2], one-shot GNN learning remains unexplored in the field of network neuroscience -with the exception of [12] where one-shot GNN architectures are trained for brain connectivity regression and classification tasks. Specifically, representative connectional brain templates (CBTs) [17] were used to train GNN architectures in one-shot fashion. Such graph templates present a compact representation of a particular brain state.…”