Comparative survey of multigraph integration methods for holistic brain connectivity mapping

Creative Commons License

Chaari N., Camgöz Akdağ H., Rekık I.

Medical Image Analysis, vol.85, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 85
  • Publication Date: 2023
  • Doi Number: 10.1016/
  • Journal Name: Medical Image Analysis
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Biotechnology Research Abstracts, Compendex, EMBASE, INSPEC, MEDLINE
  • Keywords: Connectional brain template, Graph fusion techniques, Multigraph integration, Multiview brain connectivity
  • Istanbul Technical University Affiliated: Yes


© 2023One 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, global-level 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.