Template-based graph registration network for boosting the diagnosis of brain connectivity disorders


Gürler Z., Gharsallaoui M. A., Rekık I.

Computerized Medical Imaging and Graphics, vol.103, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 103
  • Publication Date: 2023
  • Doi Number: 10.1016/j.compmedimag.2022.102140
  • Journal Name: Computerized Medical Imaging and Graphics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Keywords: Adversarial learning, Brain dysconnectivity disorder diagnosis, Brain graph registration, Connectional brain template, Graph neural networks
  • Istanbul Technical University Affiliated: Yes

Abstract

© 2022 Elsevier LtdBrain graphs are powerful representations to explore the biological roadmaps of the human brain in its healthy and disordered states. Recently, a few graph neural networks (GNNs) have been designed for brain connectivity synthesis and diagnosis. However, such non-Euclidean deep learning architectures might fail to capture the neural interactions between different brain regions as they are trained without guidance from any prior biological template—i.e., template-free learning. Here we assume that using a population-driven brain connectional template (CBT) that captures well the connectivity patterns fingerprinting a given brain state (e.g., healthy) can better guide the GNN training in its downstream learning task such as classification or regression. To this aim we design a plug-in graph registration network (GRN) that can be coupled with any conventional graph neural network (GNN) so as to boost its learning accuracy and generalizability to unseen samples. Our GRN is a graph generative adversarial network (gGAN), which registers brain graphs to a prior CBT. Next, the registered brain graphs are used to train typical GNN models. Our GRN can be integrated into any GNN working in an end-to-end fashion to boost its prediction accuracy. Our experiments showed that GRN remarkably boosted the prediction accuracy of four conventional GNN models across four neurological datasets.