Graph Neural Networks in Network Neuroscience

Creative Commons License

Bessadok A., Mahjoub M. A., Rekık I.

IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.45, no.5, pp.5833-5848, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 45 Issue: 5
  • Publication Date: 2023
  • Doi Number: 10.1109/tpami.2022.3209686
  • Journal Name: IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.5833-5848
  • Keywords: Brain graph, connectome, geometric deep learning, graph neural network, graph theory, graph topology
  • Istanbul Technical University Affiliated: Yes


IEEENoninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain –namely brain graph. Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration. The list of papers cited in our work is available at