Graph Neural Networks in Network Neuroscience

• Published in: IEEE transactions on pattern analysis and machine intelligence 2023-05, Vol.45 (5), p.5833-5848
• Main Authors: Bessadok, Alaa, Mahjoub, Mohamed Ali, Rekik, Islem
• Format: Article
• Language: English
• Subjects: Algorithms; Brain; Brain - diagnostic imaging; Brain graph; Brain modeling; connectome; Diseases; geometric deep learning; graph neural network; Graph neural networks; graph theory; graph topology; Humans; Learning; Magnetic resonance imaging; Medical imaging; Neural Networks, Computer; Neuroimaging; Neurological diseases; Neuroscience; Sociology; Task analysis
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2022.3209686

Noninvasive 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 https://github.com/basiralab/GNNs-in-Network-Neuroscience .