Graph Neural Networks in Network Neuroscience
It addresses the problem of improving neurological disorder diagnosis and population graph integration for researchers and clinicians in neuroscience, but it is incremental as it reviews existing methods rather than introducing new ones.
This paper reviews the application of Graph Neural Networks (GNNs) in network neuroscience, highlighting their use in tasks like missing brain graph synthesis and disease classification to enhance performance in analyzing brain connectivity data.
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.