Graph Neural Networks in Network Neuroscience

• URL: http://arxiv.org/abs/2106.03535v1
• Date: Mon, 7 Jun 2021 11:49:57 GMT
• Title: Graph Neural Networks in Network Neuroscience
• Authors: Alaa Bessadok, Mohamed Ali Mahjoub and Islem Rekik
• Abstract summary: graph neural network (GNN) provides a clever way of learning the deep graph structure. GNN-based methods 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.
• Score: 1.6114012813668934
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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