research-article

Graph Neural Networks in Network Neuroscience

Authors:

Mohamed Ali Mahjoub,

Islem RekikAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 45, Issue 5

Pages 5833 - 5848

https://doi.org/10.1109/TPAMI.2022.3209686

Published: 26 September 2022 Publication History

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 <uri>https://github.com/basiralab/GNNs-in-Network-Neuroscience</uri>.

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IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 45, Issue 5

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Cited By

Liu ZWan GPrakash BLau MJin WBaeza-Yates RBonchi F(2024)A Review of Graph Neural Networks in Epidemic ModelingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671455(6577-6587)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671455
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