Graph Neural Networks in Network Neuroscience

By Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

313 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/db5d583782264529456a475ce8e9a90823b3a2b5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dinggang Shen
2. Qian Wang
3. Islem Rekik

In Cognitive Neuroscience

1. Dinggang Shen
2. Islem Rekik

Citations in the corpus

• Transformers in Medical Image Analysis: A Review (2022, background): “Brain graphs play an important role in advancing our understanding of the brain as highly interconnected system in both health and disease [61], [62].”