Neuroinformatics

Oligodendrocyte

Neuroscience

Graph theory

Theoretical computer science

Computer science

Environmental health

Cognitive science

Computational neuroscience

Psychology

Graph

Mathematics

Central nervous system

Population

Data science

Artificial intelligence

Myelin

Combinatorics

Medicine

Deep learning

Artificial neural network

Power graph analysis

Systems neuroscience

Machine learning

Neuroimaging

IEEE Transactions on Pattern Analysis and Machine Intelligence

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.

Graph Neural Networks in Network Neuroscience