Graph Neural Networks in Network Neuroscience

TL;DR

This paper reviews how graph neural networks are transforming network neuroscience by improving brain connectivity analysis, disease classification, and brain graph synthesis, highlighting current methods and future directions.

Contribution

It provides a comprehensive review of GNN-based methods in network neuroscience and suggests future applications for neurological disorder diagnosis.

Findings

GNNs enhance brain graph analysis accuracy

GNNs enable better disease classification

Future potential in neurological disorder diagnosis

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.