Deep reinforcement learning guided graph neural networks for brain network analysis

TL;DR

This paper introduces BN-GNN, a novel framework that uses deep reinforcement learning to automatically optimize GNN architectures for individual brain networks, enhancing analysis performance.

Contribution

BN-GNN is the first method to adaptively determine GNN depth for each brain network, addressing limitations of fixed-layer GNNs in brain analysis.

Findings

BN-GNN outperforms traditional GNNs on multiple brain network datasets.

Adaptive GNN architecture improves analysis accuracy.

Deep reinforcement learning effectively guides GNN customization.

Abstract

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted many GNN-based methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into meaningful low-dimensional representations used for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. To solve this problem, we propose a novel brain network representation framework, namely BN-GNN, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to train a meta-policy to automatically determine the optimal number of feature aggregations (reflected in the number of GNN layers) required for a given brain network. Extensive experiments on eight real-world brain network datasets demonstrate that our proposed BN-GNN improves the performance of traditional GNNs on different brain network analysis tasks.