PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
This work addresses the challenge of limited labeled data in brain network datasets, which is crucial for improving neural development and disorder diagnosis, though it is incremental as it builds on existing GNN methods.
The authors tackled the problem of scarce labeled data in brain network analysis by proposing PTGB, a pre-training framework for Graph Neural Networks that captures intrinsic brain network structures and adapts to downstream tasks, achieving robust and superior performance compared to baseline methods.
The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis have shown a growing interest in the interactions between brain regions of interest (ROIs) and their impact on neural development and disorder diagnosis. As a powerful deep model for analyzing graph-structured data, Graph Neural Networks (GNNs) have been applied for brain network analysis. However, training deep models requires large amounts of labeled data, which is often scarce in brain network datasets due to the complexities of data acquisition and sharing restrictions. To make the most out of available training data, we propose PTGB, a GNN pre-training framework that captures intrinsic brain network structures, regardless of clinical outcomes, and is easily adaptable to various downstream tasks. PTGB comprises two key components: (1) an unsupervised pre-training technique designed specifically for brain networks, which enables learning from large-scale datasets without task-specific labels; (2) a data-driven parcellation atlas mapping pipeline that facilitates knowledge transfer across datasets with different ROI systems. Extensive evaluations using various GNN models have demonstrated the robust and superior performance of PTGB compared to baseline methods.