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.

本研究提出了一种针对大规模脑网络数据的 GNN 预训练框架 PTGB，能在没有特定任务标签的情况下学习脑网络的内在结构，并易于适应各种下游任务，从而实现对脑网络结构复杂性的自适应处理及临床神经疾病诊断的快速准确。