PRE-TRAIN GRAPH NEURAL NETWORKS FOR BRAIN NETWORK ANALYSIS

Abstract

Human brains, controlling behaviors and cognition, are at the center of complex neurobiological systems. Recent studies in neuroscience and neuroimaging analysis have reached a consensus that interactions among brain regions of interest (ROIs) are driving factors for neural development and disorders. Graph neural networks (GNNs) as a powerful tool for analyzing graph-structured data are naturally applied to the analysis of brain networks. However, training of deep learning models including GNNs often requires a significant amount of labeled data. Due to the complicated data acquisition process and restrictions on data sharing, brain network datasets are still small compared to other types of graphs (e.g., social networks, molecules, proteins). Moreover, real clinical tasks (e.g., mental disorder analysis) are often conducted on local datasets with even smaller scales and larger noises. To this end, we propose to leverage pre-training to capture the intrinsic brain network structures regardless of specific clinical outcomes, for obtaining GNN models that are easily adaptable to downstream tasks. Specifically, (1) we design brain-network-oriented unsupervised pre-training techniques to utilize large-scale brain imaging studies without highly relevant task labels; (2) we develop a data-driven parcellation atlas mapping pipeline to facilitate effective knowledge transfer across studies with different ROI systems. The proposed framework is validated with various GNN models, with extensive empirical results demonstrating consistent improvement in performance as well as robustness.

1. INTRODUCTION

In recent years, the analysis of brain networks has attracted considerable interest in neuroscience studies. Brain networks are essentially graphs, where anatomical regions of interest (ROIs) given a parcellation atlas are formed into nodes, and the connectivities among ROIs are formed into edges. Based on brain networks constructed from different modalities such as Diffusion Tensor Imaging (DTI) and functional Magnetic Resonance Imaging (fMRI), effective graph analysis plays a pivotal role in understanding the biological structures and functions of complex neural systems, which can be helpful in the early diagnosis of neurological disorders and facilitate neuroscience research Martensson et al. ( 2018 



);Yahata et al. (2016);Lindquist (2008);Smith (2012).Deep learning has replenished the fields of computer science and beyond. Among various modern deep learning models, the emerging graph neural networks (GNNs) have demonstrated superior performance and even plausible interpretability on a variety of network datasets, including social networks, recommender systems, knowledge graphs, protein and gene networks, molecules and so forth Kipf & Welling (2017); Hamilton et al. (2017); Schlichtkrull et al. (2018); Vashishth et al. (2020); Xu et al. (2019); Ying et al. (2018); He et al. (2020b); Zhang et al. (2020); Liu et al. (2022); Xiong et al. (2020), due to its powerful representations and efficient computations of complex graph structures towards specific downstream tasks. Such achievements on other types of networked data propel studies on GNNs for brain networks, especially models for graph-level classification/regression Ying et al. (2018); Xu et al. (2019); Errica et al. (2020) and important vertex/edge identification Ying et al. (2019); Luo et al. (2020); Vu & Thai (2020), towards tasks such as connectome-based disease prediction and multi-level neural pattern discovery. However, the training of powerful deep learning models including GNNs often requires significant amounts of labeled data Hu et al. (2020a); You et al. (2020); Zhu et al. (2021a). For brain network analysis, there are limited big imaging datasets from a few large-scale national neuroimaging studies such as the ABCD Casey et al. (2018), ADNI

