Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets

•A graph auto-encoding method is developed to model the population distribution of brain graphs.•A predictive model is proposed to infer brain networks with human traits.•Brain structural connectome has a stronger association with human cognitive traits.•Individuals with high cognitive traits tend to have denser connections between hemispheres, higher overall network density, and lower average path length.•Network summary measures have higher variability across the children evaluated in the ABCD dataset than adults in the HCP dataset. There has been a huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationships with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) and edges to connection strengths between ROIs. Due to the high-dimensionality and non-Euclidean nature of networks, it is challenging to depict their population distribution and relate them to human traits. Current approaches focus on summarizing the network using either pre-specified topological features or principal components analysis (PCA). In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer their relationships to human traits. We refer to our method as Graph AuTo-Encoding (GATE). We applied GATE to two large-scale brain imaging datasets, the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) for adults, to study the structural brain connectome and its relationship with cognition. Numerical results demonstrate huge advantages of GATE over competitors in terms of prediction accuracy, statistical inference, and computing efficiency. We found that the structural connectome has a stronger association with a wide range of human cognitive traits than was apparent using previous approaches.