Our interest focuses on developing statistical methods for analysis of brain structural connectomes . Nodes in the brain connectome graph correspond to different regions of interest (ROIs) while edges correspond to white matter fiber connections between these ROIs. Due to the high-dimensionality and non-Euclidean nature of the data, it becomes challenging to conduct analyses of the population distribution of brain connectomes and relate connectomes to other factors, such as cognition . Current approaches focus on summarizing the graph using either pre-specified topological features or principal components analysis (PCA). In this article, we instead develop a nonlinear latent factor model for summarizing the brain graph in both unsupervised and supervised settings. The proposed approach builds on methods for hierarchical modeling of replicated graph data, as well as variational auto-encoders that use neural networks for dimensionality reduction. We refer to our method as Graph AuTo-Encoding (GATE). We compare GATE with tensor PCA and other competitors through simulations and applications to data from the Human Connectome Project (HCP)  .
There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship 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 the relationships between brain structural connectomes and 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 understand 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 structural connectomes have a stronger association with a wide range of human cognitive traits than was apparent using previous approaches .
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Our interest focuses on developing statistical methods for analysis of brain structural connectomes . Nodes in the brain connectome graph correspond
There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding
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while edges correspond to white matter fiber connections between these
and edges to connection strengths between
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the data, it becomes challenging to conduct analyses of the population distribution of brain connectomes and relate connectomes to other factors, such as cognition
networks, it is challenging to depict their population distribution and relate them to human traits
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article, we instead
paper, building on recent advances in deep learning, we
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for summarizing the brain graph in both unsupervised and supervised settings. The proposed approach builds on methods for hierarchical modeling of replicated graph data, as well as variational auto-encoders that use neural networks for dimensionality reduction.
to characterize the population distribution of brain graphs and infer the relationships between brain structural connectomes and human traits.
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compare GATE with tensor PCA and other competitors through simulations and applications to data from the
applied GATE to two large-scale brain imaging datasets, the Adolescent Brain Cognitive Development (ABCD) study and the
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for adults, to understand 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 structural connectomes have a stronger association with a wide range of human cognitive traits than was apparent using previous approaches