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

ABSTRACT: 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

SUBMITTER: Liu M 

PROVIDER: S-EPMC9659310 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature