Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.

ABSTRACT: Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjace

SUBMITTER: Kwon H 

PROVIDER: S-EPMC9301472 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature