Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI

• Published in: Frontiers in neuroinformatics Vol. 15; p. 802305
• Main Authors: Chu, Ying, Wang, Guangyu, Cao, Liang, Qiao, Lishan, Liu, Mingxia
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 13.01.2022; Frontiers Media S.A
• Subjects: Alzheimer's disease; Autism; Automation; Biomarkers; Brain; Brain mapping; Classification; Deep learning; Diagnosis; functional connectivity; Functional magnetic resonance imaging; graph convolutional networks; Graph representations; Learning; Medical diagnosis; Medical imaging; Neural networks; Neuroimaging; Neuroscience; Prediction models; resting-state functional MRI; Time series; functional connectivity; classification; graph convolutional networks; resting-state functional MRI; autism
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2021.802305

Resting-state functional MRI (rs-fMRI) has been widely used for the early diagnosis of autism spectrum disorder (ASD). With rs-fMRI, the functional connectivity networks (FCNs) are usually constructed for representing each subject, with each element representing the pairwise relationship between brain region-of-interests (ROIs). Previous studies often first extract handcrafted network features (such as node degree and clustering coefficient) from FCNs and then construct a prediction model for ASD diagnosis, which largely requires expert knowledge. Graph convolutional networks (GCNs) have recently been employed to jointly perform FCNs feature extraction and ASD identification in a data-driven manner. However, existing studies tend to focus on the single-scale topology of FCNs by using one single atlas for ROI partition, thus ignoring potential complementary topology information of FCNs at different spatial scales. In this paper, we develop a multi-scale graph representation learning (MGRL) framework for rs-fMRI based ASD diagnosis. The MGRL consists of three major components: (1) multi-scale FCNs construction using multiple brain atlases for ROI partition, (2) FCNs representation learning via multi-scale GCNs, and (3) multi-scale feature fusion and classification for ASD diagnosis. The proposed MGRL is evaluated on 184 subjects from the public Autism Brain Imaging Data Exchange (ABIDE) database with rs-fMRI scans. Experimental results suggest the efficacy of our MGRL in FCN feature extraction and ASD identification, compared with several state-of-the-art methods.