ArticleFrontiers in neuroinformatics2021
Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI.
Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Trial
- BrainUMA: A Unified multi-atlas learning framework for brain disorders diagnosis.Medical & biological engineering & computing · 2026Article
- Lean-NET-Based Local Brain Connectome Analysis for Autism Spectrum Disorder Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Integrating multi-atlas neuroimaging data for robust biomarker identification in neuropsychiatric disorders.Frontiers in psychiatry · 2026Article
- Article
- Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder.Scientific reports · 2025Article
- Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications.Health data science · 2025Review
- Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data.Frontiers in computational neuroscience · 2025Article
- [A study on post-traumatic stress disorder classification based on multi-atlas multi-kernel graph convolutional network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification.Biology · 2023Article
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study.Bioengineering (Basel, Switzerland) · 2023Article
- The effect of node features on GCN-based brain network classification: an empirical study.PeerJ · 2023Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Multi-band network fusion for Alzheimer's disease identification with functional MRI.Frontiers in psychiatry · 2022Article
- From brain scans to classifiers: A systematic review of ML-based autism diagnostic frameworks.Digital healthReview
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.