ArticleNetwork neuroscience (Cambridge, Mass.)2021
Graph convolutional network for fMRI analysis based on connectivity neighborhood.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Brain Graph Sparsification for fMRI-based Connectome Analysis: A Methodological Review.Neuroinformatics · 2026Review
- Classifying irritable bowel syndrome using spatio-temporal graph convolution networks on brain functional MRI data.Brain communications · 2026Article
- Multiplex functional connectome graph transformer for cognitive vulnerability in dialysis-treated chronic kidney disease.Frontiers in psychiatry · 2026Article
- Transformers for fMRI analysis: from spatiotemporal modeling to brain foundation models.Frontiers in neuroinformatics · 2026Review
- Evaluating Cognitive Function and Brain Activity Patterns via Blood Oxygen Level-Dependent Transformer in N-Back Working Memory Tasks.Brain sciences · 2025Article
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.Medical & biological engineering & computing · 2024Article
- Classification accuracy of structural and functional connectomes across different depressive phenotypes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Sparsity-guided multiple functional connectivity patterns for classification of schizophrenia via convolutional network.Human brain mapping · 2023Article
- Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification.Medical image analysis · 2023Article
- TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network.Frontiers in neuroscience · 2023Article
- A transformer model for learning spatiotemporal contextual representation in fMRI data.Network neuroscience (Cambridge, Mass.) · 2023Article
- Resting-State Functional MRI Adaptation with Attention Graph Convolution Network for Brain Disorder Identification.Brain sciences · 2022Article
- Decoding Visual fMRI Stimuli from Human Brain Based on Graph Convolutional Neural Network.Brain sciences · 2022Article
- Article
- An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.Medical image analysis · 2022Article
- Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity.Brain structure & function · 2022Article
- Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis.Frontiers in human neuroscience · 2022Article
- Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer's disease and autism spectrum disorder.Frontiers in aging neuroscience · 2022Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.
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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.