ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2020
Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
50 citing papers in PubMed, 169 citations in OpenAlex.
- Learning robust and task-invariant functional representation from fMRI through Siamese self-supervised learning.Medical image analysis · 2026Article
- Hierarchical sparse spatiotemporal graph neural network for brain graph classification.iScience · 2026Article
- Identification of Major Depressive Disorder Using Multiple Functional Connection Patterns.CNS neuroscience & therapeutics · 2026Article
- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns With Modularity and State Experts.IEEE transactions on medical imaging · 2026Article
- Multimodal spatiotemporal graph convolutional attention network for dynamic risk stratification and intervention strategy generation in rare disease rehabilitation nursing.Scientific reports · 2026Article
- Classifying irritable bowel syndrome using spatio-temporal graph convolution networks on brain functional MRI data.Brain communications · 2026Article
- STNAGNN: Data-driven Spatio-temporal Brain Connectivity beyond FC.Proceedings of machine learning research · 2026Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.ArXiv · 2025Article
- dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts.bioRxiv : the preprint server for biology · 2025Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.Proceedings of machine learning research · 2025Article
- TOWARDS ZERO-SHOT TASK-GENERALIZABLE LEARNING ON FMRI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- MASKED MOMENTUM CONTRASTIVE DYNAMIC TRANSFORMER FOR SELF-SUPERVISED FUNCTIONAL CONNECTIVITY REPRESENTATION LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.Psychoradiology · 2025Review
- ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.NeuroImage · 2025Article
- Spatial Craving Patterns in Marijuana Users: Insights From fMRI Brain Connectivity Analysis With High-Order Graph Attention Neural Networks.IEEE journal of biomedical and health informatics · 2025Article
- Multi-view united transformer block of graph attention network based autism spectrum disorder recognition.Frontiers in psychiatry · 2025Article
- SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis.Frontiers in neuroscience · 2025Article
- Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs-fMRI either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brain activity. To overcome those shortcomings, we propose to formulate functional connectivity networks within the context of spatio-temporal graphs. We train a spatio-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. Simultaneously, the model learns the importance of graph edges within ST-GCN to gain insight into the functional connectivities contributing to the prediction. In analyzing the rs-fMRI of the Human Connectome Project (HCP,
Identifiers
What OpenQuestion holds
Registered trials
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.