mlsanityData to 7 Oct 2026

FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation

arXiv 2205.12465 ↗First posted 25 May 2022cs.LG cs.NE eess.IVcited by 66

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Abstract (arXiv)

Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy and unaware of downstream prediction tasks, while also incompatible with the deep graph neural network (GNN) models. In order to fully unleash the power of GNNs in network-based fMRI analysis, we develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via deep brain network generation. In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks. Along with the process, the key novel component is the graph generator which learns to transform raw time-series features into task-oriented brain networks. Our learnable graphs also provide unique interpretations by highlighting prediction-related brain regions. Comprehensive experiments on two datasets, i.e., the recently released and currently largest publicly available fMRI dataset Adolescent Brain Cognitive Development (ABCD), and the widely-used fMRI dataset PNC, prove the superior effectiveness and interpretability of FBNETGEN. The implementation is available at https://github.com/Wayfear/FBNETGEN.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). Builds on 14 of its 60 references that are in the map. First posted on arXiv 25 May 2022.

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14 of 60 references.

References · 14

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  6. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  7. 2019Learning Discrete Structures for Graph Neural Networkscited by 228
  8. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  9. 2021SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Predictioncited by 97
  10. 2019Graph Neural Network for Interpreting Task-fMRI Biomarkerscited by 40
  11. 2017Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classificationcited by 20
  12. 2013Analyzing complex functional brain networks: fusing statistics and network science to understand the braincited by 8
2 more
  1. 2018A hierarchical independent component analysis model for longitudinal Neuroimaging studiescited by 7
  2. 2017Bayesian Joint Modeling of Multiple Brain Functional Networkscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 20; 2024: 15; 2025: 15; 2026: 8.

66 papers.

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  1. 2022Brain Network Transformercited by 92
  2. 2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 58
  3. 2023AutoReP: Automatic ReLU Replacement for Fast Private Network Inferencecited by 29
  4. 2023Community-Aware Transformer for Autism Prediction in fMRI Connectomecited by 27
  5. 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inferencecited by 26
  6. 2023Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networkscited by 22
  7. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  8. 2022Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learningcited by 15
  9. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  10. 2022Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networkscited by 12
  11. 2023RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inferencecited by 12
  12. 2023Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Datacited by 12
38 more
  1. 2024BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equationscited by 12
  2. 2024Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identificationcited by 12
  3. 2025Long-range Brain Graph Transformercited by 11
  4. 2023PTGB: Pre-Train Graph Neural Networks for Brain Network Analysiscited by 10
  5. 2023Learning High-Order Relationships of Brain Regionscited by 10
  6. 2023BrainNPT: Pre-training of Transformer networks for brain network classificationcited by 9
  7. 2024Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusioncited by 9
  8. 2023Transformer-Based Hierarchical Clustering for Brain Network Analysiscited by 8
  9. 2023R-Mixup: Riemannian Mixup for Biological Networkscited by 8
  10. 2023Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Trainingcited by 8
  11. 2025Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Helpcited by 8
  12. 2023Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysiscited by 7
  13. 2024DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networkscited by 7
  14. 2025fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understandingcited by 7
  15. 2022Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Predictioncited by 6
  16. 2024BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signalscited by 6
  17. 2024Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysiscited by 6
  18. 2023QuMoS: A Framework for Preserving Security of Quantum Machine Learning Modelcited by 5
  19. 2022DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learningcited by 4
  20. 2023Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identificationcited by 4
  21. 2025Core-Periphery Principle Guided State Space Model for Functional Connectome Classificationcited by 4
  22. 2024Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Braincited by 3
  23. 2022Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detectioncited by 2
  24. 2024Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learningcited by 2
  25. 2025BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classificationcited by 2
  26. 2025Refined Causal Graph Structure Learning via Curvature for Brain Disease Classificationcited by 2
  27. 2025Hierarchical Characterization of Brain Dynamics via State Space-based Vector Quantizationcited by 2
  28. 2023Transformer and Snowball Graph Convolution Learning for Brain functional network Classificationcited by 1
  29. 2023BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Modelcited by 1
  30. 2023Balanced Graph Structure Information for Brain Disease Detectioncited by 1
  31. 2025Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesiscited by 1
  32. 2025ASDFormer: A Transformer with Mixtures of Pooling-Classifier Experts for Robust Autism Diagnosis and Biomarker Discoverycited by 1
  33. 2025VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRIcited by 1
  34. 2025Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraintscited by 1
  35. 2026Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imagingcited by 1
  36. 2023TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysiscited by 0
  37. 2023Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classificationcited by 0
  38. 2024Learnable Community-Aware Transformer for Brain Connectome Analysis with Token Clusteringcited by 0

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