mlsanityData to 14 Oct 2026

Brain Network Transformer

arXiv 2210.06681 ↗First posted 13 Oct 2022cs.LG cs.CV cs.NEcited by 94

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Abstract (arXiv)

Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data, we model brain networks as graphs with nodes of fixed size and order, which allows us to (1) use connection profiles as node features to provide natural and low-cost positional information and (2) learn pair-wise connection strengths among ROIs with efficient attention weights across individuals that are predictive towards downstream analysis tasks. Moreover, we propose an Orthonormal Clustering Readout operation based on self-supervised soft clustering and orthonormal projection. This design accounts for the underlying functional modules that determine similar behaviors among groups of ROIs, leading to distinguishable cluster-aware node embeddings and informative graph embeddings. Finally, we re-standardize the evaluation pipeline on the only one publicly available large-scale brain network dataset of ABIDE, to enable meaningful comparison of different models. Experiment results show clear improvements of our proposed Brain Network Transformer on both the public ABIDE and our restricted ABCD datasets. The implementation is available at https://github.com/Wayfear/BrainNetworkTransformer.

Key facts. Cited by 94 papers in the map (data through 14 Oct 2026). Builds on 36 of its 83 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

36 of 83 references.

References · 36

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2017Graph Attention Networkscited by 8,923
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  6. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  7. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,850
  8. 2018Link Prediction Based on Graph Neural Networkscited by 832
  9. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 767
  10. 2013Exact solutions to the nonlinear dynamics of learning in deep linear neural networkscited by 763
  11. 2020A Generalization of Transformer Networks to Graphscited by 661
  12. 2020Heterogeneous Graph Transformercited by 582
24 more
  1. 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
  2. 2022MaxViT: Multi-Axis Vision Transformercited by 476
  3. 2021Rethinking Graph Transformers with Spectral Attentioncited by 473
  4. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 333
  5. 2021Nested Graph Neural Networkscited by 137
  6. 2021Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attentioncited by 87
  7. 2020Differentiable Graph Module (DGM) for Graph Convolutional Networkscited by 86
  8. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 75
  9. 2022GRPE: Relative Positional Encoding for Graph Transformercited by 70
  10. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  11. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 67
  12. 2021Gophormer: Ego-Graph Transformer for Node Classificationcited by 63
  13. 2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 59
  14. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
  15. 2019Graph Neural Network for Interpreting Task-fMRI Biomarkerscited by 40
  16. 2021Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosiscited by 20
  17. 2021Adaptive Kernel Graph Neural Networkcited by 15
  18. 2022Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learningcited by 15
  19. 2019PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functionscited by 9
  20. 2021Neuron Campaign for Initialization Guided by Information Bottleneck Theorycited by 9
  21. 2013Analyzing complex functional brain networks: fusing statistics and network science to understand the braincited by 8
  22. 2021A deep learning model for data-driven discovery of functional connectivitycited by 8
  23. 2018A hierarchical independent component analysis model for longitudinal Neuroimaging studiescited by 7
  24. 2017Bayesian Joint Modeling of Multiple Brain Functional Networkscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 23; 2024: 14; 2025: 30; 2026: 25.

94 papers.

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  1. 2024Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Maskingcited by 65
  2. 2025QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Trainingcited by 38
  3. 2023SwiFT: Swin 4D fMRI Transformercited by 36
  4. 2023NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomicscited by 33
  5. 2023AutoReP: Automatic ReLU Replacement for Fast Private Network Inferencecited by 29
  6. 2023Community-Aware Transformer for Autism Prediction in fMRI Connectomecited by 28
  7. 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inferencecited by 27
  8. 2024Population Transformer: Learning Population-level Representations of Neural Activitycited by 26
  9. 2023Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networkscited by 22
  10. 2025Principled Multimodal Representation Learningcited by 19
  11. 2025Towards a general-purpose foundation model for fMRI analysiscited by 15
  12. 2025MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decodingcited by 14
38 more
  1. 2025A Survey of Graph Transformers: Architectures, Theories and Applicationscited by 14
  2. 2022Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networkscited by 13
  3. 2023RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inferencecited by 12
  4. 2025Long-range Brain Graph Transformercited by 12
  5. 2023PTGB: Pre-Train Graph Neural Networks for Brain Network Analysiscited by 10
  6. 2023Learning High-Order Relationships of Brain Regionscited by 10
  7. 2025Biologically Plausible Brain Graph Transformercited by 10
  8. 2023BrainNPT: Pre-training of Transformer networks for brain network classificationcited by 9
  9. 2023R-Mixup: Riemannian Mixup for Biological Networkscited by 9
  10. 2024Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusioncited by 9
  11. 2025Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Helpcited by 9
  12. 2023Transformer-Based Hierarchical Clustering for Brain Network Analysiscited by 8
  13. 2023Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysiscited by 7
  14. 2023MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learningcited by 7
  15. 2025fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understandingcited by 7
  16. 2023QuMoS: A Framework for Preserving Security of Quantum Machine Learning Modelcited by 6
  17. 2023Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embeddingcited by 6
  18. 2024BRACTIVE: A Brain Activation Approach to Human Visual Brain Learningcited by 6
  19. 2024BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signalscited by 6
  20. 2024NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomescited by 6
  21. 2024Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusioncited by 6
  22. 2025Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Datacited by 6
  23. 2023Contrastive Graph Pooling for Explainable Classification of Brain Networkscited by 5
  24. 2023EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation Learningcited by 5
  25. 2025Scaling Vision Transformers for Functional MRI with Flat Mapscited by 5
  26. 2025Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Modelcited by 4
  27. 2023Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRIcited by 3
  28. 2024MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRIcited by 3
  29. 2025PTCMIL: Multiple Instance Learning via Prompt Token Clustering for Whole Slide Image Analysiscited by 3
  30. 2025RTGMFF: Enhanced fMRI-based Brain Disorder Diagnosis via ROI-driven Text Generation and Multimodal Feature Fusioncited by 3
  31. 2024Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learningcited by 2
  32. 2025Learning 3D Medical Image Models From Brain Functional Connectivity Network Supervision For Mental Disorder Diagnosiscited by 2
  33. 2025BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classificationcited by 2
  34. 2025Truth Neuronscited by 2
  35. 2026Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classificationcited by 2
  36. 2026Brain-OF: An Omnifunctional Foundation Model for fMRI, EEG and MEGcited by 2
  37. 2026Learning beyond Site Bias for OOD Generalization in Brain Networkscited by 2
  38. 2026Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaborationcited by 2

50 most cited of 94 All 94 (JSON)