# Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

arXiv: https://arxiv.org/abs/2107.03220 · HTML: https://mlsanity.com/paper/2107.03220/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [GPT-GNN: Generative Pre-Training of Graph Neural Networks](https://mlsanity.com/paper/2006.15437/) (2020)
- [Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://mlsanity.com/paper/1710.02971/) (2017)
- [Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease](https://mlsanity.com/paper/1805.08801/) (2018)
- [A simple yet effective baseline for non-attributed graph classification](https://mlsanity.com/paper/1811.03508/) (2018)
- [Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis](https://mlsanity.com/paper/1806.07703/) (2018)
- [Multi-view Graph Embedding with Hub Detection for Brain Network Analysis](https://mlsanity.com/paper/1709.03659/) (2017)
- [Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning](https://mlsanity.com/paper/1711.02703/) (2017)

## Built on by (most cited first, 20 of 20)

- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks](https://mlsanity.com/paper/2204.07054/) (2022)
- [Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis](https://mlsanity.com/paper/2207.00813/) (2022)
- [Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning](https://mlsanity.com/paper/2206.04486/) (2022)
- [BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis](https://mlsanity.com/paper/2107.05097/) (2021)
- [Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks](https://mlsanity.com/paper/2211.00261/) (2022)
- [Graph Neural Networks for Brain Graph Learning: A Survey](https://mlsanity.com/paper/2406.02594/) (2024)
- [PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis](https://mlsanity.com/paper/2305.14376/) (2023)
- [Transformer-Based Hierarchical Clustering for Brain Network Analysis](https://mlsanity.com/paper/2305.04142/) (2023)
- [Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis](https://mlsanity.com/paper/2309.01941/) (2023)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling](https://mlsanity.com/paper/2505.15135/) (2025)
- [Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis](https://mlsanity.com/paper/2505.15139/) (2025)
- [NeuroKoop: Neural Koopman Fusion of Structural-Functional Connectomes for Identifying Prenatal Drug Exposure in Adolescents](https://mlsanity.com/paper/2508.16414/) (2025)
- [Spatial Craving Patterns in Marijuana Users: Insights from fMRI Brain Connectivity Analysis with High-Order Graph Attention Neural Networks](https://mlsanity.com/paper/2403.00033/) (2024)
- [KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure](https://mlsanity.com/paper/2601.11018/) (2026)
- [Adaptive Critical Subgraph Mining for Cognitive Impairment Conversion Prediction with T1-MRI-based Brain Network](https://mlsanity.com/paper/2403.13338/) (2024)
- [Self-supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis towards Improving Autism Detection](https://mlsanity.com/paper/2501.16346/) (2025)
- [Learning Structural-Functional Brain Representations through Multi-Scale Adaptive Graph Attention for Cognitive Insight](https://mlsanity.com/paper/2603.29967/) (2026)
- [Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis](https://mlsanity.com/paper/2605.13312/) (2026)

## Abstract (arXiv)

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. However, how to employ GNNs to extract effective representations from brain networks in multiple modalities remains rarely explored. Moreover, as brain networks provide no initial node features, how to design informative node attributes and leverage edge weights for GNNs to learn is left unsolved. To this end, we develop a novel multiview GNN for multimodal brain networks. In particular, we regard each modality as a view for brain networks and employ contrastive learning for multimodal fusion. Then, we propose a GNN model which takes advantage of the message passing scheme by propagating messages based on degree statistics and brain region connectivities. Extensive experiments on two real-world disease datasets (HIV and Bipolar) demonstrate the effectiveness of our proposed method over state-of-the-art baselines.