# Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease

arXiv: https://arxiv.org/abs/1805.08801 · HTML: https://mlsanity.com/paper/1805.08801/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

## Built on by (most cited first, 19 of 19)

- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis](https://mlsanity.com/paper/2107.03220/) (2021)
- [Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning](https://mlsanity.com/paper/2206.04486/) (2022)
- [Deep reinforcement learning guided graph neural networks for brain network analysis](https://mlsanity.com/paper/2203.10093/) (2022)
- [Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2204.13188/) (2022)
- [Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction](https://mlsanity.com/paper/2101.08316/) (2021)
- [Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome](https://mlsanity.com/paper/2211.07363/) (2022)
- [Multiplex Graph Networks for Multimodal Brain Network Analysis](https://mlsanity.com/paper/2108.00158/) (2021)
- [Learning to Model the Relationship Between Brain Structural and Functional Connectomes](https://mlsanity.com/paper/2112.09906/) (2021)
- [Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images](https://mlsanity.com/paper/2308.16863/) (2023)
- [Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding](https://mlsanity.com/paper/2312.16834/) (2023)
- [A Re-node Self-training Approach for Deep Graph-based Semi-supervised Classification on Multi-view Image Data](https://mlsanity.com/paper/2510.24791/) (2025)
- [Structure-Preserving Graph Kernel for Brain Network Classification](https://mlsanity.com/paper/2111.10803/) (2021)
- [Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI](https://mlsanity.com/paper/2212.12810/) (2022)
- [Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI](https://mlsanity.com/paper/2306.09629/) (2023)
- [GAMMA-PD: Graph-based Analysis of Multi-Modal Motor Impairment Assessments in Parkinson's Disease](https://mlsanity.com/paper/2410.00944/) (2024)
- [Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis](https://mlsanity.com/paper/2605.13312/) (2026)

## Abstract (arXiv)

Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. Neuroimaging, as another important information source for neurodegenerative disease, has also arisen considerable interests from the PD community. In this paper, we propose a deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images in relationship prediction which is useful for distinguishing PD cases from controls. On Parkinson's Progression Markers Initiative (PPMI) cohort, our approach achieved 0.9537± 0.0587 AUC, compared with 0.6443± 0.0223 AUC achieved by traditional approaches such as PCA.