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

TL;DR

This paper introduces a multi-view graph convolutional network that effectively fuses multi-modal neuroimaging data to improve Parkinson's Disease classification, outperforming traditional methods on the PPMI dataset.

Contribution

The paper presents a novel deep learning framework using GCNs for multi-modal brain image fusion in PD diagnosis, demonstrating superior predictive performance.

Findings

Achieved 0.9537 AUC on PPMI cohort

Outperformed PCA-based approaches significantly

Validated effectiveness of multi-view GCN in neuroimage analysis

Abstract

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 $0.9537\pm 0.0587$ AUC, compared with $0.6443\pm 0.0223$ AUC achieved by traditional approaches such as PCA.