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

Xi Zhang, Lifang He, Kun Chen, Yuan Luo, Jiayu Zhou, Fei Wang

Research output: Contribution to journalArticle

2 Scopus citations

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±0.0587 AUC, compared with 0.6443±0.0223 AUC achieved by traditional approaches such as PCA.

Original languageEnglish (US)
Pages (from-to)1147-1156
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2018
StatePublished - 2018

ASJC Scopus subject areas

  • Medicine(all)

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