Multi-View Brain Network Analysis with Cross-View Missing Network Generation

Authors

Gongxu Luo, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Chenyang Li, University of Waterloo
Hejie Cui, Emory University
Lichao Sun, Lehigh University
Lifang He, Lehigh University
Carl Yang, Emory University

Document Type

Conference Proceeding

Publication Title

Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Abstract

Parkinson's Disease (PD), one of the most common neurological disorders, has long been a challenge in public health clinical diagnosis as well as scientific understanding. Recently, there has been an upsurge of interest in brain network analysis which benefits the understanding of brain functions and early detection of neurological disorders extensively. Multi-view brain networks with different connectivity patterns among regions of interests (ROIs) can be constructed to reflect different and complementary perspectives of the brain connectivity profile. However, the extraction of such multi-view brain networks relies on the availability of multiple neuroimaging modalities and heavy data preprocessing, which often leads to severe missing data in either view. The cross-view missing issue hinders the pragmaticality of multi-view representation learning and downstream analysis. In this work, we formulate the novel problem of cross-view brain network generation and propose CroGen, a graph generative model that can generate the missing view when only one view is given. Specifically, GroGen leverages the potential correlation between diverse views of brain networks of the same individuals. Moreover, we design a pre-train schema to make full use of the labeled individuals with only single views of brain networks. Extensive experiments on real-life Parkinson's Progression Markers Initiative (PPMI) cohort demonstrate the supreme effectiveness of CroGen over baselines on both cross-view generation tasks and downstream PD classification.

First Page

108

Last Page

115

DOI

10.1109/BIBM55620.2022.9995283

Publication Date

12-6-2022

Keywords

brain network-based disease classification, cross-view network generation, multi-view network analysis

Comments

IR conditions: non-described

Recommended Citation

G. Luo, C. Li, H. Cui, L. Sun, L. He and C. Yang, "Multi-View Brain Network Analysis with Cross-View Missing Network Generation," 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Las Vegas, NV, USA, 2022, pp. 108-115, doi: 10.1109/BIBM55620.2022.9995283.

Additional Links

https://doi.org/10.1109/BIBM55620.2022.9995283