Deep Graph Similarity Learning for Brain Data Analysis

被引:30
|
作者
Ma, Guixiang [1 ]
Ahmed, Nesreen K. [1 ]
Willke, Theodore L. [1 ]
Sengupta, Dipanjan [1 ]
Cole, Michael W. [2 ]
Turk-Browne, Nicholas B. [3 ]
Yu, Philip S. [4 ]
机构
[1] Intel Labs, Santa Clara, CA 95054 USA
[2] Rutgers State Univ, New Brunswick, NJ USA
[3] Yale Univ, New Haven, CT 06520 USA
[4] Univ Illinois, Chicago, IL 60680 USA
来源
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) | 2019年
关键词
Metric learning; Graph Neural Networks; Graph Convolutional Networks; Higher-order Networks; Brain Networks; Functional Connectivity; Graph Similarity; Structural Similarity; Community Embedding; Graph Matching; Community-Preserving Embedding; STATE FUNCTIONAL CONNECTIVITY; ORGANIZATION; NETWORKS;
D O I
10.1145/3357384.3357815
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
We propose an end-to-end graph similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis. The proposed framework learns the brain network representations via a supervised metric-based approach with siamese neural networks using two graph convolutional networks as the twin networks. Our proposed framework performs higher-order convolutions by incorporating higher-order proximity in graph convolutional networks to characterize and learn the community structure in brain connectivity networks. To the best of our knowledge, this is the first community-preserving graph similarity learning framework for multi-subject brain network analysis. Experimental results on four real fMRI datasets demonstrate the potential use cases of the proposed framework for multi-subject brain analysis in health and neuropsychiatric disorders. Our proposed approach achieves an average AUC gain of 75% compared to PCA, an average AUC gain of 65.5% compared to Spectral Embedding, and an average AUC gain of 24.3% compared to S-GCN across the four datasets, indicating promising applications in clinical investigation and brain disease diagnosis.
引用
收藏
页码:2743 / 2751
页数:9
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