Kernel classification of connectomes based on earth mover's distance between graph spectra

• DOI: 10.48550/arxiv.1611.08812
• Publication Date: 2016-01-01
• Authors: Dodonova, Yulia; Belyaev, Mikhail; Tkachev, Anna; Petrov, Dmitry; Zhukov, Leonid
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; 0302 clinical medicine; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; Computer Science - Neural and Evolutionary Computing; Neural and Evolutionary Computing (cs.NE)

In this paper, we tackle a problem of predicting phenotypes from structural connectomes. We propose that normalized Laplacian spectra can capture structural properties of brain networks, and hence graph spectral distributions are useful for a task of connectome-based classification. We introduce a kernel that is based on earth mover's distance (EMD) between spectral distributions of brain networks. We access performance of an SVM classifier with the proposed kernel for a task of classification of autism spectrum disorder versus typical development based on a publicly available dataset. Classification quality (area under the ROC-curve) obtained with the EMD-based kernel on spectral distributions is 0.71, which is higher than that based on simpler graph embedding methods.<br/>Presented at The MICCAI-BACON 16 Workshop (arXiv:1611.03363)<br/>