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.
Cite
@article{arxiv.1611.08812,
title = {Kernel classification of connectomes based on earth mover's distance between graph spectra},
author = {Yulia Dodonova and Mikhail Belyaev and Anna Tkachev and Dmitry Petrov and Leonid Zhukov},
journal= {arXiv preprint arXiv:1611.08812},
year = {2016}
}
Comments
Presented at The MICCAI-BACON 16 Workshop (arXiv:1611.03363)