AUTHOR=Sacchet Matthew D. , Prasad Gautam , Foland-Ross Lara C. , Thompson Paul M. , Gotlib Ian H. TITLE=Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory JOURNAL=Frontiers in Psychiatry VOLUME=6 YEAR=2015 URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2015.00021 DOI=10.3389/fpsyt.2015.00021 ISSN=1664-0640 ABSTRACT=
Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right