Discovering brain regions relevant to obsessive-compulsive disorder identification through bagging and transduction

In the present study we applied a multivariate feature selection method based on the analysis of the sign consistency of voxel weights across bagged linear Support Vector Machines (SVMs) with the aim of detecting brain regions relevant for the discrimination of subjects with obsessive-compulsive disorder (OCD, n=86) from healthy controls (n=86). Each participant underwent a structural magnetic resonance imaging (sMRI) examination that was pre-processed in Statistical Parametric Mapping (SPM8) using the standard pipeline of voxel-based morphometry (VBM) studies. Subsequently, we applied our multivariate feature selection algorithm, which also included an L2 norm regularization to account for the clustering nature of MRI data, and a transduction-based refinement to further control overfitting. Our approach proved to be superior to two state-of-the-art feature selection methods (i.e., mass-univariate t-Test selection and recursive feature elimination), since, following the application of transductive refinement, we obtained a lower test error rate of the final classifier. Importantly, the regions identified by our method have been previously reported to be altered in OCD patients in studies using traditional brain morphometry methods. By contrast, the discrimination patterns obtained with the t-Test and the recursive feature elimination approaches extended across fewer brain regions and included fewer voxels per cluster. These findings suggest that the feature selection method presented here provides a more comprehensive characterization of the disorder, thus yielding not only a superior identification of OCD patients on the basis of their brain anatomy, but also a discrimination map that incorporates most of the alterations previously described to be associated with the disorder.