Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome

In this paper, we propose a new method to detect differences at the group level in brain images based on spatially regularized support vector machines (SVM). We propose to spatially regularize the SVM using a graph Laplacian. This provides a flexible approach to model different types of proximity between voxels. We propose a proximity graph which accounts for tissue types. An efficient computation of the Gram matrix is provided. Then, significant differences between two populations are detected using statistical tests on the outputs of the SVM. The method was first tested on synthetic examples. It was then applied to 72 stroke patients to detect brain areas associated with motor outcome at 90 days, based on diffusion-weighted images acquired at the acute stage (median delay one day). The proposed method showed that poor motor outcome is associated to changes in the corticospinal bundle and white matter tracts originating from the premotor cortex. Standard mass univariate analyses failed to detect any difference on the same population.

Spatial regularization of SVM using a proximity graph built from tissue types. When used to detect group differences, it showed that poor motor outcome is associated with changes mainly located in the corticospinal bundle.Figure optionsDownload high-quality image (262 K)Download as PowerPoint slideHighlights
► New method to detect group differences in brain images with spatially regularized SVM.
► Regularization based on the Laplacian of a proximity graph built from tissue types.
► Regions linked with motor outcome detected in 72 stroke patients from acute stage MRI.
► Regions located on: corticospinal tract and tracts coming from the premotor cortex.