Center for Biomedical Image Computing and Analytics SBIA License Yes University of Pennsylvania NITRC CBICA: ODVBA C++ Alexander Getka ODVBA provides a mathematically rigorous framework for determining the optimal spatial smoothing of structural and functional images, prior to applying voxel-based group analysis. In order to determine the optimal smoothing kernel, a local discriminative analysis, restricted by appropriate nonnegativity constraints, is applied to a spatial neighborhood around each voxel, aiming to find the direction best highlights the difference between two groups in that neighborhood. Since each voxel belongs to a large number of such neighborhoods, each centered on one of its neighboring voxels, the group difference at each voxel is determined by a composition of all these optimal smoothing directions. Permutation tests are used to obtain the statistical significance of the resulting Optimally-Discriminative VBM (ODVBA) maps. 2017-2-20 3.0.0 CBICA: ODVBA Clinical Neuroinformatics, MR, SBIA License, C++