Optimally-discriminative voxel-based analysis

Gaussian smoothing of images is an important step in Voxel-based Analysis and Statistical Parametric Mapping (VBA-SPM); it accounts for registration errors and integrates imaging signals from a region around each voxel being analyzed. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically, non-optimally, and lacks spatial adaptivity to the shape and spatial extent of the region of interest. In this paper, we propose a new framework, named Optimally-Discriminative Voxel-Based Analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, Nonnegative Discriminative Projection is applied locally to get the direction that best discriminates between two groups, e.g. patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Permutation tests are finally used to obtain the statistical significance. The experiments on Mild Cognitive Impairment (MCI) study have shown the effectiveness of the framework.