1. A method of classifying a test subject comprising: for each of a plurality of training subjects collecting imaging data describing an observed image attribute associated with each voxel of a first volume of interest; for each of said plurality of training subjects collecting imaging data describing a feature of an observed image morphometry attribute associated with a second volume of interest; constructing a statistical model based on each of said training subjects as a function of both said imaging data describing an observed image attribute associated with each voxel of said first volume of interest and said imaging data describing a feature of said observed image morphometry attribute associated with said second volume of interest for said each training subject; for each of a plurality of control subjects at least some of which are known to have a condition, collecting imaging data describing an observed image attribute associated with each voxel of said first volume of interest; for each of said plurality of control subjects, collecting imaging data describing a feature of said observed image morphometry attribute associated with said second volume of interest; for each of said control subjects fitting said imaging data describing an observed image attribute associated with each voxel of said first volume of interest to said statistical model, wherein said statistical model is built to contain data characterizing subjects involving a linear registration of voxels in said first volume of interest; for each of said control subjects fitting said imaging data describing a feature of said observed image morphometry attribute associated with said second volume of interest to said statistical model, wherein said statistical model is built to contain data characterizing subjects involving a non-linear registration of voxels in said second volume of interest; for said test subject collecting imaging data describing an observed image attribute associated with each voxel of said first volume of interest; for said test subject, collecting imaging data describing a feature of said observed image morphometry attribute associated with said second volume of interest; classifying, in a computer, said test subject as having or not having said condition, based on a fit, involving a linear registration of voxels in said first volume of interest, of said imaging data describing an observed image attribute associated with each voxel of said first volume of interest for said test subject and based on a fit, involving a non-linear registration of voxels in said second volume of interest, of said imaging data describing a feature of said observed image morphometry attribute associated with said second volume of interest for said test subject, and said fitting of said imaging data for each of said control subjects, to said statistical model.