Spatial registration of support vector machine models for multi-session and group real-time fMRI

INTRODUCTION We are developing a support vector machine (SVM)-based real-time (rt)-fMRI system similar to the one described in [1], but with the additional capability of multi-session and group-based SVM models. This will be critical for handling movement between runs within a session, progressive training and testing across sessions, and the use of group models to affect rehabilitation/therapy for applications such as addiction and stroke by reinforcing desired compensatory or normative multi-voxel pattern targets built from databases of recovered individuals. This requires that the SVM model and the test data be spatially aligned, and here we investigate alignment strategies to verify the potential trade-off between classification accuracy and rt-fMRI computational demands. We investigate this tradeoff using two tasks that are unlikely to elicit learning effects across repeated runs or sessions, a bimanual button tapping task and a multi-source interference task. These tasks were also chosen to provide both high and modest (above chance) prediction accuracies, respectively. Finally, to evaluate group models, we tested a nine-subject model based on data collected across two 3T scanner platforms.