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Abstract #1150

Spatial Registration of Support Vector Machine Models for Multi-Session and Group Real-Time FMRI

Andrew Fischer1, Jonathan Lisinski2, Pearl Chiu2, Brooks King-Casas2, Stephen LaConte2

1Rice University, Houston, TX, United States; 2Neuroscience, Baylor College of Medicine, Houston, TX, United States

A pattern-based rt-fMRI system capable of multi-session and group-based models enables progressive training and testing across sessions, and potentially enables the use of group models for rehabilitation/therapy using multi-voxel targets built from databases of recovered individuals. Here we investigate alignment strategies to verify that there is not a significant tradeoff between classification accuracy and rt-fMRI computational demands. Our results demonstrate the feasibility of a model-to-scan alignment system for real-time fMRI in which the least demanding computational approach does not lead to a compromise of classification accuracy. This work also demonstrates the feasibility of using group SVM models in real-time experiments.