Abstract #0270

Support Vector Machine Classification of FMRI Data in Image and K-Space Domains

Scott Peltier¹, Jonathan Lisinski², Douglas Noll, Stephen LaConte²

¹Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States; ²Computational Psychiatry Unit, Baylor College of Medicine, Houston, TX, United States

This work examines support vector machine (SVM) classification of complex fMRI data, both in the image domain and in the acquired k-space data. We achieve high classification accuracy using image magnitude, image phase, and k-space magnitude data. Additionally, we maintain high classification accuracy even when using only partial k-space data.