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.