title: A complex way to compute fMRI activation.
abstract: In functional magnetic resonance imaging, voxel time courses after Fourier or non-Fourier "image reconstruction" are complex valued as a result of phase imperfections due to magnetic field inhomogeneities. Nearly all fMRI studies derive functional "activation" based on magnitude voxel time courses [Bandettini, P., Jesmanowicz, A., Wong, E., Hyde, J.S., 1993. Processing strategies for time-course data sets in functional MRI of the human brain. Magn. Reson. Med. 30 (2): 161-173 and Cox, R.W., Jesmanowicz, A., Hyde, J.S., 1995. Real-time functional magnetic resonance imaging. Magn. Reson. Med. 33 (2): 230-236]. Here, we propose to directly model the entire complex or bivariate data rather than just the magnitude-only data. A nonlinear multiple regression model is used to model activation of the complex signal, and a likelihood ratio test is derived to determine activation in each voxel. We investigate the performance of the model on a real dataset, then compare the magnitude-only and complex models under varying signal-to-noise ratios in a simulation study with varying activation contrast effects.
authors.affiliation: Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA. dbrowe@mcw.edu
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Rowe
authors.forename: Brent R
authors.initials: BR
authors.lastname: Logan
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D004525:Efferent Pathways / Q000502:physiology; D005385:Fingers / Q000502:physiology; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009044:Motor Cortex / Q000502:physiology; D017711:Nonlinear Dynamics