A Complex Data Method to Compute Fmri Activation
نویسنده
چکیده
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 and random noise. Nearly all fMRI studies derive functional “activation” based on magnitudeonly voxel time courses. Here the entire complex or bivariate data are modeled rather than just the magnitudeonly 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. The magnitude-only and complex time course models are applied to a real dataset.
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تاریخ انتشار 2010