FDG-PET scans were performed using a 3.0T Biograph mMR (PET-MR) scanner (Siemens, Germany) and 3D T1-weighted magnetic resonance imaging was simultaneously performed with PET. Details of the acquisition were described previously (Byun et al., 2017 (link)).
The FDG-PET data were preprocessed using Statistical Parametric Mapping 12 (SPM12; Institute of Neurology, University College of London, United Kingdom) implemented in Matlab 2015ba (Mathworks, Natick, MA, USA). In the first step, static FDG-PET images were co-registered to individual T1 structural images. Next, transformation parameters were calculated from the individual T1 images that were coregistered to the MNI template image. The forward parameters were used to spatially normalize individual T1 and FDG-PET images to the MNI template. The spatially normalized FDG-PET images were smoothed with a 12-mm Gaussian filter and pons were used as the reference region for intensity normalized (Minoshima et al., 1995 (link)).
The FDG-PET data were preprocessed using Statistical Parametric Mapping 12 (SPM12; Institute of Neurology, University College of London, United Kingdom) implemented in Matlab 2015ba (Mathworks, Natick, MA, USA). In the first step, static FDG-PET images were co-registered to individual T1 structural images. Next, transformation parameters were calculated from the individual T1 images that were coregistered to the MNI template image. The forward parameters were used to spatially normalize individual T1 and FDG-PET images to the MNI template. The spatially normalized FDG-PET images were smoothed with a 12-mm Gaussian filter and pons were used as the reference region for intensity normalized (Minoshima et al., 1995 (link)).
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