MRI
REGRESSION, SENSITIVITY
On the afternoon following the morning PET scanning, high-resolution anatomical brain MRI was acquired with a HDx General Electric 3 Tesla scanner (echo time [TE] = 2.7ms, repetition time [TR] = 7.24 ms, flip angle 12°, voxel size 0.937 Under each diet condition, all subjects were scanned at 18:00, 4.5 hours after a standardized, diet-appropriate meal. Functional and structural imaging was performed on a 3T General Electric scanner and a GE 8-channel receive-only head coil. High-resolution anatomical images were collected prior to functional scanning runs (TE = 2.7 ms, TR = 7.24 ms, flip angle: 12°, voxel size: 0.937 × 0.937 × 1.2 mm). For the functional scans, 206 magnetic resonance (MR) volumes were acquired. Each echoplanar image (EPI) consisted of 44 2.8 mm slices (TE = 27 ms, TR = 2,500 ms, flip angle = 90°, voxel size = 3.4375 × 3.4375 × 2.8 mm). All structural and functional images were collected with a Sensitivity Encoding (SENSE) factor of 2 used to reduce image collection time (for structural images) or minimize image distortions (in functional images) while reducing gradient coil heating over the course of the scan session.The fMRI task is described in detail elsewhere (Analyses of functional neuroimaging were performed in AFNI (AFNI_20.2.00 ‘Aulus Vitellius’). Each individual’s anatomical MRI was transformed into the Talairach space, and the transformation matrix was applied to the functional data during preprocessing. All functional volumes were aligned to a common base EPI represented by the third volume of the first functional run. The first 3 volumes of each EPI run were trimmed to allow the fMRI signal to reach steady state. A slice-time correction was applied to all functional volumes, which were also smoothed with a 6 mm full-width half-max Gaussian kernel. Additionally, the signal value for each EPI volume was normalized to the percent signal change from the voxel’s mean signal across the time course.Individual subject data were checked for quality assurance, and outlying time points resulting from head motion were censored from the analyses. At the individual level, multiple regression was used to analyze the data, with regressors of noninterest included in the model to account for each run’s signal mean, linear, quadratic, and cubic signal trends, as well as 6 motion parameters (3 translations and 3 rotations) saved from the image registration step during preprocessing. The food pleasantness task regressor was constructed by convolving a box-car function with a width of 5 seconds beginning at the onset of the food image with a gamma-variate function to adjust the predictor variable for the delay and shape of the BOLD response. Given similarities in pleasantness ratings across diet conditions (