MRI process and analysis were performed as previously described.
23 (link) For VBM analysis, all mice 3D T2‐WI images were preprocessed using SPM12 software (Wellcome Department of Cognitive Neurobiology, University College of London, UK), which included linear registration, segmentation, normalization, Jacobian modulation, and smoothing.
For rs‐fMRI analysis, we first discarded 10 volumes of fMRIs. After correcting the slice timing and head motif for the rest of the volumes, they were spatially normalized to the template established by 3D T2‐WI images. Then, to smooth the images, an 8‐mm full width at half maximum (FWHM) Gaussian kernel was used. Signal regression and motion vectors were used to correct for systemic noise. Next, the seed region of interest (ROI) was obtained from the VBM results. Second, the resting‐state functional connectivity map between the PFC and the reward system was calculated using the Pearson correlation coefficient. Fisher Z‐transformation was performed to increase the normality of the functional connectivity data. After statistical analysis of functional connectivity values using GraphPad Prism 8 (La Jolla, CA, USA), t‐values reflecting the differences in functional connectivity were converted into a matrix using MATLAB software (The MathWorks Inc).
23 (link) For VBM analysis, all mice 3D T2‐WI images were preprocessed using SPM12 software (Wellcome Department of Cognitive Neurobiology, University College of London, UK), which included linear registration, segmentation, normalization, Jacobian modulation, and smoothing.
For rs‐fMRI analysis, we first discarded 10 volumes of fMRIs. After correcting the slice timing and head motif for the rest of the volumes, they were spatially normalized to the template established by 3D T2‐WI images. Then, to smooth the images, an 8‐mm full width at half maximum (FWHM) Gaussian kernel was used. Signal regression and motion vectors were used to correct for systemic noise. Next, the seed region of interest (ROI) was obtained from the VBM results. Second, the resting‐state functional connectivity map between the PFC and the reward system was calculated using the Pearson correlation coefficient. Fisher Z‐transformation was performed to increase the normality of the functional connectivity data. After statistical analysis of functional connectivity values using GraphPad Prism 8 (La Jolla, CA, USA), t‐values reflecting the differences in functional connectivity were converted into a matrix using MATLAB software (The MathWorks Inc).
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