MRI data processing and analysis
POSTERIOR
Longitudinal analyses were performed using the Computational Anatomy Toolbox (CAT r1932), implemented in MATLAB 9.6 (MathWorks, Inc., Natick, MA, USA) and Statistical Parametric Mapping (SPM12 v7771). Firstly, all the images were automatically reoriented (center of mass) and intra-subject co-registration was computed between all the images of the three time points. Next, the images were skull stripped, realigned across all subjects, and then bias corrected with regard to the mean image. This mean image was computed from all three time points for each subject separately. Next, all the images, including the mean images, were segmented with the Spatial-Adaptive Maximum A Posterior (AMAP) approach to accurately classify the three tissue types: GM, white matter, and cerebrospinal fluid. Tissue segments were then spatially normalized into the MNI (Montreal Neurological Institute) space employing DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra) and the geodesic shooting algorithm. Next, the modulation of the normalized tissue segments was computed for each participant based on the Jacobian determinant. This is an essential step to account for local warping and global affine transformation. Finally, the modulated data were smoothed with a 9 mm full-width at half-maximum (FWHM) smoothing kernel. In addition, the surface-based metrics were estimated using the fully automated pipeline in CAT12. The project-based thickness (PBT) approach was used for the cortical thickness and central surface estimation. The PBT approach includes topology correction, spherical mapping, and spherical registration. Parameters for the cortical thickness and folding (cortical depth) were extracted for each subject. Finally, we computed the weighted overall image quality index for all the participants and time points (mean of 2.02 ± 0.121) to ensure a sufficient data quality.