Preprocessing of Gray Matter Density Maps for Neuroimaging Analysis

The downloaded data were first converted from DICOM to Neuroimaging Informatics Technology Initiative (NIFTI) format, by using MRIcron software (http://people.cas.sc.edu/rorden/mricron/index.html (accessed on 1 January 2022)). Images were manually reoriented to place their native-space origin at the anterior commissure. Images were then preprocessed by using the Computational Anatomy Toolbox (CAT12) toolbox (http://www.neuro.uni-jena.de/cat/ (accessed on 1 January 2022)), an extended toolbox of SPM12 [27 (link)] with default settings. The preprocessing pipeline included realignment, skull stripping, segmentation into gray matter and white matter, and finally, the segmented gray matter images were spatially normalized into the Montreal Neurological Institute (MNI) space by using diffeomorphic anatomical registration by using exponentiated Lie algebra nonlinear normalization and modulated to preserve volume information. The modulated and warped 3D gray matter density maps (GMDMs) were smoothed by using a 3D Gaussian kernel of 2 mm full width at half maximum. The GMDMs had a dimensionality of 121 × 145 × 121 in the voxel space, with the voxel size of 1.5 × 1.5 × 1.5 mm³. The background voxels increased the computational complexity of model, but they did not contribute to the classification performance. Thus, we established a new bounding box with the dimension of 91 × 115 × 91 (voxel size of 1.5 × 1.5 × 1.5 mm³), which removed most of the background voxels. The complete preprocessing pipeline is summarized in Figure 1.