The diffeomorphic anatomical registration through the exponentiated lie algebra (DARTEL) toolbox was applied to all participants' GM and WM to refine the inter-subject registration.
We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images.
For inter-subject registration, these affine-aligned GM and white matter images were warped to a study-specific tissue template constructed from all study participants using a diffeomorphic non-linear image registration algorithm [26].
The anatomy scan was used for inter-subject registration and visualization of the results.
However, in neurodegenerative disorders single voxels are unlikely to contain generalizable information due to a limited across-subject registration of MR and FDG-PET images.
For this, intra-subject registration was first performed using Automated Image Registration (AIR) [50] to remove linear transformation (rotation and translation) between the 35 diffusion weighted images and T2 weighted image.
This procedure resulted in an accurate anatomical overlap of both imaging modalities and in an accurate between-subject registration, with both images having the same voxel size and approximately the same effective smoothness.
To account for anatomical differences and any small errors in inter-subject registration that could masquerade as stable functional effects, the voxelwise analyses were co-varied for the probability of gray-matter at each voxel in the brain [14].
A whole-brain three-dimensional fast field echo T1-weighted scan (150 slices; repetition time = 8.4 ms; echo time = 3.8 ms; flip angle = 8°; field of view, 288×252×185 mm; voxel size: 1 mm isotropic) was acquired for within-subject registration purposes.
