Neuroimaging data were prospectively acquired with a Siemens 3T Verio scanner using an 8-channel head coil. For gray and white matter analyses we acquired 2 T1-weighted scans (176 contiguous sagittal slices, 1mm3 voxels, 256×256 matrix, TR/TE= 2500/3.77ms, 7/8 Partial Fourier, acquisition time 9:22). The first of the two T1 scans was analyzed for this analysis. For skull segmentation, T2-weighted images were acquired (176 contiguous sagittal slices, 1mm3 voxels, 256×256 matrix, TR/ TE= 3200/ 409ms, GRAPPA, acquisition time 4:43). Images were visually examined for excessive motion, and images showing more than a moderate degree of motion were excluded from the analyses.
For all participants in the first analysis (PD vs control), TICV was measured by exporting the inner surface of the skull from FSL version 4.1 Brain Extraction Tool (BET; Smith 2002 (link)). These initial intracranial masks were then manually edited by expert raters to fill the enclosure within the inner surface of the skull. The inferior portion of the mask terminated on a line between the bottom of the occipital bone and the clivus. Reliability was high (intra-rater and inter-rater reliability DSC > 0.99). The final variable of interest was TICV in mm3. For participants added for the large vs small TICV analysis, TICV was measured using an automated method from Freesurfer (Fischl et al 2002 (link)), which combines all voxels labeled as white matter, gray matter, and CSF to create a volume that represents TICV.
A VBM analysis was performed on T1 structural images to investigate voxel-wise grey matter changes between PD patients and control participants. Structural data was analyzed with FSL-VBM (Douaud et al. 2007 (link)), an optimized VBM protocol (Good et al. 2001 (link)) carried out with FSL tools (Smith et al. 2004 (link)). First, structural images were brain-extracted and grey matter-segmented before being registered to the MNI 152 standard space using non-linear registration (Andersson et al. 2007 ). The resulting images were averaged and flipped along the x-axis to create a left-right symmetric, study-specific grey matter template. Second, all native grey matter images were non-linearly registered to this study-specific template and "modulated" to correct for local expansion (or contraction) due to the non-linear component of the spatial transformation. The modulated grey matter images were then smoothed with an isotropic Gaussian kernel with a sigma of 3 mm.
For all participants in the first analysis (PD vs control), TICV was measured by exporting the inner surface of the skull from FSL version 4.1 Brain Extraction Tool (BET; Smith 2002 (link)). These initial intracranial masks were then manually edited by expert raters to fill the enclosure within the inner surface of the skull. The inferior portion of the mask terminated on a line between the bottom of the occipital bone and the clivus. Reliability was high (intra-rater and inter-rater reliability DSC > 0.99). The final variable of interest was TICV in mm3. For participants added for the large vs small TICV analysis, TICV was measured using an automated method from Freesurfer (Fischl et al 2002 (link)), which combines all voxels labeled as white matter, gray matter, and CSF to create a volume that represents TICV.
A VBM analysis was performed on T1 structural images to investigate voxel-wise grey matter changes between PD patients and control participants. Structural data was analyzed with FSL-VBM (Douaud et al. 2007 (link)), an optimized VBM protocol (Good et al. 2001 (link)) carried out with FSL tools (Smith et al. 2004 (link)). First, structural images were brain-extracted and grey matter-segmented before being registered to the MNI 152 standard space using non-linear registration (Andersson et al. 2007 ). The resulting images were averaged and flipped along the x-axis to create a left-right symmetric, study-specific grey matter template. Second, all native grey matter images were non-linearly registered to this study-specific template and "modulated" to correct for local expansion (or contraction) due to the non-linear component of the spatial transformation. The modulated grey matter images were then smoothed with an isotropic Gaussian kernel with a sigma of 3 mm.
