Neuroimaging Acquisition and Analysis

MRI data were acquired at the UCSF Neuroscience Imaging Center using a Siemens Trio 3T scanner. The protocol includes a T1-weighted structural scan, acquired sagittally, with the following parameters: acquisition time = 8 minutes and 53 seconds; field of view = 160 × 240 × 256 mm with isotropic voxel resolution = 1 mm³; repetition time = 2300 ms; echo time = 2.98 ms; time inversion = 900 ms; flip angle = 9°. Fluid attenuated inversion recovery images were acquired for quantification of WMH (slice thickness = 1.00 mm; slices per slab = 160; in-plane resolution = 0.98 × 0.98 mm; matrix = 256 × 256; repetition time = 6000 ms; echo time = 388 ms; time inversion = 2100ms; flip angle = 120°).

T1-weighted images were visually inspected for quality prior to processing; images containing excessive artifact or motion were excluded. The N3 algorithm was used to adjust for magnetic field bias (24). Tissue segmentation was achieved via SPM12's unified segmentation procedure (25). A study-specific template was created with Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL) for warping of individual participant T1-weighted images (26). Nonlinear and rigid-body registration was implemented to normalize and modulate the images within the study-specific template space. An 8-mm full width half maximum Gaussian kernel was used for smoothing. Linear and nonlinear transformations between DARTEL's space and International Consortium of Brain Mapping (ICBM) space were performed to enable registration with a brain parcellation atlas. Volumetric quantification entailed transforming a standard parcellation atlas (27) into ICBM space and summing all gray matter within each parcellated region. Total intracranial volume (TIV) and total GMV (in L) were calculated in Montreal Neurological Institute (MNI) space.

WMHs were calculated (in mm³) using the fluid attenuated inversion recovery and T1-weighted images. After raw scans were visually inspected for quality, a fully automated segmentation process was implemented, which is based on a regression algorithm and uses Hidden Markov Random Field with Expectation Maximization Software (28,29). Resultant segmentations were all assessed manually for accuracy.