Dunedin Brain Imaging Study MRI Protocol
White Matter Hyperintensities
T1-weighted MP-RAGE and 3D FLAIR images were acquired from each participant using a Siemens Skyra 3T equipped with a 64-channel head/neck coil (due to size constraints, 7 participants were scanned with a 20-channel head/neck coil) with the following parameters: TR = 2400 ms; TE = 1.98 ms; 208 sagittal slices; flip angle, 9°; FOV, 224 mm; matrix =256×256; slice thickness = 0.9 mm with no gap (voxel size 0.9×0.875×0.875 mm); and total scan time = 6 min and 52 s. 3D fluid-attenuated inversion recovery (FLAIR) images were obtained with the following parameters: TR = 8000 ms; TE = 399 ms; 160 sagittal slices; FOV = 240 mm; matrix = 232×256; slice thickness = 1.2 mm (voxel size 1.2×0.9×0.9 mm); and total scan time = 5 min and 38 s.
To identify and extract the total volume of WMHs, T1-weighted and FLAIR images for each participant were processed with UBO Detector, a cluster-based, fully-automated pipeline with high reliability in our data (test-retest ICC = 0.87, 95% CI = [.73, .95]) and out of sample performance (Jiang 2018). The resulting WMH probability maps were thresholded at 0.7, which is the suggested standard. WMH volume is measured in Montreal Neurological Institute (MNI) space, removing the influence of differences in brain volume and intracranial volume on WMH volume. Because of the potential for bias and false positives due to the thresholds and masks applied in UBO, the resulting WMH maps for each participant were manually checked by two independent raters to ensure that false detections did not substantially contribute to estimates of WMH volume. Visual inspections were done blind to the participants’ cognitive status. Due to the tendency of automated algorithms to mislabel regions surrounding the septum as white matter hyperintensities, these regions were manually masked out, to further ensure the most accurate grading possible. Of the 875 Study members for whom data were available, 4 were excluded due to major incidental findings or previous injuries (e.g., large tumors or extensive damage to the brain/skull), 8 due to missing FLAIR scans, 3 due to diagnosis with Multiple Sclerosis, and 8 due to inaccurate white matter labelling or low-quality MRI data yielding 852 datasets for analyses.
References
Jiang, J., Liu, T., Zhu, W., Koncz, R., Liu, H., Lee, T., Sachdev, P.S., Wen, W. UBO Detector – A cluster-based, fully automated pipeline for extracting white matter hyperintensities. NeuroImage, doi.org/10.1016/j.neuroimage.2018.03.050 (2018).