Dunedin Brain Imaging Study MRI Protocol
Cortical Thickness, Surface Area, and Volume
Image Acquisition
Each participant was scanned using a MAGNETOM Skyra (Siemens Healthcare GmbH) 3T scanner equipped with a 64-channel head/neck coil (due to size constraints, 7 participants were scanned with a 20-channel head/neck coil) at the Pacific Radiology Group imaging center in Dunedin, New Zealand. High resolution T1-weighted images were obtained using an MP-RAGE sequence 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. Additionally, a gradient echo field map was acquired with the following parameters: TR = 712 ms; TE = 4.92 and 7.38 ms; 72 axial slices; FOV = 200 mm; matrix = 100×100; slice thickness = 2.0 mm (voxel size 2 mm isotropic); and total scan time = 2 min and 25 s
Image Processing
Structural MRI data were analyzed using the Human Connectome Project (HCP) minimal preprocessing pipeline as extensively detailed elsewhere (Glasser et al., 2013). Briefly, T1-weighted and FLAIR images were processed through the PreFreeSurfer, FreeSurfer, and PostFreeSurfer pipelines. T1-weighted and FLAIR images were corrected for readout distortion using the gradient echo field map, coregistered, brain-extracted, and aligned together in the native T1 space using boundary-based registration (Greve & Fischl, 2009). Images were then processed with a custom FreeSurfer recon-all pipeline that is optimized for structural MRI with higher resolution than 1 mm isotropic. Finally, recon-all output were converted into CIFTI format and registered to common 32k_FS_LR mesh using MSM-sulc (Robinson et al., 2014).
For each subject the mean cortical thickness and surface area were then extracted from each of the 360 cortical areas in the HCP-MPP1.0 parcellation (Glasser et al., 2016). Subcortical volumes were extracted separately using the automatic segmentation (“aseg”) step of FreeSurfer version 6.0. FreeSurfer version 6.0 was used because the HCP FreeSurfer pipeline was optimized for the cortical surface, resulting in lower-quality segmentation of subcortical volumes in our dataset. Outputs of the minimal preprocessing pipeline were visually checked for accurate surface generation by examining each subject’s myelin map, pial surface, and white matter boundaries. Accuracy of subcortical segmentation was confirmed by visual inspection of the "aseg" labels overlaid on the volumes. 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), 9 due to missing FLAIR or field map scans, and 1 due to poor surface mapping yielding 861 datasets for analyses.
Statistical Analyses
All analyses were conducted in R version 3.4.1 (R Core Team, 2017). First, we ran a linear regression using [independent variable] to predict each of the global structural measures (total surface area, average cortical thickness, and total grey matter volume; ICCs for test-retest reliability = .996, .939, and .998 respectively). Next, we ran a linear regression using [independent variable] to predict each of the 360 regions comprising the parcellation scheme described above (Glasser et al., 2016; mean ICCs = .846 and .942 for parcel-wise cortical thickness and surface area, respectively). We corrected for multiple comparisons across the 360 tests performed using a false discovery rate (FDR) procedure (Benjamini & Hochberg, 1995). Finally, we used [independent variable] to predict the volume of 10 subcortical structures (mean ICC = .956). Sex was included as a covariate in all analyses.
Correspondence with Functional Connectivity Gradient
In follow-up analyses, we tested whether the strength of the parcel-wise associations between [independent variable] and cortical thickness were evenly distributed across the cortex or whether the strongest associations were overrepresented in heteromodal cortex (Mesulam 1998; Buckner & Dinichola 2019). Specifically we tested whether the beta's describing the parcelwise association between [independent variable] and cortical thickness corresponded with a cortical gradient described in Margulies et al., 2016. This gradient situates the default mode network as the apex of heteromodal cortex at one end of a spectrum and primary sensorimotor regions at the other. To test correspondence between the two maps, we first parcellated the connectivity gradient into the 360 HCP-MMP1.0 by taking the mean of each parcel. This parcellated gradient was then correlated with the parcel-wise standardized b’s for the association between [independent variable] and cortical thickness. To determine significance, we compared this value to a null distribution generated by spin permutation testing (Alexander-Bloch et al., 2018), in which each of the gradient and standardized b maps were randomly spherically rotated 1000 times and correlated with the other map. Results were considered significant at p < 0.05.
Also see Global covariate rationale.
References
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