Dunedin Brain Imaging Study MRI Protocol
Resting State
BOLD fMRI Data 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. A series of 72 interleaved axial T2-weighted functional slices were acquired using a 3-fold multi-band accelerated echo planar imaging sequence with the following parameters: TR = 2000 ms, TE = 27 msec, flip angle = 90°, field-of-view = 200 mm, voxel size = 2mm isotropic, slice thickness = 2 mm without gap. Participants were shown a blank gray screen and instructed to remain awake, with their eyes open during the 8:16 resting state scan.
BOLD fMRI Data Pre-Processing
Anatomical images for each subject were skull-stripped, intensity-normalized, and nonlinearly warped to a study-specific average template in a standard stereotactic space (Montreal Neurological Institute template) using ANTs (Klein et al., 2009). BOLD time series for each subject were processed in AFNI (Cox, 1996). Images for each subject were despiked, slice-time-corrected, realigned to the first volume in the time series to correct for head motion, corrected for B0 distortions using SPM's fieldmap toolbox (Jezzard and Balaban, 1995), coregistered to the anatomical image using FSL's Boundary Based Registration (Greve and Fischl, 2009), spatially normalized into MNI space using the non-linear warp from the anatomical image, and smoothed to minimize noise and residual difference in gyral anatomy with a Gaussian filter, set at 6-mm full-width at half-maximum. All transformations were concatenated so that a single interpolation was performed.
Time-series images for each participant were furthered processed to limit the influence of motion and other artifacts. Voxel-wise signal intensities were scaled to yield a time series mean of 100 for each voxel. Motion regressors were created using each subject’s 6 motion correction parameters (3 rotation and 3 translation) and their first derivatives (Jo et al., 2013; Satterthwaite et al., 2013) yielding 12 motion regressors. White matter (WM) and cerebrospinal fluid (CSF) nuisance regressors were created using CompCorr (Behzadi et al., 2007). Images were bandpass filtered to retain frequencies between .008 and .1 Hz, and volumes exceeding 0.35mm frame-wise displacement or 1.55 standardized DVARS (Nichols, 2017; Power et al., 2014) were censored. Nuisance regression, bandpass filtering and censoring for each time series was performed in a single processing step using AFNI’s 3dTproject. Participants were excluded if they had less than (work in progress) 185 TRs left after censoring (resulting in inadequate degrees of freedom to perform nuisance regressions), resulting in a final sample of 605 subjects.
Intrinsic Network Connectivity: Seed-Based Analyses
After pre-processing, mean timeseries were extracted from each seed and used to create whole brain Z-transformed correlation maps for each participant corresponding to the seeds.
References
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Nichols TE. Notes on Creating a Standardized Version of DVARS. 2017;(July 2006):1-5. http://arxiv.org/abs/1704.01469.
Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage. 2014;84:320-341. doi:10.1016/j.neuroimage.2013.08.048.