Duke Neurogenetics Study MRI Protocol
Resting State
BOLD fMRI Data Acquisition
Each participant was scanned using a research-dedicated GE MR750 3 T scanner equipped with high-power high-duty-cycle 50-mT/m gradients at 200 T/m/s slew rate, and an eight-channel head coil for parallel imaging at high bandwidth up to 1MHz at the Duke-UNC Brain Imaging and Analysis Center. A semi-automated high-order shimming program was used to ensure global field homogeneity. A series of 34 interleaved axial functional slices aligned with the anterior commissure-posterior commissure plane were acquired for full-brain coverage using an inverse-spiral pulse sequence to reduce susceptibility artifacts (TR/TE/flip angle=2000 ms/30 ms/60; FOV=240mm; 3.75×3.75×4mm voxels; interslice skip=0). Four initial radiofrequency excitations were performed (and discarded) to achieve steady-state equilibrium. For each participant, 2 back-to-back 4-minute 16-second resting state functional MRI scans were acquired. Participants were instructed to remain awake, with their eyes open during each resting state scan. To allow for spatial registration of each participant's data to a standard coordinate system, high-resolution three-dimensional structural images were acquired in 34 axial slices coplanar with the functional scans (TR/TE/flip angle=7.7 s/3.0 ms/12; voxel size=0.9×0.9×4mm; FOV=240mm, interslice skip=0).
BOLD fMRI Data Pre-Processing (2016 forward)
Preprocessing was conducted using SPM12 (www.fil.ion.ucl.ac.uk/spm). Images for each subject were slice-time corrected, realigned to the first volume in the time series to correct for head motion, spatially normalized into a standard stereotactic space (Montreal Neurological Institute template) using the non-linear DARTEL technique (Ashburner, 2007) (final resolution of functional images=2mm isotropic voxels), and smoothed to minimize noise and residual difference in gyral anatomy with a Gaussian filter, set at 6-mm full-width at half-maximum. Voxel-wise signal intensities were ratio normalized to the whole-brain global mean. Variability in single-subject whole-brain functional volumes was determined using the Artifact Recognition Toolbox (http://www.nitrc.org/projects/artifact_detect). Individual whole-brain BOLD fMRI volumes meeting at least one of two criteria were assigned a lower weight in determination of task-specific effects: (1) significant mean-volume signal intensity variation (i.e. within volume mean signal greater or less than 4 SD of mean signal of all volumes in time series) and (2) individual volumes where scan-to-scan movement exceeded 2mm translation or 2° rotation in any direction.
BOLD fMRI Data Pre-Processing (through 2015)
Preprocessing was conducted using SPM8 (www.fil.ion.ucl.ac.uk/spm). Images for each subject were slice-time corrected, realigned to the first volume in the time series to correct for head motion, spatially normalized into a standard stereotactic space (Montreal Neurological Institute template) using a 12-parameter affine model (final resolution of functional images=2mm isotropic voxels), and smoothed to minimize noise and residual difference in gyral anatomy with a Gaussian filter, set at 6-mm full-width at half-maximum. Voxel-wise signal intensities were ratio normalized to the whole-brain global mean. Variability in single-subject whole-brain functional volumes was determined using the Artifact Recognition Toolbox (http://www.nitrc.org/projects/artifact_detect). Individual whole-brain BOLD fMRI volumes meeting at least one of two criteria were assigned a lower weight in determination of task-specific effects: (1) significant mean-volume signal intensity variation (i.e. within volume mean signal greater or less than 4 SD of mean signal of all volumes in time series) and (2) individual volumes where scan-to-scan movement exceeded 2mm translation or 2° rotation in any direction.
fMRI Quality Assurance Criteria
A participant's imaging data were only included in further analyses if <5% of volumes exceeded artifact detection criteria for motion or signal intensity outliers.
Intrinsic Network Connectivity: Seed-Based Analyses (2016 forward)
Seed-based correlation maps were generated for each participant using the Conn toolbox (Whitfield-Gabrieli 2012). Individual head motion realignment parameters were included as confound regressors to remove the effects of residual head motion. Signal from three principal noise components associated with both white matter and cerebrospinal fluid were also included, along with indicators for volumes exceeding artifact detection criteria to be censored. Mean timeseries were extracted from each seed and used to create whole brain correlation maps for each participant corresponding to the seeds.
Intrinsic Network Connectivity: Seed-Based Analyses (through 2015)
Seed-based correlation maps were generated for each participant using the Conn toolbox (Whitfield-Gabrieli 2012). Individual head motion realignment parameters were included as confound regressors to remove the effects of residual head motion. Signal from three principal noise components associated with white matter and cerebrospinal fluid and one component associated with grey matter were also included. Mean timeseries were extracted from each seed and used to create whole brain correlation maps for each participant corresponding to the seeds.
References
Ashburner J. 2007. A fast diffeomorphic image registration algorithm. Neuroimage 38: 95-113.
Whitfield-Gabrieli S, Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2012; 2: 125-41.