Duke Neurogenetics Study MRI Protocol
Ventral Striatum Paradigm
BOLD fMRI Data Pre-Processing
(Note: This description applies the LoNG pre-processing pipeline 1.0, used for all analyses before Spring 2017. For current analyses, see pipeline 2.0)Preprocessing was conducted using SPM8 (www.fil.ion.ucl.ac.uk/spm). Images for each subject were 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
Quality control criteria for inclusion of a participant's imaging data were: <5% volumes exceed artifact detection criteria for motion or signal intensity outliers and ≥90% coverage of signal within 5mm bilateral ventral striatum spheres centered at (±12, 10, -10). Additionally, data were only included in further analyses if the participant demonstrated sufficient engagement with the task, defined as responding to and receiving positive or negative feedback on at least 60% of trials within win and loss blocks, respectively.
BOLD fMRI Data Analysis
The general linear model of SPM8 (http://www.fil.ion.ucl.ac.uk/spm) was used to conduct fMRI data analyses. Following preprocessing, linear contrasts employing canonical hemodynamic response functions were used to estimate differential effects of feedback (i.e. reward) from the contrast of Positive Feedback > Negative Feedback for each individual. Individual contrast images were then used in second-level random effects models to conduct group-level analyses.