Duke Neurogenetics Study MRI Protocol
Amygdala Paradigm
BOLD fMRI Data Pre-Processing 1.0
(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) via the co-registered grey matter segmentation of the anatomical coplanar scan 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 the anatomically-defined bilateral amygdala. Additionally, data were only included in further analyses if the participant demonstrated sufficient engagement with the task, defined as 75% accuracy matching.
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 effects of condition (Angry block > Control blocks, Fearful block > Control blocks, Surprised block > Control blocks, Neutral block > Control blocks) for each individual. Individual contrast images were then used in second-level random effects models to conduct group-level analyses.