Duke Neurogenetics Study MRI Protocol
Working Memory Paradigm
Working Memory Reactivity Paradigm
Activity of the dlPFC was measured during BOLD fMRI using an event-related working memory paradigm adapted from Tan et al. (Tan et al., 2007, Scult et al. 2016). The paradigm included 10 trials for each of 6 different conditions, including 3 control conditions, consisting only of a 3s response phase, and 3 working memory (WM) conditions, consisting of a 0.5s encoding phase followed by a 4s maintenance interval and a 3s response phase (see diagram). Control and WM conditions were interleaved with jittered rest intervals lasting 4s to 8.5s for a total scan length of 11m 48s. Responses were recorded via an MR-compatible button box using the index (left button) and middle (right button) fingers of the dominant hand.
During the control conditions, participants performed 1) a simple motor task (M) in which they pressed either the left or the right button according to a prompt, 2) a numerical size judgment task (J) in which they chose the number on the left or right based on an instruction to choose either the larger or the smaller number, and 3) a numerical computation and size judgment task (CJ) in which they performed a numerical subtraction of 2 or 3 from either the left or right number, and made a numerical size judgment as instructed.
In the first WM condition, participants viewed 2 numbers during the brief encoding phase, then recalled the numbers and performed a numerical size judgment as instructed (E_RJ). In the second WM condition, the participants additionally performed subtraction of 2 or 3 from one of the remembered numbers as indicated before making the numerical size judgment during recall (E_RCJ). In the final WM condition, participants performed subtraction of 2 or 3 from one of the 2 numbers during the brief encoding phase, then recalled the resulting two numbers and performed a numerical size judgment as instructed during the response phase after the maintenance interval (EC_RJ). In each WM condition trial, all the numbers were single digits from 0 to 9; the two numbers on which the numerical size judgment was ultimately performed (after numerical computation if applicable) were equally balanced across 0 to 9, and equally likely to differ by either 1 or 3 units. Numerical computation was equally likely on the left or right number, with correct responses equally balanced on the left or right, and equally likely to be the larger or smaller number for each WM trial type. The trials were performed in an order that was optimized using a sequencing program (Wager & Nichols, 2003).
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. 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
(Note: This description applies the LoNG pre-processing pipeline 2.0, used for all analyses beginning in Spring 2017. For earlier analyses, see pipeline 1.0)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, 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, resampled to 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. All transformations were concatenated so that a single interpolation was performed. Voxel-wise signal intensities were scaled to yield a time series mean of 100 for each voxel. Volumes exceeding 0.5mm frame-wise displacement or 2.5 standardized DVARS (Nichols, 2017; Power et al., 2014) were censored from the GLM.
fMRI Quality Assurance Criteria
Quality control criteria for inclusion of a participant's imaging data were: >5 volumes for each condition of interest (i.e., recall period) retained after censoring for FD and DVARS and sufficient temporal SNR within the bilateral dlPFC, defined as greater than 3 standard deviations below the mean of this value across subjects. The dlPFC was defined as the union of BA regions 9 and 46. Additionally, data were only included in further analyses if the participant demonstrated sufficient engagement with the task, defined as at least 75% average accuracy across all trial types, and at least 50% accuracy within each trial type.
BOLD fMRI Data Analysis
Following preprocessing, the AFNI program 3dREMLfit (Cox, 1996) was used to fit a general linear model for first-level fMRI data analyses. Events were modeled for the response phase of correctly performed trials for each of the 6 trial types, and the maintenance and encoding (with and without computation modeled separately) phases for WM trials. Incorrect responses were also modeled as regressors of no interest. A linear contrast employing the canonical hemodynamic response function was used to estimate main effects for each individual for the comparison of E_RCJ > EC_RJ in order to isolate the manipulation of information in working memory above and beyond basic computation and maintenance of information across a delay. Individual contrast images were then used in second-level random effects models to conduct group-level analyses.
References
Scult, M. A., Knodt, A. R., Swartz, J. R., Brigidi, B. D., & Hariri, A. R. (2016). Thinking and Feeling: Individual Differences in Habitual Emotion Regulation and Stress-Related Mood Are Associated With Prefrontal Executive Control. Clinical Psychological Science, 2167702616654688.
Tan, H.-Y., Chen, Q., Goldberg, T. E., Mattay, V. S., Meyer-Lindenberg, A., Weinberger, D. R., & Callicott, J. H. (2007). Catechol-O-methyltransferase Val158Met modulation of prefrontal-parietal-striatal brain systems during arithmetic and temporal transformations in working memory. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 27(49), 13393-401. doi:10.1523/JNEUROSCI.4041-07.2007
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Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63-72. https://doi.org/10.1016/j.neuroimage.2009.06.060
Nichols, T. E. (2017). Notes on Creating a Standardized Version of DVARS, 1-5. Retrieved from http://arxiv.org/abs/1704.01469
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., and Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320-341. doi: 10.1016/j.neuroimage.2013.08.048