Duke Neurogenetics Study MRI Protocol
Hippocampus Paradigm
Hippocampus Reactivity Paradigm
Our fMRI paradigm consists of the encoding and subsequent recall of novel face-name pairs (Zeineh et al., 2003). A distractor task (odd/even number identification) is interleaved between encoding and recall blocks to prevent maintenance of information in working memory. During each of four encoding blocks, subjects view six novel face-name pairs for 3.5 seconds each. During each of four recall blocks, subjects view six faces each presented for 2 seconds and immediately followed by an incomplete name fragment for 1 second during which they are required by forced-choice to determine if the fragment is correct or incorrect. A 1 second inter-trial interval is used during recall blocks. During each of four distractor blocks, subjects view six different numbers for 3.5 seconds each and are required to determine if the numbers are odd or even (see diagram). Total task length is 324 seconds.
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 retained after censoring for FD and DVARS and sufficient temporal SNR within the bilateral AAL hippocampus ROI, defined as greater than 3 standard deviations below the mean of this value across subjects. Additionally, data were only included in further analyses if the participant demonstrated sufficient engagement with the task, defined as 66% accuracy recalling the names.
BOLD fMRI Data Analysis
The AFNI program 3dREMLfit (Cox, 1996) was used to fit a general linear model for first-level fMRI data analyses. Following preprocessing, linear contrasts employing canonical hemodynamic response functions were used to estimate effects of condition (Encoding > Distractor, Recall > Distractor, Encoding > Recall) for each individual. Individual contrast images were then used in second-level random effects models in SPM12 (http://www.fil.ion.ucl.ac.uk/spm) accounting for scan-to-scan and participant-to-participant variability to determine mean condition-specific regional responses using one-sample t-tests. A statistical threshold of p<0.05, FWE corrected across the AAL hippocampus ROI, and ≥10 contiguous voxels was applied to the Encoding > Distractor contrast.
References
Arno Klein, Jesper Andersson, Babak A. Ardekani, John Ashburner, Brian Avants, Ming-Chang Chiang, Gary E. Christensen, D. Louis Collins, James Gee, Pierre Hellier, Joo Hyun Song, Mark Jenkinson, Claude Lepage, Daniel Rueckert, Paul Thompson, Tom Vercauteren, Roger P. Woods, J. John Mann, Ramin V. Parsey, Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration, NeuroImage, Volume 46, Issue 3, 1 July 2009, Pages 786-802, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2008.12.037. (http://www.sciencedirect.com/science/article/pii/S1053811908012974)
Cox RW (1996): AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res., 29(3):162-173
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
Zeineh MM, Engel SA, Thompson PM, Bookheimer SY. Dynamics of the hippocampus during encoding and retrieval of face-name pairs. Science. 2003;299:577–580.