Dopaminergic mechanisms underlying normal variation in trait anxiety

Contributed by aberryobie

If you use data from this collection please cite:
http://doi.org/10.1523/JNEUROSCI.2382-18.2019

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AuthorsAnne S. Berry, Robert L White, Daniella J. Furman, Jenna R. Naskolnakorn, Vyoma D. Shah, Mark D'Esposito and William J. Jagust
DescriptionResting state functional connectivity map with bilateral amygdala seed region. Amygdala ROIs were derived from individual participants' Freesurfer (v5.1) segmentation. Thirty participants (18-25 years old, M=21.50, SD=2.08, 20 female, 17 Asian, 7 White, 2 Black or African American, 2 Latino, 1 more than one race) are included in the current analyses. Participants did not have a history of neurological, psychological or psychiatric disorder, and did not smoke or take medication that affects cognition. Functional images were acquired using T2*-weighted echo planar imaging (EPI) with 36 interleaved slices (TR=2s, TE=24ms, FA=65°, matrix=64x64, FOV=192mm; 180 volumes; axial; voxels=3x3x3mm). Two dummy scans were acquired prior to acquisition. During resting-state fMRI acquisition, a white fixation cross on a black background was displayed in the center of the screen. Participants were asked to remain awake with their eyes open and focused on the cross. Preprocessing was performed with AFNI software. The first 3 functional volumes were excluded to ensure steady-state magnetization. Raw time series data were despiked with AFNI 3dDespike (https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dDespike.html). Images were then corrected for differences in slice acquisition timing using quintic interpolation. Volume registration to the functional volume with the lowest computed signal outlier fraction and nonlinear spatial warping to the MNI template were applied in a single step to limit instances of data interpolation. Removal of nuisance signals, volume censoring (“scrubbing”), and bandpass filtering (0.01Hz < f < 0.1Hz) were performed in a single linear regression using AFNI’s 3dTproject function. Nuisance signals included six motion parameters derived from volume registration along with their first-order derivatives, local white matter signal, time series associated with three principal components of lateral ventricle signal (aCompCor), and 0th-3rd order polynomial trends. Framewise motion was calculated as the derivative of the Euclidian norm of the 6 motion parameters calculated from volume registration. Motion scrubbing was performed as follows. Volumes with >0.3 mm motion were censored along with the volumes that immediately preceded them. Volumes were also flagged as contaminated when more than 15% of voxels within the brain were temporal outliers, as defined by the AFNI program 3dToutcount. Across all subjects, the combination of these masks censored 4.23±8.54 frames per subject (range 0–35). Residual data were smoothed with a 5mm FWHM Gaussian kernel within an anatomically-defined gray matter mask to avoid blurring with adjacent white matter and cerebrospinal fluid.
JournalThe Journal of Neuroscience
Contributors
DOI10.1523/JNEUROSCI.2382-18.2019
Field StrengthNone
id4666
Add DateDec. 12, 2018, 7:01 p.m.