Computational protocol: The influence of valence and decision difficulty on self-referential processing

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Protocol publication

[…] MRI parameters. Participants were scanned during task performance using a 3-T GE Signa scanner (GE Healthcare, Chalfont St Giles, England). A total of 133 functional images per run were taken with a gradient echo planar imaging (EPI) sequence (repetition time = 2900 ms; echo time = 27 ms; 64_64 matrix; 90° flip angle; 24-cm field of view). A repetition time of 2900 ms was the shortest TR that allowed us full brain coverage with our chosen voxel size. Whole brain coverage was obtained with 46 axial slices (thickness, 3 mm; in-plane resolution, 3.75 × 3.75 mm). A high-resolution anatomical scan (3-dimensional spoiled gradient recalled acquisition in a steady state; repetition time = 7 ms; echo time = 2.984 ms; 24-cm field of view; 12° flip angle; 128 axial slices; thickness, 1.2 mm; 256 × 192 matrix) in register with the EPI data set was obtained covering the whole brain.Imaging data preprocessing. Imaging data were preprocessed and analyzed in AFNI (Cox, ). At the individual level, functional images from the first 5 repetitions were collected before equilibrium magnetization was reached and were discarded. Functional images from the 4 time series were motion corrected and spatially smoothed with a 6-mm full-width half-maximum gaussian filter. The time series were normalized by dividing the signal intensity of a voxel at each point by the mean signal intensity of that voxel for each run and multiplying the result by 100. Resultant regression coefficients represented a percentage of signal change from the mean.Following this, the following eight regressors were generated: self-referential high intensity negative traits (Self-High-Neg), self-referential low intensity negative traits (Self-Low-Neg), self-referential high intensity positive traits (Self-High-Pos), self-referential low intensity positive traits (Self-Low-Pos), other-referential high intensity negative traits (Other-High-Neg), other-referential low intensity negative traits (Other-Low-Neg), other-referential high intensity positive traits (Other-High-Pos), and other-referential low intensity positive traits (Other-Low-Pos). These indicator functions were then convolved with a gammavariate hemodynamic response function to account for the slow hemodynamic response and used as regressors for our first-level analyses. Linear regression modeling was performed using the eight regressors described above plus regressors to model a first-order baseline drift function. This produced a β-coefficient and associated t statistic for each voxel and regressor. The participants' anatomical scans were individually registered to the Talairach and Tournoux atlas (Talairach and Tournoux, ). The individuals' functional EPI data were then registered to their Talairach anatomical scan within AFNI.fMRI data analysis. Analysis was then performed on regression coefficients from individual subject analyses using a 2 (Referential target: self or other) × 2 (Valence: positive or negative) × 2 (Intensity: low or high) repeated measures ANOVA. All regions were corrected for multiple comparisons via ClustSim (initial threshold: p < 0.001 corrected at p < 0.05 using an extent threshold of 12 voxels). Group effects were masked using a brain mask based on the mean normalized anatomical images of all participants.After observing hypothesized effects, post-hoc analyses were performed to facilitate interpretations. For these analyses, average percent signal change was measured across all voxels within each ROI generated from the functional mask, and data were analyzed using appropriate follow-up tests within SPSS.Behavioral analysis. The evaluations made by the participants were re-coded into numerical values (completely disagree = 1, disagree = 2, agree = 3, completely agree = 4). Three 2 (Referential target: self or other) × 2 (Valence: positive or negative) × 2 (Intensity: low or high) repeated measures ANOVAs were conducted on the participant's judgment (their agreement with the statement), their RT and response consistency (i.e., response variance for each of the 8 stimulus classes), respectively. […]

Pipeline specifications

Software tools AFNI, SPSS
Applications Miscellaneous, Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens
Diseases Brain Diseases, Neural Tube Defects