128 Chapter 5 could see the task through a mirror attached to the head coil. Foam inserts were used to restrict head motion. Scans were examined by a radiologist in case of any suspicion of abnormalities. Data preprocessing and analyses Affective responses Self-reported affective responses in the task were analyzed in R (version 3.6.1), with the following packages: Nlme for mixed model analysis, psych for descriptive statistics, and ggplot2 for creating figures (Bates, Maechler, Bolker, & Walker, 2015; R Core Team, 2013; Wickham, 2009). Trials that were not answered by the participants within a set time period of 8000 ms were reported as missing values and excluded from the analyses. To assess the influence of ‘perspective’ (3 levels: Self, own child, unfamiliar child) and ‘stimulus type’ (2 levels: Physical and social) on self-reported distress we used a generalized linear mixed regression model with ‘perspective’ and ‘stimulus type’ as predictors of participants’ self-reported distress ratings. To examine how these affective responses towards the own child were associated with parental care as reported by the adolescent child, we ran correlation analyses where we correlated mean ∆self-reported distress for the own child (i.e., distress for own child minus distress for unfamiliar child) against child-reported parental care. All analyses were controlled for gender and age of the parents and adolescents. Significance was set at p <.05 (two-tailed) and Cohen’s d effect sizes were calculated for significant effects. Neural data analyses MRI data were preprocessed and analyzed using SPM12 (Wellcome Trust Centre for Neuroimaging, University College London). Functional MR images were slice-time corrected, corrected for field-strength inhomogeneity’s using b0 field maps, unwarped and realigned, co-registered to subject-specific structural images, normalized to MNI space (using the DARTEL toolbox (Ashburner, 2007)), and smoothed using an 8-mm full width at half maximum isotropic Gaussian kernel. Raw and preprocessed data were checked for quality, registration and movement. Head movement did not exceed 1 voxel/3 mm for any of the participants. Furthermore, we corrected for serial autocorrelations using a first order autoregressive model (AR(1)). We removed low-frequency signals using a high-pass filter (cutoff = 128 s) and included nuisance covariates to remove effects of run. To examine neural responses to imagined social and physical suffering for the self, own child and an unfamiliar child, we constructed a GLM with six regressors indicating cue onset for each condition separately and one regressor for subjective rating onsets. Cue onset regressors were defined from the onset of the statement period (“imagine the situation”) and modeled for the duration of this period (5000 ms). The subjective rating regressor was defined from the onsets of the question and modeled for the duration each question was displayed on the screen (self-
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