Brain areas differing in activity as a function of the interaction of the Morality and Difficulty factors (the TPJ, dACC and vmPFC), our next aim was to deconstruct these interactions to examine functionality within those regions for Difficult and Easy Moral decisions relative to the matched Non-Moral comparison conditions. First, in order to understand which areas are differentially more activated for difficult moral decisions, we compared Difficult Moral with Difficult Non-Moral scenarios (DM > DN) at the whole-brain level. This revealed a network starting at the TPJ and extending the length of the temporal lobe into the temporal pole (Figure 3a and Table 4). These findings demonstrate that difficult moral choices activate a network within the temporal lobeareas implicated in theory of mind (Young and Saxe, 2009), attentional switching (Tassy et al., 2012), higher order social concepts (Moll et al., 2008) and the understanding of social cues (Van Overwalle, 2009). To reveal brain regions demonstrating relative decreases in activity for difficult moral decisions, Difficult Non-Moral scenarios were contrasted with Difficult Moral scenarios (DN > DM), revealing vmPFC and bilateral orbital frontal cortex (OFC) deactivation (Figure 3a and Table 5). Thus, regions often associated with the moral network were found to be relatively less activated during difficult moral (vs non-moral) decisions once the difficulty of the scenario was controlled for. Using a similar rationale, we compared Easy Moral decisions with Easy Non-Moral decisions (EM > EN), revealing activation of the vmPFCan area known to integrate emotion into decision making and planning (Moretto et al., 2010). Research has also shown that patients suffering damage to the vmPFC exhibit poor practical judgment (Raine and Yang, 2006; Blair, 2008). Interestingly, there was a pattern of TPJ and dlPFC relative deactivation for Easy Moral decisions (EN > EM) (Figure 3b and Tables 6 and 7). Taken together, these patterns of activation and deactivation highlight that difficult moral decisions appear to differentially recruit theweighted structural images were acquired at a resolution of 1 ?1 ?1 mm. Imaging processing Statistical parametric mapping software (SPM5: www.fil.ion.ucl.ac.uk/ spm/software/spm5/) was used to analyze all data. Preprocessing of fMRI data included spatial realignment, AC220 manufacturer coregistration, normalization and smoothing. The first eight scans were discarded as dummy scans. To control for motion, all functional volumes were realigned to the mean volume. Images were spatially normalized to standard space using the Montreal Neurological Institute (MNI) template with a voxel size of 3 ?3 ?3 mm and smoothed using a Gaussian kernel with an isotropic full width at half maximum of 8 mm. Additionally, high-pass temporal filtering with a cut-off of 128 s was applied to remove low-frequency drifts in signal. Data analysis After preprocessing, statistical analysis was performed using the BX795 manufacturer general linear model. Activated voxels were identified using an event-related statistical model representing each of the response events, convolved with a canonical hemodynamic response function and mean corrected. Six head-motion parameters defined by the realignment were added to the model as regressors of no interest. Analysis was carried out to establish each participant’s voxel-wise activation when subjects made their response regarding each scenario (the aforementioned fixed 15 s floating window ap.Brain areas differing in activity as a function of the interaction of the Morality and Difficulty factors (the TPJ, dACC and vmPFC), our next aim was to deconstruct these interactions to examine functionality within those regions for Difficult and Easy Moral decisions relative to the matched Non-Moral comparison conditions. First, in order to understand which areas are differentially more activated for difficult moral decisions, we compared Difficult Moral with Difficult Non-Moral scenarios (DM > DN) at the whole-brain level. This revealed a network starting at the TPJ and extending the length of the temporal lobe into the temporal pole (Figure 3a and Table 4). These findings demonstrate that difficult moral choices activate a network within the temporal lobeareas implicated in theory of mind (Young and Saxe, 2009), attentional switching (Tassy et al., 2012), higher order social concepts (Moll et al., 2008) and the understanding of social cues (Van Overwalle, 2009). To reveal brain regions demonstrating relative decreases in activity for difficult moral decisions, Difficult Non-Moral scenarios were contrasted with Difficult Moral scenarios (DN > DM), revealing vmPFC and bilateral orbital frontal cortex (OFC) deactivation (Figure 3a and Table 5). Thus, regions often associated with the moral network were found to be relatively less activated during difficult moral (vs non-moral) decisions once the difficulty of the scenario was controlled for. Using a similar rationale, we compared Easy Moral decisions with Easy Non-Moral decisions (EM > EN), revealing activation of the vmPFCan area known to integrate emotion into decision making and planning (Moretto et al., 2010). Research has also shown that patients suffering damage to the vmPFC exhibit poor practical judgment (Raine and Yang, 2006; Blair, 2008). Interestingly, there was a pattern of TPJ and dlPFC relative deactivation for Easy Moral decisions (EN > EM) (Figure 3b and Tables 6 and 7). Taken together, these patterns of activation and deactivation highlight that difficult moral decisions appear to differentially recruit theweighted structural images were acquired at a resolution of 1 ?1 ?1 mm. Imaging processing Statistical parametric mapping software (SPM5: www.fil.ion.ucl.ac.uk/ spm/software/spm5/) was used to analyze all data. Preprocessing of fMRI data included spatial realignment, coregistration, normalization and smoothing. The first eight scans were discarded as dummy scans. To control for motion, all functional volumes were realigned to the mean volume. Images were spatially normalized to standard space using the Montreal Neurological Institute (MNI) template with a voxel size of 3 ?3 ?3 mm and smoothed using a Gaussian kernel with an isotropic full width at half maximum of 8 mm. Additionally, high-pass temporal filtering with a cut-off of 128 s was applied to remove low-frequency drifts in signal. Data analysis After preprocessing, statistical analysis was performed using the general linear model. Activated voxels were identified using an event-related statistical model representing each of the response events, convolved with a canonical hemodynamic response function and mean corrected. Six head-motion parameters defined by the realignment were added to the model as regressors of no interest. Analysis was carried out to establish each participant’s voxel-wise activation when subjects made their response regarding each scenario (the aforementioned fixed 15 s floating window ap.