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Shocking Information Regarding XAV-939

Prior to collecting fMRI data for each participant, a reference echoplanar imaging scan was collected; this scan was visually inspected for artifacts and good signal. BOLD fMRI data were processed with SPM8 (Wellcome Department of Imaging Neuroscience, London, England). Images for each participant were realigned to the first volume in the time series to correct for head motion, spatially normalized into a standard stereotactic space (Montreal Neurological Institute template) using a 12-parameter affine model, and smoothed to minimize noise and residual difference in gyral anatomy with a Gaussian filter set at 6?mm full-width at half-maximum. Next, the ARtifact detection Tool (ART) (Neuroimaging Informatics Tools and Resources Clearinghouse) was used to selleckchem generate regressors to account for images with large motion (i.e. >0.6?mm relative to the previous time frame) or spiking artifacts (i.e. global mean intensity 2.5 standard deviations from the entire time series). After preprocessing, linear contrasts using canonical hemodynamic response functions estimated condition-specific (i.e. faces?>?shapes) BOLD activation for each individual. As we were not interested in neural networks associated with face-specific processing per se, but rather in eliciting a maximal amygdala response across all participants, we chose not to use neutral faces as control stimuli because neutral faces can be subjectively experienced as affectively laden or ambiguous and thus engage the amygdala (Schwartz et al. 2003; Wright et al. 2003). Individual contrast tuclazepam images (i.e. weighted sum of the beta images) were used in a second-level random effects model to determine mean condition-specific responses using a one-sample t test, see more corrected for FWE at a voxel level of P?<?0.05 with a voxel extant of 10 contiguous voxels across amygdala ROIs. Mean BOLD contrast estimates were extracted from functional clusters exhibiting a main effect of task (FWE P?<?.05; ��10 contiguous voxels) within anatomically defined amygdala ROIs. Extracting parameter estimates from functional clusters activated by our fMRI paradigm, rather than clusters specifically correlated with our independent variables of interest, precludes the possibility of any correlation coefficient inflation that may result when an explanatory covariates is used to select a ROI (Viviani 2010). We have used this more conservative and rigorous analytic strategy in recent studies (Carr�� et al. 2010; Hyde et al. 2011). To account for distinct functional subregions within the amygdala, we constructed separate ventral (i.e. basolateral complex; 1024?mm3/42 voxels) and dorsal (i.e. central nucleus and substantia inominata; 1920?mm3/93.33 voxels) amygdala ROIs as previously described (Carr�� et al. 2010; Manuck et al. 2010).</div>
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