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Trait self-consciousness predicts amygdala activation and its functional brain connectivity during emotional suppression: an fMRI analysis

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      Abstract

      The present functional magnetic resonance imaging study investigated how trait neuroticism and its heterogeneous subdimensions are related to the emotional consequences and neural underpinnings of emotion regulation. Two levels of neuroticism assessments were conducted with 47 female subjects, who were required to attend to, suppress emotion displays to, or cognitively reappraise the meanings of negative images. The results showed reduced emotional experience and bilateral amygdala activation during reappraisal, and this regulation effect is unaffected by individual differences in neuroticism and its subdimensions. By contrast, the emotion downregulation effect of suppression in the right amygdala is compromised with increasing self-consciousness but not overall neuroticism dimension. This association holds robust after controlling the potential contribution of habitual suppression. Moreover, the psychophysiological interaction (PPI) analysis revealed that self-consciousness predicts weaker functional coupling of the right amygdala to supplementary motor area and putamen during expressive suppression, two regions mediating the control and execution of motor actions. These findings suggest that self-consciousness predicts increased difficulty in emotional regulation using expressive suppression; and that the heterogeneous nature of trait neuroticism needs to be considered in exploring the association of neuroticism and emotion regulation.

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        A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
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          An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets.

          Analysis and interpretation of functional MRI (fMRI) data have traditionally been based on identifying areas of significance on a thresholded statistical map of the entire imaged brain volume. This form of analysis can be likened to a "fishing expedition." As we become more knowledgeable about the structure-function relationships of different brain regions, tools for a priori hypothesis testing are needed. These tools must be able to generate region of interest masks for a priori hypothesis testing consistently and with minimal effort. Current tools that generate region of interest masks required for a priori hypothesis testing can be time-consuming and are often laboratory specific. In this paper we demonstrate a method of hypothesis-driven data analysis using an automated atlas-based masking technique. We provide a powerful method of probing fMRI data using automatically generated masks based on lobar anatomy, cortical and subcortical anatomy, and Brodmann areas. Hemisphere, lobar, anatomic label, tissue type, and Brodmann area atlases were generated in MNI space based on the Talairach Daemon. Additionally, we interfaced these multivolume atlases to a widely used fMRI software package, SPM99, and demonstrate the use of the atlas tool with representative fMRI data. This tool represents a necessary evolution in fMRI data analysis for testing of more spatially complex hypotheses.
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            Author and article information

            Affiliations
            [1 ]GRID grid.263906.8, The Laboratory for Affect Cognition and Regulation (ACRLAB), , Key Laboratory of Cognition and Personality of Ministry of Education (SWU), Faculty of Psychology, Southwest University, ; Chongqing, China
            [2 ]ISNI 0000 0000 9655 6126, GRID grid.463053.7, School of Educational Sciences, , Xinyang Normal University, ; Xinyang, China
            Contributors
            yangjiemin85@126.com
            yuanjiajin168@126.com
            Journal
            Sci Rep
            Sci Rep
            Scientific Reports
            Nature Publishing Group UK (London )
            2045-2322
            8 March 2017
            8 March 2017
            2017
            : 7
            28273918
            5428331
            73
            10.1038/s41598-017-00073-3
            © The Author(s) 2017

            This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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