Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder

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• 作者：Huawang Wu^a ; ^b ; Hui Sun^c ; Chao Wang^d ; Lin Yu^b ; Yilan Li^b ; Hongjun Peng^b ; Xiaobing Lu^b ; Qingmao Hu^e ; Yuping Ning^b ; Tianzi Jiang^a ; ^f ; ^g ; ^h ; Jinping Xu^e ; ^{jp.xu@siat.ac.cn} ; Jiaojian Wang^a ; ^{jiaojianwang@uestc.edu.cn}
• 关键词：Major depressive disorder ; Structural covariance ; VBM ; Gray matter volume ; Emotion regulation network
• 刊名：Journal of Psychiatric Research
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：84
• 期：Complete
• 页码：237-242
• 全文大小：537 K
• 卷排序：84

文摘

Major depressive disorder (MDD) is a common psychiatric disorder that is characterized by cognitive deficits and affective symptoms. To date, an increasing number of neuroimaging studies have focused on emotion regulation and have consistently shown that emotion dysregulation is one of the central features and underlying mechanisms of MDD. Although gray matter morphological abnormalities in regions within emotion regulation networks have been identified in MDD, the interactions and relationships between these gray matter structures remain largely unknown. Thus, in this study, we adopted a structural covariance method based on gray matter volume to investigate the brain morphological abnormalities within the emotion regulation networks in a large cohort of 65 MDD patients and 65 age- and gender-matched healthy controls. A permutation test with p < 0.05 was used to identify the significant changes in covariance connectivity strengths between MDD patients and healthy controls. The structural covariance analysis revealed an increased correlation strength of gray matter volume between the left angular gyrus and the left amygdala and between the right angular gyrus and the right amygdala, as well as a decreased correlation strength of the gray matter volume between the right angular gyrus and the posterior cingulate cortex in MDD. Our findings support the notion that emotion dysregulation is an underlying mechanism of MDD by revealing disrupted structural covariance patterns in the emotion regulation network.