Zongling He1, Fengmei Lu1, Wei Sheng1, Shaoqiang Han1, Yajing Pang1, Yuyan Chen1, Qin Tang1, Yang Yang1, Wei Luo1, Yue Yu1, Xiaohan Jia1, Di Li1, Ailing Xie2, Qian Cui3, Huafu Chen4. 1. The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China. 2. School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China. 3. School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China. Electronic address: qiancui26@gmail.com. 4. The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China. Electronic address: chenhf@uestc.edu.cn.
Abstract
OBJECTIVE: Major depressive disorder (MDD) is a neuropsychiatric disorder associated with functional dysconnectivity in emotion regulation system. State characteristics which measure the current presence of depressive symptoms, and trait characteristics which indicate the long-term vulnerability to depression are two important features of MDD. However, the relationships between trait and state characteristics of MDD and functional connectivity (FC) within the emotion regulation system still remain unclear. METHODS: This study aims to examine the neural biological mechanisms of trait characteristics measured by the Affective Neuroscience Personality Scale (ANPS) and state anhedonia measured by the Snaith-Hamilton Pleasure Scale (SHAPS) in MDD. Sixty-three patients with MDD and 63 well-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. A spatial pairwise clustering and the network-based analysis approaches were adopted to identify the abnormal FC networks. Support vector regression was utilized to predict the trait and state characteristics based on abnormal FCs. RESULTS: Four disrupted subnetworks mainly involving the prefrontal-limbic-striatum system were observed in MDD. Importantly, the abnormal FC between the left amygdala (AMYG)/hippocampus (HIP) and right AMYG/HIP could predict the SADNESS scores of ANPS (trait characteristics) in MDD. While the aberrant FC between the medial prefrontal cortex (mPFC)/anterior cingulate gyrus (ACC) and AMYG/parahippocampal gyrus could predict the state anhedonia scores (state characteristics). CONCLUSIONS: The present findings give first insights into the neural biological basis underlying the trait and state characteristics associated with functional dysconnectivity within the emotion regulation system in MDD.
OBJECTIVE: Major depressive disorder (MDD) is a neuropsychiatric disorder associated with functional dysconnectivity in emotion regulation system. State characteristics which measure the current presence of depressive symptoms, and trait characteristics which indicate the long-term vulnerability to depression are two important features of MDD. However, the relationships between trait and state characteristics of MDD and functional connectivity (FC) within the emotion regulation system still remain unclear. METHODS: This study aims to examine the neural biological mechanisms of trait characteristics measured by the Affective Neuroscience Personality Scale (ANPS) and state anhedonia measured by the Snaith-Hamilton Pleasure Scale (SHAPS) in MDD. Sixty-three patients with MDD and 63 well-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. A spatial pairwise clustering and the network-based analysis approaches were adopted to identify the abnormal FC networks. Support vector regression was utilized to predict the trait and state characteristics based on abnormal FCs. RESULTS: Four disrupted subnetworks mainly involving the prefrontal-limbic-striatum system were observed in MDD. Importantly, the abnormal FC between the left amygdala (AMYG)/hippocampus (HIP) and right AMYG/HIP could predict the SADNESS scores of ANPS (trait characteristics) in MDD. While the aberrant FC between the medial prefrontal cortex (mPFC)/anterior cingulate gyrus (ACC) and AMYG/parahippocampal gyrus could predict the state anhedonia scores (state characteristics). CONCLUSIONS: The present findings give first insights into the neural biological basis underlying the trait and state characteristics associated with functional dysconnectivity within the emotion regulation system in MDD.
Authors: Anou Pietrek; Maria Kangas; Reinhold Kliegl; Michael A Rapp; Stephan Heinzel; Jolene van der Kaap-Deeder; Andreas Heissel Journal: Front Psychiatry Date: 2022-09-20 Impact factor: 5.435
Authors: Julia-Katharina Pfarr; Katharina Brosch; Tina Meller; Kai Gustav Ringwald; Simon Schmitt; Frederike Stein; Susanne Meinert; Dominik Grotegerd; Katharina Thiel; Hannah Lemke; Alexandra Winter; Lena Waltemate; Tim Hahn; Nils Opel; Jonathan Repple; Jochen Bauer; Andreas Jansen; Udo Dannlowski; Axel Krug; Tilo Kircher; Igor Nenadić Journal: Hum Brain Mapp Date: 2021-07-24 Impact factor: 5.038