Dataset Information

Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns.

ABSTRACT:

Background

Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder.

Methods

The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in China. Baseline resting-state functional magnetic resonance imaging (rs-fMRI) data was recorded for each participant. Discriminative features were selected from DMN between patients and HC. Patient subgroups were defined by K-means and principle component analysis in the multi-site datasets and validated in an independent single-site dataset. Statistical significance of resultant clustering were confirmed. Demographic and clinical variables were compared between identified patient subgroups.

Results

Two MDD subgroups with differing functional connectivity profiles of DMN were identified in the multi-site datasets, and relatively stable in different validation samples. The predominant dysfunctional connectivity profiles were detected among superior frontal cortex, ventral medial prefrontal cortex, posterior cingulate cortex and precuneus, whereas one subgroup exhibited increases of connectivity (hyperDMN MDD) and another subgroup showed decreases of connectivity (hypoDMN MDD). The hyperDMN subgroup in the discovery dataset had age-related severity of depressive symptoms. Patient subgroups had comparable demographic and clinical symptom variables.

Conclusions

Findings suggest the existence of two neural subtypes of MDD associated with different dysfunctional DMN connectivity patterns, which may provide useful evidence for parsing heterogeneity of depression and be valuable to inform the search for personalized treatment strategies.

SUBMITTER: Liang S

PROVIDER: S-EPMC7724374 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns.

Liang Sugai S Deng Wei W Li Xiaojing X Greenshaw Andrew J AJ Wang Qiang Q Li Mingli M Ma Xiaohong X Bai Tong-Jian TJ Bo Qi-Jing QJ Cao Jun J Chen Guan-Mao GM Chen Wei W Cheng Chang C Cheng Yu-Qi YQ Cui Xi-Long XL Duan Jia J Fang Yi-Ru YR Gong Qi-Yong QY Guo Wen-Bin WB Hou Zheng-Hua ZH Hu Lan L Kuang Li L Li Feng F Li Kai-Ming KM Liu Yan-Song YS Liu Zhe-Ning ZN Long Yi-Cheng YC Luo Qing-Hua QH Meng Hua-Qing HQ Peng Dai-Hui DH Qiu Hai-Tang HT Qiu Jiang J Shen Yue-Di YD Shi Yu-Shu YS Si Tian-Mei TM Wang Chuan-Yue CY Wang Fei F Wang Kai K Wang Li L Wang Xiang X Wang Ying Y Wu Xiao-Ping XP Wu Xin-Ran XR Xie Chun-Ming CM Xie Guang-Rong GR Xie Hai-Yan HY Xie Peng P Xu Xiu-Feng XF Yang Hong H Yang Jian J Yu Hua H Yao Jia-Shu JS Yao Shu-Qiao SQ Yin Ying-Ying YY Yuan Yong-Gui YG Zang Yu-Feng YF Zhang Ai-Xia AX Zhang Hong H Zhang Ke-Rang KR Zhang Zhi-Jun ZJ Zhao Jing-Ping JP Zhou Ru-Bai RB Zhou Yi-Ting YT Zou Chao-Jie CJ Zuo Xi-Nian XN Yan Chao-Gan CG Li Tao T

NeuroImage. Clinical 20201128

<h4>Background</h4>Major depressive disorder (MDD) is heterogeneous disorder associated with aberrant functional connectivity within the default mode network (DMN). This study focused on data-driven identification and validation of potential DMN-pattern-based MDD subtypes to parse heterogeneity of the disorder.<h4>Methods</h4>The sample comprised 1397 participants including 690 patients with MDD and 707 healthy controls (HC) registered from multiple sites based on the REST-meta-MDD Project in Ch ...[more]

PMID: 33396001

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Dataset Information

Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns.

Background

Methods

Results

Conclusions

Publications

Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns.

Similar Datasets

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