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In NeuroImage. Clinical

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.

Liang Sugai, Deng Wei, Li Xiaojing, Greenshaw Andrew J, Wang Qiang, Li Mingli, Ma Xiaohong, Bai Tong-Jian, Bo Qi-Jing, Cao Jun, Chen Guan-Mao, Chen Wei, Cheng Chang, Cheng Yu-Qi, Cui Xi-Long, Duan Jia, Fang Yi-Ru, Gong Qi-Yong, Guo Wen-Bin, Hou Zheng-Hua, Hu Lan, Kuang Li, Li Feng, Li Kai-Ming, Liu Yan-Song, Liu Zhe-Ning, Long Yi-Cheng, Luo Qing-Hua, Meng Hua-Qing, Peng Dai-Hui, Qiu Hai-Tang, Qiu Jiang, Shen Yue-Di, Shi Yu-Shu, Si Tian-Mei, Wang Chuan-Yue, Wang Fei, Wang Kai, Wang Li, Wang Xiang, Wang Ying, Wu Xiao-Ping, Wu Xin-Ran, Xie Chun-Ming, Xie Guang-Rong, Xie Hai-Yan, Xie Peng, Xu Xiu-Feng, Yang Hong, Yang Jian, Yu Hua, Yao Jia-Shu, Yao Shu-Qiao, Yin Ying-Ying, Yuan Yong-Gui, Zang Yu-Feng, Zhang Ai-Xia, Zhang Hong, Zhang Ke-Rang, Zhang Zhi-Jun, Zhao Jing-Ping, Zhou Ru-Bai, Zhou Yi-Ting, Zou Chao-Jie, Zuo Xi-Nian, Yan Chao-Gan, Li Tao

2020

Biotypes, Default mode network, Machine learning, Major depressive disorder, Resting-state fMRI