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

2020; Elsevier BV; Volume: 28; Linguagem: Inglês

10.1016/j.nicl.2020.102514

2213-1582

Sugai Liang, Wei Deng, Xiaojing Li, Andrew J. Greenshaw, Qiang Wang, Mingli Li, Xiaohong Ma, Tongjian Bai, Qijing Bo, Jun Cao, Guanmao Chen, Wei Chen, Cheng Chang, Yuqi Cheng, Xilong Cui, Jia Duan, Yiru Fang, Qiyong Gong, Wenbin Guo, Zhenghua Hou, Lan Hu, Li Kuang, Li Feng, Kaiming Li, Yan‐Song Liu, Zhening Liu, Yicheng Long, Qinghua Luo, Huaqing Meng, Daihui Peng, Haitang Qiu, Jiang Qiu, Yuedi Shen, Yu‐Shu Shi, Tianmei Si, Chuanyue Wang, Fei Wang, Kai Wang, Li Wang, Xiang Wang, Ying Wang, Xiaoping Wu, Xinran Wu, Chunming Xie, Guangrong Xie, Haiyan Xie, Peng Xie, Xiu‐Feng Xu, Hong Yang, Jian Yang, Hua Yu, Jiashu Yao, Shuqiao Yao, Yingying Yin, Yonggui Yuan, Yu‐Feng Zang, Ai‐Xia Zhang, Hong Zhang, Kerang Zhang, Zhijun Zhang, Jingping Zhao, Rubai Zhou, Yiting Zhou, Chao‐Jie Zou, Xi‐Nian Zuo, Chao‐Gan Yan, Tao Li,

Advanced Neuroimaging Techniques and Applications

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. 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. 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. 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.