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Abstract #0794

Clustering analysis differentiates clinical subtypes of major depressive disorder that identify symptom-specific brain connectivity

Shi Tang1, Yanlin Wang1, Yongbo Hu1, Lu Lu1, Lianqing Zhang1, Xuan Bu1, Hailong Li1, Yingxue Gao1, Lingxiao Cao1, Xinyue Hu1, Jing Liu1, Xinyu Hu1, Weihong Kuang2, Qiyong Gong1, and Xiaoqi Huang1
1Huaxi MR Research Center (HMRRC), Functional and molecular imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China, Chengdu, China, 2Department of psychiatry, West China Hospital, Sichuan University, Chengdu 610041, China, Chengdu, China

Functional connectivity/network analyses using fMRI data have been applied to characterize diagnostic biomarkers in MDD. However, the association between brain connection and dimensional symptoms of this heterogeneous syndrome still remains unclear. In this work, we focused on first-episode and unmedicated MDD patients, firstly using unsupervised clustering analysis differentiated them into two subgroups on the basis of clinical features. Also, we compared the brain connectivity among subgroups plus healthy people. Then we used multivariate methods identified which clinical symptoms are significantly influenced by which brain connectivity. Our results may provide neurobiological mechanisms of MDD symptoms and serve as effective diagnostic biomarkers.

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