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Abstract #0197

Data-driven clustering differentiates subtypes of major depressive disorder with distinct connectivity-symptom association

Yanlin Wang1, Shi Tang1, Xinyu Hu1, Yongbo Hu1, Weihong Kuang2, Zhiyun Jia1, Xiaoqi Huang1, and Qiyong Gong1
1Department of Radiology, West China Hospital, Sichuan University, Huaxi MR Research Center (HMRRC), Functional and molecular imaging Key Laboratory of Sichuan Province, Chengdu, China, 2Department of Psychiatry, Sichuan University West China Hospital, Chengdu, China

Major depressive disorder (MDD) is a clinically heterogeneous syndrome and commonly co-occur alongside symptoms of other psychiatric domains. It is challenging to identify the correspondence between these clinical heterogeneous and relevant neurobiological substrates and define neurophysiological subtypes of MDD. We used regularized canonical correlation analysis (rCCA) to assess a two-dimensional mapping between the intrinsic connectivity networks (ICNs) and clinical symptoms and thus aid in defined MDD subtypes. We then compared potential symptom severity and neural features alterations between these subtypes and further assess the association between these features.

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