Abstract #0214

Heterogeneous lifespan development of functional networks in major depression and its association with clinical and gene expression profiles

Chenxuan Pang¹, Xiaoyi Sun¹, Xiaoqin Wang², Dongtao Wei², Yuan Chen³, Bangshan Liu⁴, Chu-Chung Huang⁵, Yanting Zheng⁶, Yankun Wu⁷, Taolin Chen⁸, Yuqi Cheng⁹, Xiufeng Xu⁹, Qiyong Gong⁸, Tianmei Si⁷, Shijun Qiu⁶, Ching-Po Lin¹⁰, Jingliang Cheng³, Yanqing Tang¹¹, Fei Wang¹¹, Jiang Qiu², Peng Xie¹², Lingjiang Li⁴, Yong He¹, and Mingrui Xia¹

¹Beijing Normal University, Beijing, China, ²Southwest University, Chongqing, China, ³The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ⁴The Second Xiangya Hospital of Central South University, Changsha, China, ⁵East China Normal University, Shanghai, China, ⁶The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China, ⁷Peking University Sixth Hospital, Beijing, China, ⁸West China Hospital, Sichuan University, Chengdu, China, ⁹First Affiliated Hospital of Kunming Medical University, Kunming, China, ¹⁰National Yang-Ming Chiao-Tung University, Taipei, Taiwan, ¹¹The First Affiliated Hospital of China Medical University, Shenyang, China, ¹²The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

Synopsis

Keywords: Functional Connectivity, fMRI (resting state), Major depressive disorder; brain age; lifespan trajectory; connectome; transcriptome

Motivation: Depression is a heterogeneous disorder linked to abnormal development of functional networks across the lifespan. However, neurodevelopmental heterogeneity in depression is not well understood.

Goal(s): To investigate heterogeneous neurodevelopmental functional networks abnormalities in depression and their associations with clinical and gene expression profiles.

Approach: We assessed brain age gap in depression using machine learning, identified divergent developmental trajectories of network topography, and conducted multivariate analyses of topography with clinical symptoms and gene expressions.

Results: Depression-related brain age gaps were primarily associated with abnormal topography of ventral attention and sensorimotor networks, revealing two distinct developmental trajectories linked to specific clinical and gene expression profiles.

Impact: Our results highlight the heterogeneity of neurodevelopment in depression and suggest potential underlying molecular mechanisms, providing deeper insights into the disorder’s progression and supporting the identification of biomarkers for precise diagnosis and treatment.

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