Gene expression profiles associated with gray matter and dynamic connectivity disruptions in major depressive disorder

Danni Chen; Qinghe Li; Yinuo Xiao; Yuting Guo; Wanying Jing; Kaili Che; Fanghui Dong; Heng Ma; Feng Zhao; Haifeng Lian; Xicheng Song; Chao Ren; Tongpeng Chu; Ning Mao; Peiyuan Wang

doi:10.1016/j.jad.2025.119697

Gene expression profiles associated with gray matter and dynamic connectivity disruptions in major depressive disorder

J Affect Disord. 2025 Jun 17:119697. doi: 10.1016/j.jad.2025.119697. Online ahead of print.

Authors

Danni Chen¹, Qinghe Li², Yinuo Xiao³, Yuting Guo¹, Wanying Jing⁴, Kaili Che⁴, Fanghui Dong⁴, Heng Ma⁴, Feng Zhao⁵, Haifeng Lian⁶, Xicheng Song⁷, Chao Ren⁷, Tongpeng Chu⁸, Ning Mao⁹, Peiyuan Wang¹⁰

Affiliations

¹ Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264100, Shandong, PR China.
² Medical Imaging Department of Linyi People's Hospital, Linyi 276000, Shandong, PR China.
³ Jining Medical University, 133 Hehua Road, Tai Baihu New District, Jining 272067, Shandong, PR China.
⁴ Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, Shandong, PR China.
⁵ School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264000, Shandong, PR China.
⁶ Department of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical University, No. 717, Jinbu street, Muping District, Yantai 264003, PR China.
⁷ Shandong Provincial Key Laboratory of Neuroimmune Interaction and Regulation, Yantai 26400, Shandong, PR China.
⁸ Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, Shandong, PR China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Yantai 264000, Shandong, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, Shandong, PR China; Shandong Provincial Key Laboratory of Neuroimmune Interaction and Regulation, Yantai 26400, Shandong, PR China. Electronic address: chutongpeng@163.com.
⁹ Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, Shandong, PR China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Yantai 264000, Shandong, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, Shandong, PR China; Shandong Provincial Key Laboratory of Neuroimmune Interaction and Regulation, Yantai 26400, Shandong, PR China. Electronic address: maoning@pku.edu.cn.
¹⁰ Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264100, Shandong, PR China. Electronic address: wangpeiyuan1640@163.com.

PMID: 40553733
DOI: 10.1016/j.jad.2025.119697

Abstract

Purpose: To identify biomarkers linking molecular mechanisms to macroscale brain changes in major depressive disorder (MDD) by integrating multimodal neuroimaging, transcriptomics, and machine learning.

Methods: First, T1-weighted and resting-state functional magnetic resonance imaging (rs-fMRI) data from 160 first-episode, drug-naïve MDD patients and 119 age-/sex-matched healthy controls (HCs) were analyzed. Voxel-based morphometry (VBM) and dynamic functional connectivity (dFC) analyses were conducted to generate case-control t-maps. Next, minimum Redundancy Maximum Relevance (mRMR) was applied for feature selection, followed by support vector machine (SVM) modeling for diagnostic classification and symptom prediction. Subsequently, partial least squares (PLS) regression was employed to examine the link between case-control t-maps and gene expression. Finally, the findings were validated using two independent cohorts and alternative brain atlases.

Results: Patients with MDD exhibited gray matter reductions in bilateral inferior frontal gyri and dFC disruptions between default mode and sensorimotor networks (all P_FDR < 0.05). The models classifier built on multimodal imaging features achieved high diagnostic performance (AUC = 0.92 [0.80-0.97], sensitivity = 0.84, specificity = 0.87, accuracy = 0.83) and accurately predicted symptom severity (HAMD: r = 0.614, NGASR: r = 0.581, MoCA: r = 0.707; all P_FDR < 0.05). Neuroimaging-transcriptome integration identified 884 genes associated with structural-functional alterations (|Z| > 3, P_FDR < 0.05), enriched in protein localization/trafficking, RNA metabolism, and chromatin organization. Replication analyses confirmed the model's robust generalizability.

Conclusion: Multimodal imaging and transcriptomic integration revealed reliable biomarkers and underlying molecular pathways, supporting personalized diagnosis and intervention in MDD.

Keywords: Dynamic functional connectivity; Major depressive disorder; Prediction; Transcriptomics; Voxel based morphometry.