Regional Structural-Functional Connectivity Coupling in Major Depressive Disorder Is Associated With Neurotransmitter and Genetic Profiles

Tongpeng Chu; Xiaopeng Si; Haizhu Xie; Heng Ma; Yinghong Shi; Wei Yao; Dong Xing; Feng Zhao; Fanghui Dong; Qun Gai; Kaili Che; Yuting Guo; Danni Chen; Dong Ming; Ning Mao

doi:10.1016/j.biopsych.2024.08.022

Regional Structural-Functional Connectivity Coupling in Major Depressive Disorder Is Associated With Neurotransmitter and Genetic Profiles

Biol Psychiatry. 2025 Feb 1;97(3):290-301. doi: 10.1016/j.biopsych.2024.08.022. Epub 2024 Aug 30.

Authors

Tongpeng Chu¹, Xiaopeng Si², Haizhu Xie³, Heng Ma³, Yinghong Shi³, Wei Yao⁴, Dong Xing³, Feng Zhao⁵, Fanghui Dong³, Qun Gai³, Kaili Che³, Yuting Guo⁶, Danni Chen⁶, Dong Ming⁷, Ning Mao⁸

Affiliations

¹ Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.
² Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China. Electronic address: xiaopengsi@tju.edu.cn.
³ Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.
⁴ Department of Neurology, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, Shandong, China.
⁵ School of Computer Science and Technology, Shandong Technology and Business, University, Yantai, Shandong, China.
⁶ School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, China.
⁷ Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin University, Tianjin, China. Electronic address: richardming@tju.edu.cn.
⁸ Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases, Yantai Yuhuangding Hospital, Yantai, Shandong, China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China. Electronic address: maoning@pku.edu.cn.

PMID: 39218135
DOI: 10.1016/j.biopsych.2024.08.022

Abstract

Background: Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms that underlie regional SC-FC coupling patterns are not well understood.

Methods: We enrolled 182 patients with MDD and 157 healthy control participants and quantified the intergroup differences in regional SC-FC coupling. Extreme gradient boosting (XGBoost), support vector machine, and random forest models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression.

Results: We observed increased regional SC-FC coupling in the default mode network (t₃₃₇ = 3.233) and decreased coupling in the frontoparietal network (t₃₃₇ = -3.471) in patients with MDD compared with healthy control participants. XGBoost (area under the receiver operating characteristic curve = 0.853), support vector machine (area under the receiver operating characteristic curve = 0.832), and random forest (p < .05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of 4 neurotransmitters (p < .05) and expression maps of specific genes. These enriched genes were implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on 2 brain atlases.

Conclusions: This work enhances our understanding of MDD and paves the way for the development of additional targeted therapeutic interventions.

Keywords: Biological mechanisms; Default Mode Network; Frontoparietal Network; Major Depressive Disorder; Prediction; Regional.

MeSH terms

Adult
Brain* / diagnostic imaging
Brain* / physiopathology
Case-Control Studies
Connectome
Default Mode Network / diagnostic imaging
Default Mode Network / physiopathology
Depressive Disorder, Major* / genetics
Depressive Disorder, Major* / physiopathology
Female
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Nerve Net / diagnostic imaging
Nerve Net / physiopathology
Neurotransmitter Agents* / metabolism
Support Vector Machine
Young Adult

Substances

Neurotransmitter Agents