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Functional magnetic resonance imaging and machine learning in the application of brain network mechanisms and diagnosis and treatment of depression
WANG Siqi  SUN Siyu  ZHU Haijing  MI Weifeng  GAO Yujun  CUI Minghu 

Cite this article as: WANG S Q, SUN S Y, ZHU H J, et al. Functional magnetic resonance imaging and machine learning in the application of brain network mechanisms and diagnosis and treatment of depression[J]. Chin J Magn Reson Imaging, 2025, 16(10): 106-113. DOI:10.12015/issn.1674-8034.2025.10.017.


[Abstract] Depression is a prevalent and severe mental disorder characterized by a complex pathogenesis involving an interplay of genetic, environmental, and neurobiological factors. An accurate understanding of its pathogenesis and the implementation of precise diagnostic and therapeutic strategies are crucial. The advancement of functional magnetic resonance imaging (fMRI) and machine learning algorithms has introduced novel perspectives and methodologies for depression research, demonstrating significant potential in elucidating brain network mechanisms and facilitating diagnosis and treatment. However, this field continues to face numerous challenges, including significant data heterogeneity, insufficient standardization across multiple centers, limited investigation into the dynamic properties of brain networks, and the absence of established pathways for clinical translation. This paper systematically reviews the current research status of fMRI and machine learning in elucidating the mechanisms of brain networks in depression, as well as their clinical applications. It further highlights that future efforts should focus on standardizing multicenter data acquisition and processing, integrating multimodal neuroimaging information, and employing advanced models such as dynamic graph neural networks to capture the temporal evolution of brain networks. The ultimate goal is to provide a solid theoretical foundation and forward-looking direction for overcoming current research bottlenecks and constructing a precision diagnosis and treatment system for depression based on brain network analysis.
[Keywords] depression；functional magnetic resonance imaging；magnetic resonance imaging；machine learning；brain networks；diagnosis and treatment

WANG Siqi1, 2, 3   SUN Siyu1, 2, 4   ZHU Haijing1   MI Weifeng3*   GAO Yujun4*   CUI Minghu1*  

1 Department of Psychiatry, Binzhou Medical University Hospital, Binzhou 256603, China

2 Binzhou Medical University, Binzhou 256603, China

3 Institute of Mental Health, Peking University, Key Laboratory of Mental Health, National Health Commission of the People's Republic of China (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China

4 Department of Psychiatry, Wuchang Hospital Affiliated to Wuhan University of Science and Technology, Wuhan 430063, China

Corresponding author: MI W F, E-mail: weifengmi@bjmu.edu.cn GAO Y J, E-mail: Yujun_Gao@whu.edu.cn CUI M H, E-mail: 825724247@qq.com

Conflicts of interest   None.

Received  2025-07-01
Accepted  2025-10-08
DOI: 10.12015/issn.1674-8034.2025.10.017
Cite this article as: WANG S Q, SUN S Y, ZHU H J, et al. Functional magnetic resonance imaging and machine learning in the application of brain network mechanisms and diagnosis and treatment of depression[J]. Chin J Magn Reson Imaging, 2025, 16(10): 106-113. DOI:10.12015/issn.1674-8034.2025.10.017.

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