114027
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
用于抑郁症预测的动态脑网络生物标志物：全球神经影像数据库的多队列分析
Authors Peng Y, Zhan Y , Zhang Q
Received 7 July 2025
Accepted for publication 20 December 2025
Published 31 December 2025 Volume 2025:18 Pages 2469—2494
DOI https://doi.org/10.2147/PRBM.S552134
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Professor Mei-Chun Cheung
Yangyao Peng,1,* Yuting Zhan,2,* Qing Zhang3
1Department of Cardiovascular Surgery, Zhongnan Hospital, Wuhan University, Wuhan, Hubei Province, 430072, People’s Republic of China; 2Department of Psychology, School of Education and Teach, Ningxia University, Yinchuan, Ningxia Province, 750021, People’s Republic of China; 3Department of Neurological Rehabilitation, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, 430072, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yangyao Peng, Email pengyyao@whu.edu.cn Qing Zhang, Email zqzqing@whu.edu.cn
Background: Major depressive disorder affects over 300 million people worldwide, yet clinicians lack reliable biomarkers for early identification of at-risk individuals. Recent advances in computational neuroscience suggest that dynamic brain network reorganization during emotional challenges may provide objective indicators of depression vulnerability that could enhance clinical practice.
Objective: To determine whether individual differences in dynamic brain network flexibility can predict depression onset and inform personalized clinical interventions through comprehensive analysis of large-scale neuroimaging databases.
Methods: We conducted a comprehensive analysis of 14,376 adults aged 18– 72 years from seven major international neuroimaging databases (UK Biobank, Human Connectome Project, ADNI, ABIDE, OpenfMRI, NITRC, and COINS) spanning 2018– 2024. Participants underwent standardized emotion regulation tasks during functional MRI with concurrent EEG. We quantified brain network flexibility using advanced graph-theoretical approaches and employed machine learning to identify distinct phenotypic patterns. Depression outcomes were assessed using validated clinical instruments over 30-month follow-up periods available in longitudinal sub-cohorts.
Results: Unsupervised machine learning revealed four distinct brain network flexibility phenotypes with remarkable cross-database consistency. The Rigid-Inflexible phenotype (18.7% prevalence) was associated with 4.3-fold higher depression incidence compared to Adaptive-Flexible individuals (38.7% vs 8.9%, P< 0.001). Network flexibility metrics predicted depression onset with 83.2% accuracy (AUC=0.89), significantly outperforming traditional risk models (AUC=0.69, P< 0.001). Network flexibility moderated stress-depression relationships (β=− 0.61, P< 0.001), with flexible individuals maintaining psychological resilience under high stress conditions while rigid individuals showed steep symptom escalation.
Conclusion: Dynamic brain network biomarkers represent a promising advance toward predictive, personalized psychiatry, pending external validation. These findings provide a neurobiological foundation for early intervention strategies and suggest novel therapeutic targets for depression prevention. The consistency across diverse global populations indicates potential universality of these mechanisms, supporting further clinical translation efforts.
Keywords: biomarkers, depression prediction, emotion regulation, machine learning, personalized medicine, neuroimaging
1Department of Cardiovascular Surgery, Zhongnan Hospital, Wuhan University, Wuhan, Hubei Province, 430072, People’s Republic of China; 2Department of Psychology, School of Education and Teach, Ningxia University, Yinchuan, Ningxia Province, 750021, People’s Republic of China; 3Department of Neurological Rehabilitation, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, 430072, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yangyao Peng, Email pengyyao@whu.edu.cn Qing Zhang, Email zqzqing@whu.edu.cn
Background: Major depressive disorder affects over 300 million people worldwide, yet clinicians lack reliable biomarkers for early identification of at-risk individuals. Recent advances in computational neuroscience suggest that dynamic brain network reorganization during emotional challenges may provide objective indicators of depression vulnerability that could enhance clinical practice.
Objective: To determine whether individual differences in dynamic brain network flexibility can predict depression onset and inform personalized clinical interventions through comprehensive analysis of large-scale neuroimaging databases.
Methods: We conducted a comprehensive analysis of 14,376 adults aged 18– 72 years from seven major international neuroimaging databases (UK Biobank, Human Connectome Project, ADNI, ABIDE, OpenfMRI, NITRC, and COINS) spanning 2018– 2024. Participants underwent standardized emotion regulation tasks during functional MRI with concurrent EEG. We quantified brain network flexibility using advanced graph-theoretical approaches and employed machine learning to identify distinct phenotypic patterns. Depression outcomes were assessed using validated clinical instruments over 30-month follow-up periods available in longitudinal sub-cohorts.
Results: Unsupervised machine learning revealed four distinct brain network flexibility phenotypes with remarkable cross-database consistency. The Rigid-Inflexible phenotype (18.7% prevalence) was associated with 4.3-fold higher depression incidence compared to Adaptive-Flexible individuals (38.7% vs 8.9%, P< 0.001). Network flexibility metrics predicted depression onset with 83.2% accuracy (AUC=0.89), significantly outperforming traditional risk models (AUC=0.69, P< 0.001). Network flexibility moderated stress-depression relationships (β=− 0.61, P< 0.001), with flexible individuals maintaining psychological resilience under high stress conditions while rigid individuals showed steep symptom escalation.
Conclusion: Dynamic brain network biomarkers represent a promising advance toward predictive, personalized psychiatry, pending external validation. These findings provide a neurobiological foundation for early intervention strategies and suggest novel therapeutic targets for depression prevention. The consistency across diverse global populations indicates potential universality of these mechanisms, supporting further clinical translation efforts.
Keywords: biomarkers, depression prediction, emotion regulation, machine learning, personalized medicine, neuroimaging