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  • Systematic Review
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A network model of depressive and anxiety symptoms: a statistical evaluation

Molecular Psychiatry (2024)Cite this article

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Abstract

Background

Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.

Methods

A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.

Results

Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. ‘Sad mood’, ‘Uncontrollable worry’, and ‘Worrying too much’ were the most central symptoms, while ‘Sad mood’, ‘Restlessness’, and ‘Motor disturbance’ were the most frequent bridge centrality symptoms. In addition, the connection between ‘Sleep’ and ‘Fatigue’ was the most frequent edge for the depressive and anxiety symptoms network model.

Conclusion

Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

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Fig. 1
Fig. 2: Centrality ranking for network models of depressive and anxiety symptoms.
Fig. 3: Bridge centrality ranking for network models of depressive and anxiety symptoms.
Fig. 4

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Data availability

The data of the investigation are available in Tables and Figures of this systematic review.

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Acknowledgements

The authors are grateful to all participants and clinicians involved in this study.

Funding

The study was supported by STI2030-Major Projects(2021ZD0200600), Beijing High Level Public Health Technology Talent Construction Project (Discipline Backbone-01-028) and the University of Macau (MYRG2019-00066-FHS; MYRG2022-00187-FHS). MM’s research was supported by the National Institute of Mental Health (1K23MH134068).

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Author notes

  1. These authors contributed equally: Hong Cai, Meng-Yi Chen, Xiao-Hong Li, Ling Zhang.

Authors and Affiliations

  1. Unit of medical psychology and behavior medicine, school of public health, Guangxi Medical University, Nanning, Guangxi, China

    Hong Cai

  2. Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China

    Meng-Yi Chen & Yu-Tao Xiang

  3. Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China

    Meng-Yi Chen & Yu-Tao Xiang

  4. Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China

    Xiao-Hong Li

  5. Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China

    Ling Zhang & Qinge Zhang

  6. School of Public Health, Southeast University, Nanjing, China

    Zhaohui Su

  7. School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China

    Teris Cheung

  8. Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA

    Yi-Lang Tang

  9. Atlanta VA Medical Center, Atlanta, GA, USA

    Yi-Lang Tang

  10. Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA

    Matteo Malgaroli

  11. Department of Psychology, University of Macau, Macao SAR, China

    Todd Jackson

Authors
  1. Hong Cai
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  2. Meng-Yi Chen
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  4. Ling Zhang
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  10. Qinge Zhang
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  11. Yu-Tao Xiang
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Contributions

Study design: HC, QZ, Y-TX. Data collection, analysis and interpretation: HC, M-YC, X-HL, LZ, ZS, TC, Y-LT, MM. Drafting of the manuscript: HC, Y-TX. Critical revision of the manuscript: TJ. Approval of the final version for publication: all co-authors.

Corresponding authors

Correspondence to Qinge Zhang or Yu-Tao Xiang.

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Cai, H., Chen, MY., Li, XH. et al. A network model of depressive and anxiety symptoms: a statistical evaluation. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-023-02369-5

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