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A network model of depressive and anxiety symptoms: a statistical evaluation.
Cai, Hong; Chen, Meng-Yi; Li, Xiao-Hong; Zhang, Ling; Su, Zhaohui; Cheung, Teris; Tang, Yi-Lang; Malgaroli, Matteo; Jackson, Todd; Zhang, Qinge; Xiang, Yu-Tao.
Afiliación
  • Cai H; Unit of medical psychology and behavior medicine, school of public health, Guangxi Medical University, Nanning, Guangxi, China.
  • Chen MY; Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China.
  • Li XH; Centre for Cognitive and Brain Sciences, University of Macau, Macao SAR, China.
  • Zhang L; Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China.
  • Su Z; 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.
  • Cheung T; School of Public Health, Southeast University, Nanjing, China.
  • Tang YL; School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China.
  • Malgaroli M; Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA.
  • Jackson T; Atlanta VA Medical Center, Atlanta, GA, USA.
  • Zhang Q; Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA.
  • Xiang YT; Department of Psychology, University of Macau, Macao SAR, China.
Mol Psychiatry ; 29(3): 767-781, 2024 Mar.
Article en En | MEDLINE | ID: mdl-38238548
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ansiedad / Depresión Tipo de estudio: Diagnostic_studies / Prognostic_studies / Systematic_reviews Límite: Female / Humans / Male Idioma: En Revista: Mol Psychiatry Asunto de la revista: BIOLOGIA MOLECULAR / PSIQUIATRIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ansiedad / Depresión Tipo de estudio: Diagnostic_studies / Prognostic_studies / Systematic_reviews Límite: Female / Humans / Male Idioma: En Revista: Mol Psychiatry Asunto de la revista: BIOLOGIA MOLECULAR / PSIQUIATRIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido