4.4 Review

Networks of major depressive disorder: A systematic review

Journal

CLINICAL PSYCHOLOGY REVIEW
Volume 85, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cpr.2021.102000

Keywords

Depression; Network analysis; Heterogeneity; Systematic review

Funding

  1. NYU Langone Clinical and Translational Science Institute
  2. American Foundation for Suicide Prevention [PRG-0-10419]
  3. NIH [R01MH091034]

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There has been a significant increase in network studies of Major Depressive Disorder, with 254 studies from 2010 to 2020 examined in a systematic review. Results showed substantial variability in study samples, depression measures, and network features, with Fatigue and Depressed Mood identified as the most central symptoms.
There has been a marked increase of network studies of Major Depressive Disorder (MDD). Despite rapidly growing contributions, their findings have yet to be systematically aggregated and examined. We therefore conducted a systematic review of depression network studies using PRISMA guidelines. A total of 254 clinical and population studies were collected from ISI?s Web of Science and PsycINFO, between January 2010 to May 2020. A total of 23 between-subject studies were included for review, resulting in 58 cross-sectional networks. To determine their most critical symptoms and their connections, we analyzed strength centrality rankings, and aggregated the most robust symptoms connections into a summary network. Results indicated substantial variability between study samples, depression measures, and network features. Fatigue and Depressed Mood were the most central symptoms, while Weight changes tended to have the weakest centrality. Depressed Mood and Fatigue formed two separated symptoms communities characterized by recurrent connections, with MoodAnhedonia as the most frequent edge of MDD. Network analysis informed our understanding of MDD, suggesting the critical role of Fatigue and Depressed Mood. The study?s findings are discussed in their clinical and methodological implications, including future directions for network studies of MDD.

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