From Symptoms to Structures: A Systematic Review and Meta-Analysis of Major Depressive Symptom Networks
Abstract
Major depressive disorder (MDD) is a leading condition examined using network analysis. However, meta-analytic evidence is limited for MDD symptom network studies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines, we searched Embase, MEDLINE, PsycINFO, CINAHL and bibliographies of previous literature for articles that have produced MDD symptom networks. After screening, 104 articles (N = 875,852) were included in this review, consisting of 85 cross-sectional and 19 temporal network analysis studies. A random-effects Meta-analytic Gaussian Network Aggregation (MAGNA) of MDD symptom networks was estimated based on available data. Despite a pronounced variation in MDD measures and sample demographics, findings consistently showed that depressed mood and fatigue emerged as the most central symptoms. Collectively, this systematic review and meta-analysis underscores network analysis as a technique for understanding MDD symptom dynamics and one that has the potential to improve psychological interventions. Ongoing efforts toward intensive longitudinal datasets and improved reporting practices represent two key avenues for MDD network analysis research.
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BibTeX
@misc{strathdee2026from,
title = {{From Symptoms to Structures: A Systematic Review and Meta-Analysis of Major Depressive Symptom Networks}},
author = {Charles Jack Strathdee and Phuong Linh Dang and isabel Krug and Litza Kiropoulos and Charles Benjamin Malpas},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/kt5wz_v2},
url = {https://osf.io/preprints/psyarxiv/kt5wz_v2/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - From Symptoms to Structures: A Systematic Review and Meta-Analysis of Major Depressive Symptom Networks
AU - Charles Jack Strathdee
AU - Phuong Linh Dang
AU - isabel Krug
AU - Litza Kiropoulos
AU - Charles Benjamin Malpas
PY - 2026
DA - 2026/10/28
PB - PsyArXiv
DO - 10.31234/osf.io/kt5wz_v2
UR - https://osf.io/preprints/psyarxiv/kt5wz_v2/
AB - Major depressive disorder (MDD) is a leading condition examined using network analysis. However, meta-analytic evidence is limited for MDD symptom network studies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines, we searched Embase, MEDLINE, PsycINFO, CINAHL and bibliographies of previous literature for articles that have produced MDD symptom networks. After screening, 104 articles (N = 875,852) were included in this review, consisting of 85 cross-sectional and 19 temporal network analysis studies. A random-effects Meta-analytic Gaussian Network Aggregation (MAGNA) of MDD symptom networks was estimated based on available data. Despite a pronounced variation in MDD measures and sample demographics, findings consistently showed that depressed mood and fatigue emerged as the most central symptoms. Collectively, this systematic review and meta-analysis underscores network analysis as a technique for understanding MDD symptom dynamics and one that has the potential to improve psychological interventions. Ongoing efforts toward intensive longitudinal datasets and improved reporting practices represent two key avenues for MDD network analysis research.
ER -