Background
Network analysis offers a novel framework for understanding the dynamic interconnections among symptoms, moving beyond traditional approaches to identify central symptoms that drive illness courses and treatment response. Recent studies have used this method to examine how symptoms group together and influence one another in unipolar depression; however, its application to bipolar disorder (BD) remains limited.

Objectives
This review systematically examines literature that applied network analysis methodology to BD to investigate central symptoms and their interrelationships within this condition.

Methods
A systematic search of PubMed, Web of Science, PsycINFO, Embase, and Scopus databases was conducted from inception to January 2026 to identify studies that applied network analysis to examine mood symptom centrality in individuals with BD. 

Results
Eleven studies met the inclusion criteria, encompassing diverse BD populations including young adults, and mixed-age samples across different illness phases. Across depressive symptom networks, depressed mood, low energy, and negative self-concept emerged as central nodes, while high energy, pressured speech, and elevated self-esteem were most central in networks of manic symptoms. Methodological and reporting heterogeneity, including variations in network estimation techniques, sample characteristics, and symptom assessment instruments, limited the comparability of findings. 

Conclusion
These findings advance understanding of central symptoms and network structures in BD, revealing consistent patterns across depressive and manic symptom networks. Identifying key symptom patterns and their interconnections may inform future symptom-targeted research and enhance understanding of symptom dynamics in BD.