Longitudinal symptom network structure in first-episode psychosis: A possible marker for remission

Yan Hong Piao, Je Yeon Yun, Thong Ba Nguyen, Woo Sung Kim, Jing Sui, Nam In Kang, Keon Hak Lee, Seunghyong Ryu, Sung Wan Kim, Bong Ju Lee, Jung Jin Kim, Je Chun Yu, Kyu Young Lee, Seung Hee Won, Seung Hwan Lee, Seung Hyun Kim, Shi Hyun Kang, Euitae Kim, Young Chul Chung

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Background Network approach has been applied to a wide variety of psychiatric disorders. The aim of the present study was to identify network structures of remitters and non-remitters in patients with first-episode psychosis (FEP) at baseline and the 6-month follow-up. Methods Participants (n = 252) from the Korean Early Psychosis Study (KEPS) were enrolled. They were classified as remitters or non-remitters using Andreasen's criteria. We estimated network structure with 10 symptoms (three symptoms from the Positive and Negative Syndrome Scale, one depressive symptom, and six symptoms related to schema and rumination) as nodes using a Gaussian graphical model. Global and local network metrics were compared within and between the networks over time. Results Global network metrics did not differ between the remitters and non-remitters at baseline or 6 months. However, the network structure and nodal strengths associated with positive-self and positive-others scores changed significantly in the remitters over time. Unique central symptoms for remitters and non-remitters were cognitive brooding and negative-self, respectively. The correlation stability coefficients for nodal strength were within the acceptable range. Conclusion Our findings indicate that network structure and some nodal strengths were more flexible in remitters. Negative-self could be an important target for therapeutic intervention.

Original languageEnglish
Pages (from-to)3193-3201
Number of pages9
JournalPsychological medicine
Issue number14
StatePublished - 16 Oct 2022


  • First-episode psychosis
  • network analysis
  • prediction
  • remission


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