Dynamics of symptom network in patients with first-episode schizophrenia: Insight from the CNFEST project

Schizophrenia is a heterogeneous psychotic disorder. Recent theories have emphasized the importance of interactions among psychiatric symptoms in understanding the pathological mechanisms of schizophrenia. In the current study, we examined the symptom network in patients with first-episode schizophrenia (FES) at four time points during a six-month follow-up period.

In total, 565 patients with FES were recruited from the Chinese First-Episode Schizophrenia Trial (CNFEST) project. Clinical symptoms were measured using the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up (514 patients at one month, 429 at three months, and 392 at six months). We used a network analysis approach to estimate symptom networks with individual symptoms as nodes and partial correlation coefficients between symptoms as edges. A cross-lagged panel network (CLPN) model was used to identify predictive pathways for clinical symptoms.

We found stable and strongly connected edges in patients across the time points, such as links between delusions and suspiciousness/persecution (P1:P6), and emotional withdrawal and passive/apathetic social withdrawal (N2:N4). Emotional withdrawal (N2), poor rapport (N3), and passive/apathetic social withdrawal (N4) had high centrality estimates across all four time points. CLPN analysis showed that negative symptoms, including emotional withdrawal (N2), poor rapport (N3), and passive/apathetic social withdrawal (N4), and stereotyped thinking (N7) may have predictive effects for negative and general symptoms at follow-ups.

The symptom network of schizophrenia may be dynamic as treatment progresses. Negative symptoms remain the central and stable symptoms of schizophrenia. Negative symptoms may be potential therapeutic targets that predict other symptoms.