Symptom Network Dynamics during Antipsychotic Treatment in First-Episode Psychosis

Area of Science:

• Psychiatry
• Computational Neuroscience
• Clinical Psychology

Background:

• Treatment response in first-episode psychosis (FEP) is highly variable.
• Symptom network theory posits that inter-symptom relationships influence treatment outcomes.
• Understanding these dynamics is crucial for identifying early markers of non-remission.

Purpose of the Study:

• To investigate differences in baseline and temporal symptom networks between remitters and non-remitters in FEP.
• To explore whether antipsychotic medications have differential effects on symptom networks.
• To identify potential early indicators of treatment response or non-response.

Main Methods:

• Comparison of baseline and temporal symptom networks (Positive and Negative Syndrome Scale - PANSS) in remitters (n=250) and non-remitters (n=196) from the OPTiMiSE trial.
• Gaussian graphical models for baseline networks and Cross-Lagged Panel Network analysis for temporal dynamics.
• Network Intervention Analysis to assess differential medication effects (amisulpride vs. olanzapine).

Main Results:

• Baseline symptom networks did not significantly differ between remitters and non-remitters.
• Temporal symptom networks showed minimal overlap and correlation in connection strengths between groups over time.
• Key symptom nodes (in/out-prediction) differed between outcome groups, suggesting distinct dynamic patterns.
• No robust evidence for antipsychotic-specific symptom targeting effects was found.

Conclusions:

• Dynamic changes in symptom networks over time, rather than static baseline structures, differentiate treatment response trajectories in first-episode psychosis.
• Temporal symptom network analysis may offer early markers for identifying individuals at risk of non-remission.
• Current findings suggest that antipsychotics may act through general mechanisms rather than specific symptom targeting.