Mapping the trajectory of psychotic symptoms and their interaction with antipsychotic treatment: a longitudinal network intervention study

Antipsychotic efficacy in schizophrenia spectrum disorders (SSD) is commonly evaluated using static measures that fail to capture the dynamic evolution of symptoms and the unfolding impact of treatment over time. Network Intervention Analysis (NIA) is a novel approach that models pharmacological treatments as active nodes within longitudinal symptom networks, capturing both direct and indirect treatment effects. This study aimed to investigate how the receptor-binding profile of antipsychotics influence symptom trajectories over six weeks. NIA was used to characterise the evolving impact of treatment with muscarinic antagonists, serotonergic/dopaminergic antagonists, and adrenergic agents with low dopaminergic antagonism within dynamic symptom networks. We hypothesised that NIA would reveal distinct patterns of symptom change, reflecting the pharmacodynamic mechanisms specific to each drug class. Forty-seven patients with SSD underwent baseline assessments including neuropsychological tests and five symptom rating scales from the Manual for the Assessment and Documentation of Psychopathology in Psychiatry (AMDP). They were then followed weekly for six additional weeks evaluating them with the AMDP-based symptom ratings, providing a comprehensive, standardised, and fine-grained measure of psychopathological changes. Muscarinic antagonists initially targeted self-disorder, then shifted to delusions, reducing symptom interconnectivity and increasing network resilience. Serotonergic/dopaminergic antagonists primarily influenced hallucinations but showed a late stage rebound, with increased network density and reduced treatment influence. Adrenergic agents exhibited a stabilising effect, preserving network structure with minimal symptom reduction. These findings demonstrate the utility of NIA in capturing the temporal dynamics of antipsychotic effects based on receptor affinity, supporting the development of phase-specific, network-informed, and personalised interventions.