Distress as a bridge to suicidality in schizophrenia spectrum disorders: a network-based intervention simulation study

Schizophrenia spectrum disorders (SSD) are associated with a markedly elevated risk of suicide, yet the symptom-level mechanisms linking psychopathology to suicidality remain incompletely understood. Network-based models provide a data-driven framework for characterizing symptom interrelations, identifying candidate intervention targets, and generating hypotheses about potential downstream effects. However, the Positive and Negative Syndrome Scale (PANSS), the gold-standard assessment of schizophrenia symptoms, has primarily been used as a static rating tool rather than as a dynamic simulation framework for intervention prioritization. Here, we developed a network-based simulation framework using PANSS to identify symptom domains most strongly associated with suicidality and to evaluate how targeted modulation of these symptoms may relate to suicidal ideation. Symptom networks reproduced a robust five-domain PANSS structure (Positive, Negative, Cognitive Impairment, Impulsive-Hostile, and Distress). Across item- and cluster-level analyses, distress-related symptoms (anxiety, depression, guilt, and tension) showed the most consistent associations with suicidality and emerged as key bridge symptoms. Analyses stratified by lifetime suicide attempt history revealed distinct networks and simulation responses across risk strata. Simulation-based modulation of the Distress domain was associated with attenuated network connectivity and lower predicted suicidal ideation. While derived from simulations, these findings highlight affective distress as a clinically relevant symptom pathway linked to suicide risk in SSD and demonstrate how network-informed digital simulations may support the prioritization of symptom targets. Future studies, particularly randomized controlled trials, will be critical to determine whether targeting distress leads to measurable reductions in suicidality, with simulation-based analyses complementing observational evidence by informing hypothesis generation and trial design.