PROJECT SUMMARY/ABSTRACT People with serious mental illness (SMI; i.e., schizophrenia, schizoaffective disorder, bipolar disorder, and major depressive disorder with psychotic features) are at an increased risk of suicidal ideation and behavior as compared to the general population. Despite this, there is still relatively little understanding of the mechanisms that contribute to suicidal ideation in SMI, and people with SMI are excluded in almost 80% of biobehavioral clinical trials. Recent research indicates that aberrant social cognitive emotion processing, in particular over- attribution of threat, is common in psychosis and is associated with suicide ideation and behavior. Our prior work indicates that perceived burdensomeness and thwarted belongingness can be measured validly with ecological momentary assessment (EMA) in SMI and relate to suicidal ideation in this population. However, the relationships social cognitive abilities and biases, which can be objectively measured, have not been explored in a dynamic fashion. Social cognitive interventions are effective in psychosis but to date have not been evaluated as a component of suicide prevention. This project will evaluate real-time dynamic and longitudinal linkages between social cognitive abilities and biases measured by a validated mobile social cognitive testing tool, in a large sample of people SMI stratified by suicide ideation. Furthermore, the candidate will apply a novel computational technique, network analysis adapted for EMA data, to understand time-varying factors affecting these modifiable constructs related to suicide in psychosis. Network analyses augment traditional within-person analyses with EMA data to evaluate complex time interdependencies among variables, including density and time-lagged relationships. This project will utilize existing EMA, mobile cognitive, and longitudinal in-lab data collected from an NIMH supported study that includes 304 people with SMI. This proposed F31 project aims to understand the individual time-varying relationships between over-attribution of threat, social variables, perceived burdensomeness, and thwarted belongingness using traditional mixed-effects models, and utilize network analyses to evaluate the interdependencies among these constructs and symptoms between people with and without suicidal ideation. Then, this project aims to examine how these baseline associations and networks predict ideation trajectories and suicidal behavior at 12-month follow-up. In conjunction, the training opportunities afforded by the F31 funding mechanism will facilitate the applicant’s long-term goal of becoming an independent investigator in the psychological and social cognitive contributors to suicide in psychotic disorders, capable of applying sophisticated computational approaches to inform new translational interventions including those delivered in real time through digital health. This proposal is consistent with NIMH strategy aim 2.2.A. by eva...