PROJECT SUMMARY/ABSTRACT Social cognitive deficits are prevalent in schizophrenia spectrum disorders (SSD) and significantly contribute to poor community functioning. Since most people living with SSD are in their mid- and late-life and social cognitive deficits often persist or even worsen over the course of illness, there is a critical need to develop effective interventions for mid-late life SSD. Characterizing social cognitive deficits and their changes across age and identifying neural markers are prerequisites to developing efficient identification strategies and targeted neurobiological treatments. In response to RFA-MH-22-270 “Schizophrenia and Related Disorders during Mid- to Late-life,” this project aims to advance knowledge in social cognition—one of the identified priority research areas. We will recruit a large sample (n = 192; 50% female) of SSD participants in their mid- late life (age 35 to 75). Additionally, 48 early-psychosis (age 18-34) and 120 age-matched non-psychiatric participants will serve as clinical and healthy comparisons, respectively. Participants will complete assessments of psychiatric phenotypes, neurocognition, and community functioning. Social cognition will be assessed using a comprehensive battery capturing low- to high-level processes. A subset (75%) of the participants will additionally undergo EEG during social cognitive tasks to determine the theta-band neural oscillatory features underlying social cognitive deficits. The specific aims of this project are threefold: 1) Delineate the age trajectories of social cognitive deficits in mid-late life SSD; 2) Evaluate theta-band neural oscillatory features as neural markers of social cognitive deficits in mid-late life SSD; and 3) Parse heterogeneity based on neural oscillatory signatures in mid-late life SSD. Successfully completing these specific aims will advance our understanding of the course and neural mechanisms of social cognition in mid- late life SSD, advancing NIMH’s Strategic Objective Objectives 2.1 (characterize the trajectories of cognitive and affective processes across the lifespan), 2.2 (identify behavioral and biological markers of mental illnesses), and 1.3 (identify neural mechanisms contributing to mental illnesses). The findings will guide identification and personalized treatment strategies, providing critical knowledge to determine who and how to intervene.