PROJECT: JTC2023 - Resilience: ResilNet

This project aims to provide a major advance in our understanding of vulnerability and resilience to adverse mental health outcomes by taking a multi-level measurement and analytical approach that will uncover moderators and mechanisms of resilience that can then be translated to scalable interventions to improve mental health. The project brings together groups with previously established collaborations, diversity of methodological expertise, established large datasets (both cross-sectional and longitudinal) including multiple patient and at-risk cohorts, as well as existing recruitment infrastructure and links with patient advocacy groups to facilitate the acquisition of new data to complement the analysis of existing cohorts. The considerable translational value offered by these team attributes ensures we can feasibly test neural circuit and cognitive marker hypotheses of resilience vs. vulnerability, as well as implement data-driven machine-learning-assisted analyses of resilience proxies, in both existing data and newly recruited (pilot) cohorts of people with environmental challenges. Outcomes will include comprehensive analyses of neural circuit level mechanisms of resilience, identification of biological signatures (based on machine learning / network analyses) and translation into at-risk cohorts (affective and psychotic disorder patients with high/low resilience, refugee populations, earthquake survivors, and Covid patients with mental health impairments).