Resilience in adolescence following childhood maltreatment. From definition to neuroimaging markers.

Adolescents who have experienced childhood maltreatment (CM), as compared to those who have not, are at an increased risk of mental health disorders in adolescence and young adulthood. Given the dramatic increase and the chronic course of adolescent-onset mental health disorders, it is important to enhance our understanding of resilience, which consists in an individual's attainment of positive adaptation and competent functioning within the context of significant adversity. However, a consensus on how to define, detect and measure resilience is still lacking, and there is limited knowledge of the brain structures and processes that characterise resilient youths. The project has two main aims: (a) refining the boundaries of resilience (e.g., using the FemNAT-CD dataset (n=1840), explore the full range of effective degrees of freedom present in multi-domain measures of psychopathology, and exposure to CM); (b) applying the resilience construct to neuroimaging data from the FemNAT-CD sample (e.g., employ state-of-the-art machine learning methods for analysing and modelling neuroimaging data to identify structural and functional neuroimaging markers and networks that accompany resilience processes and phenotypes).The project will combine methods from psychology, neuroimaging, and computer/data science, which together will put this project at the forefront of resilience research.