Progressive social withdrawal in trauma-exposed older adolescents and young adults: neurocircuitry predictors

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Social withdrawal is a transdiagnostic phenotype that is strongly associated with detrimental physical and mental health outcomes across the lifespan. As quantified using structural features of individuals’ social networks, social withdrawal is associated with 60-70% increased mortality and a threefold increase in suicide risk. Recent findings document associations between trauma-related psychopathology and altered social network structural features, including size, density, diversity, and embeddedness. Critically, because the transition to adulthood (age 16-20) is a period of rapid expansion in social networks, forms of psychopathology that produce social withdrawal may confer particular risk for poor outcomes during this developmental stage. Thus, the transition to adulthood is a key developmental period for the evolution of social withdrawal in trauma- exposed populations. Recent literature indicates that social reward processing may drive social network size and complexity, but the role of disrupted social reward functioning in driving progressive social withdrawal following trauma exposure is poorly understood. Using an innovative longitudinal design including digital phenotyping of social behavior, the proposed study will investigate activity and connectivity within brain regions that bidirectionally code for social approach and avoidance, including the basolateral amygdala (BLA), ventromedial prefrontal cortex (VMPFC), and nucleus accumbens (NAcc). The hypothesis is that BLA-VMPFC- NAcc functional connectivity will prospectively predict progressive social withdrawal during the transition to adulthood following interpersonal trauma exposure. We will enroll 120 trauma-exposed participants (ages 16- 20) who endorse posttraumatic and/or depressive symptoms, and 60 healthy controls. Trauma-exposed participants will be stratified for baseline self-reported social anhedonia. We obtain baseline social withdrawal measures, including active self-reports of social interaction and passive smartphone based phenotyping of activity via accelerometer, GPS, and call/text metadata. Participants will complete an fMRI scan to obtain measures of BLA-VMPFC-NAcc connectivity, followed by one year of digital phenotyping using active and passive data to measure social withdrawal. Based on our extensive preliminary data, we hypothesize that: 1) baseline social withdrawal will be associated with baseline BLA-VMPFC-NAcc connectivity; (2) baseline BLA- VMPFC-NAcc connectivity will prospectively predict progressive social withdrawal over the course of a 12- month follow-up; (3) predictive models of social withdrawal that include BLA-VMPFC-NAcc connectivity will outperform alternate models. The proposed integrated approach (fMRI, digital phenotyping) will identify specific circuitry associated with progressive social withdrawal during the transition to adulthood, and will develop predictive algorithms that forecast social withdrawal trajectories based on baseline connectivity within BLA- VMPFC-NAcc circuitry. Ultimately this work could lead to the development of targeted interventions (e.g. neurofeedback), and may shift the field toward reward-circuitry accounts of social withdrawal.