The proposed work will consist of four key components. The investigators will collect data from the class of 2023 in each of their remaining years at college, producing (i) longitudinal data that will allow them to probe the long-term mental health effects of connections made during the transition to college, as well as mental health issues and resilience in the wake of the COVID-19 pandemic as a function of social connectedness. The investigators will leverage this rich dataset to build (ii) prospective quantitative models to predict mental health outcomes later in college based on a range of social factors measured early in college. They will further collect (iii) a longitudinal replication cohort, the class of 2024, allowing to establish the robustness of their initial findings across samples. Finally, the investigators will add (iv) a neuroimaging component to this dataset in a subset of the class of 2024. This will allow them to examine brain markers of connectedness, and add them to a prospective model, providing a novel integration across brain, behavior, and community levels of analysis, and a synthetic assessment of social factors in long-term mental health.
The central goal of this project is to produce a novel, precise, and comprehensive account of social factors in young adult mental health - using a novel combination of network nominations, ecological momentary assessment, and neuroimaging methods. To that end, the investigators will collect data from two successive classes of college undergraduates (i.e., classes of 2023 and 2024) over the course of their collegiate tenure.