PROJECT SUMMARY/ABSTRACT Adolescence is a key risk period for depression and anxiety, and adolescent-onset psychopathology is predictive of poorer health and life outcomes. Functional connectivity (FC) networks, which reflect trait-like brain functioning, have emerged as a promising biomarker to inform diagnosis and intervention of psychopathology. However, despite findings of FC differences between clinical and control groups, particularly in resting state (RS) networks, there has been minimal clinical translation. A key limitation is that qualitative network heterogeneity between- and within-individuals threatens the ability to draw inferences valid at the individual level. At the between-person level, qualitatively distinct networks across individuals limit the ability of group-averaged networks to validly reflect each individual. If group-level networks do not reflect individuals, behavioral inferences drawn from them will not apply to the individual. Our preliminary work and previous precision imaging studies provide evidence of this limitation by demonstrating FC network heterogeneity across individuals. However, the generalizability of group networks to individuals is yet to be tested empirically. This proposal will assess group-to-individual generalizability of adolescent RS networks and examine the ability of data-driven subgroups of similar individuals to address the limitation of heterogeneity (Aim 1). At the within-person level, FC variability across a single scan also threatens the validity of FC networks. If FC means and covariances vary with time across a scan (i.e., are not stationary), a static (time-invariant) network would not validly reflect network processes across the scan. This proposal will estimate dynamic FC to assess stationarity of adolescent RS networks to determine the validity of static networks (Aim 2). For both aims, this proposal will use a large 11–12-year-old sample from the Adolescent Brain Cognitive Development study. The proposal will determine the levels of data aggregation (group, subgroup, or individual) and time precision (static or dynamic) necessary for individual-level inferences from FC networks. Findings will be critical for the ultimate goal of using FC networks for clinical translation, which requires individual-level prediction. We will then use RS network features that are precise to individuals to predict depression and anxiety outcomes in adolescents (Aim 3), building a foundation with increased potential for clinical translation. The training plan to achieve the proposed project was developed in collaboration with a team of relevant experts that consists of formal coursework, workshops, and applied research activities. Specifically, I will develop expertise in fMRI research and analysis methods, machine learning approaches for subgroup identification, and idiographic (person- centered) methods necessary to complete the research proposal. Training will emphasize development toward my goal of b...