PROJECT SUMMARY Almost all brain imaging studies now collect multiple imaging modalities, in an effort to derive measures of both structure and function from diverse imaging sequences. While quantitative data scientists have focused on machine learning approaches for predicting outcomes using multi-modal imaging, rigorous statistical methods for examining the relationship between imaging modalities have lagged behind. At present, the lack of statistical methodologies for assessing inter-modal coupling (IMCo) has left investigators with ad hoc solutions that lack statistical power and are prone to type I error, posing a threat to scientific rigor and reproducibility. In this application, we propose robust methods that leverage subject-specific measurements and use nonlinear modeling to address complex relationships in brain maps or networks, while accounting for important covariates (Aim 1). Furthermore, we will develop powerful approaches for assessing whether effects of interest (e.g., psychopathology, development) are enriched within brain networks (Aim 2). Assessment of this coupling between statistical associations and brain networks will capitalize upon tools from statistical genomics (e.g., gene set enrichment analysis) to provide principled methods for conducting enrichment analyses using high- dimensional, personalized brain networks. Finally, we will use these tools to delineate how trans-diagnostic executive dysfunction in youth with mental illness is related to abnormalities in structure-function coupling within brain networks (Aim 3). To do this, we will leverage three massive data resources: the Philadelphia Neurodevelopmental Cohort (PNC; n=1,601), the Healthy Brain Network (n=3,200), and the Human Connectome-Development (HCP-D; n=1,300) study Taken together, the proposed work builds upon the notable success in the first project period, promising to yield rigorous and generalizable methods for delineating the relationships between complementary measures of brain structure and function.