ABSTRACT Intrinsic functional connectivity magnetic resonance imaging is a powerful tool to study the organization of functional networks (FNs) in the human brain. Rich and accumulating evidence demonstrates that FNs undergo predictable normative development in youth, and that abnormal development is associated with diverse psychopathology. Recent work based on advances in image analytics has established that FNs are in fact person-specific. When paired with large-scale neuroimaging datasets, person-specific FNs provide unprecedented translational opportunities for the development of new diagnostics that could guide personalized treatments for neuropsychiatric illnesses. However, the translational promise of person-specific FNs is at present hindered by several obstacles. First, current methods compute personalized FNs at a specific scale, despite clear evidence that the brain is a multi-scale system with a hierarchical functional organization. Second, to enforce correspondence across different subjects personalized FNs are typically computed under certain constraints, which may yield biased results. Third, deep learning has achieved mixed success in neuroimaging data analysis partially due to the fact that ad-hoc network architecture is typically adopted and feature learning capability is often deprived by adopting pre-engineered rather than learned features. Fourth, to correct site effects of neuroimaging measures from multiple datasets of large-scale neuroimaging studies current methods typically attempt to harmonize data prior to statistical modeling, resulting in loss of valuable information. Fifth, longitudinal neuroimaging and clinical data are increasingly available, but effective analytic tools for longitudinal data are scarce. Last but not least, deep learning algorithms have been developed to analyze fcMRI data but are often released as poorly documented source code, limiting both reproducibility and adoption by translational researchers. In this application, we build on the success of the prior award period to address these limitations by developing, validating, and disseminating tools that characterize brain functional organization at an individual subject level. We will leverage complementary large-scale studies of brain development to validate our methods and delineate how abnormal development of FNs is associated with major dimensions of psychopathology in youth, including depression, anxiety, psychosis, and ADHD-spectrum symptoms. Specifically, we will develop novel methods to 1) accurately identify bias-free personalized FNs with a multiscale hierarchical organization; 2) robustly predict psychiatric symptom dimensions using personalized FNs with optimized deep neural network architecture and integrated site-effect correction, and 3) effectively model longitudinal data of FNs to create predictive models of psychopathology. These tools will be released in a freely available, containerized software package to ensure frictionless portability across computing platforms and full reproducibility.

PROJECT NARRATIVE We will develop novel tools for modeling personalized functional networks and predicting psychopathology in youth.