PROJECT SUMMARY In the healthy brain, white matter connections coordinate the speed and reliability of communication among spatially distributed regions. The transmission of neural information with fine temporal precision is crucial for executive function (EF), defined as the coordination of attention, memory, and inhibitory control for goal-directed actions crucial for survival, health, and well-being. Notably, EF undergoes protracted development throughout youth, and the development of executive deficits is present in many psychiatric illnesses, including schizophrenia and ADHD. However, the degree to which transdiagnostic executive deficits arise due to abnormal development of white matter networks remains unknown. Existing neuroimaging studies of transdiagnostic executive deficits have been limited by 1) inadequate methods to assess neurodevelopmental patterns of spatially distributed brain regions, and 2) inadequate theories of neural communication at the level of macro-scale structural networks. Here we address the latter challenge by generalizing long-established theoretical principles and empirical observations of efficient neurotransmission at the microscale neuron level to the macroscale structural connectome by calculating transmission fidelity. Transmission fidelity models how efficiently a brain region embedded in the structural network can transmit messages to a target region with individually differing speed and reliability. Moreover, we address the former challenge by capitalizing on advances in network science and machine learning. Utilizing these methods is a promising direction for investigating abnormal brain development. In this proposal, we describe the application of a novel analysis of structural network maturation to study the manifestation of transdiagnostic executive deficits. Using a large sample of youth who completed cross-sectional neuroimaging as part of the Philadelphia Neurodevelopmental Cohort (n=1,042), our preliminary analyses demonstrate previously uncharacterized relationships between brain structural connectivity and EF across age. We will use an additional cross-sectional dataset enriched with youths presenting with psychopathology acquired through the Healthy Brain Network study (n=5,000) to generalize our findings to executive dysfunction. To quantify developmental norms, we will capitalize on our large-scale datasets to train a machine learning model that predicts expected ranges of healthy brain development. Furthermore, understanding within-person changes in brain structure and EF necessitates longitudinal data. We will longitudinally assess youths diagnosed with ADHD (n=50) or schizophrenia (n=50), and typically developing comparators (n=40). In this proposal, we aim to 1) delineate how age-related variation of brain network transmission fidelity is associated with executive dysfunction across psychiatric disorders, and 2) determine how within-individual development in transmission fidelity is associ...