Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource - the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) - that combines longitudinal multi-modal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n-back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

Mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopathology. To this end, we introduce the Reproducible Brain Charts (RBC), an open resource that integrates data from 5 large studies of brain development in youth from three continents (N=6,346). Bifactor models were used to create harmonized psychiatric phenotypes, capturing major dimensions of psychopathology. Following rigorous quality assurance, neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner. Initial analyses of RBC emphasize the benefit of careful quality assurance and data harmonization in delineating developmental effects and associations with psychopathology. Critically, all RBC data–including harmonized psychiatric phenotypes, unprocessed images, and fully processed imaging derivatives–are openly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Together, RBC facilitates large-scale, reproducible, and generalizable research in developmental and psychiatric neuroscience.

White matter bundle reconstruction from diffusion MRI has thus far mostly been constrained to high-quality data acquired from many directions on multiple shells. Here, we evaluate test-retest reliability and predictive capability of WM bundles reconstructed from single-shell, 32-direction dMRI data in 1221 subjects, comparing three orientation distribution function reconstruction methods (GQI, CSD, SS3T-CSD). We find that WM connections can be reliably reconstructed from these simple acquisitions with features well-suited for predicting cognition. The three ODF methods strongly influenced bundle reliability, completeness, and predictive performance, with advantages for single-shell-optimized methods. Our results highlight the enormous potential for clinical and legacy dMRI datasets to accelerate WM research.