The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S. effort to track brain development in adolescence. Diffusion MRI (dMRI) offers a powerful window into white matter, but large, multi-site datasets are difficult to process and analyze. We address this with the ABCD-BIDS Community Collection (ABCC, release 3.1.0): an open resource of 24,000+ fully processed dMRI scans. ABCC includes analysis-ready data, detailed quality metrics, advanced microstructural measures, and individualized white matter tractography. Using these data, we show that newer dMRI metrics outperform traditional measures in detecting developmental change—and are more robust to differences in image quality. We also demonstrate that harmonizing data across scanners improves the consistency of findings across sites. ABCC lowers barriers to large-scale neuroimaging and provides a powerful, open foundation for studying adolescent brain development.

We introduce the Myelin Imaging Reliability and ReprOducibility Resource (MIRROR), an open multimodal test–retest MRI dataset for benchmarking scalable approaches to myelin-sensitive imaging. Across 27 measures derived from diffusion MRI, ihMT, MP2RAGE, QSM, T1w/T2w imaging, and relaxometry, we show that metrics differ substantially in tissue contrast, intermetric correspondence, and test–retest reliability. We openly release the imaging data, derived maps, and processing code to support method development, acquisition selection, and future validation against histological and other independent references.

Intrinsic timescale is a commonly used measure of spontaneous neural dynamics that quantifies the temporal window of processing in neuronal populations. It displays a hierarchical cortical organization across species and imaging modalities, with shorter timescales in sensorimotor cortex and longer timescales in association cortex. However, less is known about how intrinsic timescale evolves during human brain development. Here, we estimate intrinsic timescale in two independent youth datasets and examine its developmental patterns. We find that changes in intrinsic timescale follow a hierarchical gradient along the sensorimotor-to-association (S–A) axis. Analysis of an independent adult dataset suggests this pattern stabilizes in adulthood. Together, these findings highlight intrinsic timescale as a marker of hierarchical brain maturation during development.