Research Project
10406015
PROJECT SUMMARY/ABSTRACT The ABCD-ReproNim Course (1R25-DA051675) is a collaborative partnership to provide research educational training in reproducible analyses of data from the ABCD Study. The course integrates curriculum from ReproNim: A Center for Reproducible Neuroimaging Computation, which is a NIBIB-funded P41 Biomedical Technology Resource Center (BTRC) whose vision is to help neuroimaging researchers achieve more reproducible data analysis workflows and outcomes. The ReproNim approach relies on both technical development of readily accessible, user-friendly computational tools and services that can be readily integrated into current research practices, as well as a broad educational outreach about reproducibility to the neuroimaging community at large, including developers as well as applied researchers across basic sciences and clinical disciplines. The current project proposes an administrative supplement to provide dedicated research training on making data from the Adolescent Brain Cognitive Development (ABCD) Study FAIR (i.e., Findable, Accessible, Interoperable, and Reusable) and AI/ML (i.e., Artificial Intelligence and Machine Learning) ready. ML/AI applications have increased relevance in the discovery of biomarkers, predicting intervention outcomes, and integrating information across datasets. However, the knowledge required to perform effective biomedical ML research spans knowledge about data, scientific questions, computing technologies alongside ML/AI platforms and tools. The ABCD-ReproNim AI/ML Course will extend the current training to make trainees aware of the tools, concepts, and caveats for multimodal ML/AI processing of ABCD data. Students will first receive training across a 5-week online course that includes lectures, readings, and ABCD data exercises on topics that include: (1) FAIR for and FAIRness in ML/AI Applications, (2) Core Concepts in ML, (3), Neuroimaging ML, (4) Interpretable/Explainable ML, and (5) Introduction to Deep Learning. Competencies and skills addressed will include training and publishing ML models, organizing and evaluating data for ML applications, and reusing existing models efficiently. Didactic instruction will be followed by a 5-day remote Project Week, where students will apply the skills learned and work towards completion of AI/ML data analysis projects. Success will result in well-trained researchers who are able to apply reproducible AI/ML practices to test generalizability of AI/ML models for cross-sectional and longitudinal prediction across the ABCD dataset.
