ABCD Course on Reproducible Data Analyses

项目概要/摘要 ABCD-ReproNim课程（1 R25-DA 051675）是一个合作伙伴关系，提供研究教育 对ABCD研究数据进行可重复分析的培训。该课程整合了ReproNim的课程： 可再现神经成像计算中心，这是NIBIB资助的P41生物医学技术 资源中心（BTRC），其愿景是帮助神经影像学研究人员获得更多的可重复数据 分析工作流程和结果。ReproNim的方法依赖于技术开发和易于实现的 可访问的，用户友好的计算工具和服务，可以很容易地集成到当前的研究 实践，以及广泛的教育推广有关的可重复性，以神经影像学界在整个， 包括基础科学和临床学科的开发人员和应用研究人员。当前 该项目提出了一项行政补充，以提供专门的研究培训， 青少年大脑认知发展（ABCD）研究FAIR（即，可查找、可扩展、可互操作， 可重用）和AI/ML（即，人工智能和机器学习）准备就绪。ML/AI应用程序增加 在发现生物标志物、预测干预结果和整合信息方面的相关性 数据集。然而，进行有效的生物医学ML研究所需的知识涵盖了以下知识： 数据、科学问题、计算技术以及ML/AI平台和工具。ABCD-ReproNim AI/ML课程将扩展目前的培训，使学员了解工具，概念和注意事项， ABCD数据的多模态ML/AI处理。学生将首先接受为期5周的在线课程培训 包括讲座，阅读和ABCD数据练习，主题包括：（1）公平和公平 ML/AI应用，（2）ML的核心概念，（3）神经成像ML，（4）可解释/可解释ML，以及（5） 深度学习入门。所涉及的能力和技能将包括培训和出版ML 模型，组织和评估ML应用程序的数据，并有效地重用现有模型。说教 指导之后将是为期5天的远程项目周，学生将应用所学的技能和工作 完成AI/ML数据分析项目。成功将导致训练有素的研究人员能够 应用可重复的AI/ML实践来测试AI/ML模型在横截面和纵向的通用性 ABCD数据集的预测。

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.