Modeling Developmental Change in the ABCD Study: Longitudinal Analyses for Clinical Outcomes

ABCD 研究中的发育变化建模：临床结果的纵向分析

• 批准号: 10043873
• 负责人: Kathryn L Mills
• 金额: $ 12.18万
• 依托单位: UNIVERSITY OF OREGON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10043873
• 关键词: Adolescence; Adolescent; Area; Brain; Child Rearing; Clinical; Code; Collaborations; Communities; Competence; Computer software; Data; Data Analyses; Data Science; Data Set; Development; Dimensions; Educational process of instructing; Educational workshop; Environment; Environmental Risk Factor; Familiarity; Financial Hardship; Fostering; Funding; Goals; Health Sciences; Immersion; Instruction; Investigation; Knowledge; Learning; Longitudinal Studies; Magnetic Resonance Imaging; Measures; Mental Health; Methodology; Methods; Modality; Modeling; Neurosciences; Outcome; Paper; Participant; Psychopathology; Publishing; Reproducibility; Request for Applications; Research; Research Personnel; Scholarship; Secondary to; Structure; Techniques; Training; Work; career; cognitive development; design; disability; hackathon; interest; large datasets; longitudinal analysis; longitudinal dataset; mental development; neuroimaging; online repository; open data; open source; recruit; secondary analysis; skills; social factors; social inequality; symposium; theories; working group

Project Summary/Abstract Abstract: This project will fund a five-day immersive workshop that will prepare junior and senior researchers alike to utilize the ABCD data for longitudinal investigations of brain development in relation to mental health. The workshop will focus on training researchers on reproducible neuroimaging methods, longitudinal modeling approaches, and clinical mental health outcomes, with equal emphasis on theory and application. The workshop will be composed of instructional, tutorial, roundtable, break-out session, and open hacking components. Workshop attendees will immediately apply what they learn during the workshop through group projects (“open hacking”) focused on applying longitudinal approaches to relate brain development to mental health measures in the ABCD dataset. The main outcome of this workshop will be that researchers leave with answers to questions about analyzing the longitudinal ABCD data, with a deeper understanding of the theory behind modeling approaches as well as analytic considerations for highly dimensional data. This will include training on robust methodology appropriate for clinical outcomes, as well as fostering collaboration between computational and clinical researchers across career stages. Another principal aim of this workshop is to contribute to national efforts to diversify the pool of highly trained biomedical researchers by recruiting a diverse group of attendees by eliminating participation barriers associated with social inequality, including financial burden, parenting, or disability, and by creating a safe and welcoming environment during the workshop. The immediate goal of this workshop is for attendees to acquire the following competencies: a) knowledge of analytical and statistical considerations specific to large-scale, open, longitudinal datasets; b) how to conduct reproducible research, with emphasis on secondary analysis of neuroimaging data; c) knowledge of theory behind longitudinal modeling and ability to apply these models using open-source software; d) design and execute investigations with clinically meaningful outcomes; e) identify social and environmental factors that impact the development of psychopathology; and f) communicate with researchers from diverse backgrounds and build interdisciplinary teams. The dissemination goals of the proposed workshop include forming working groups to publish papers and present at conferences, as well as making all code or analytic techniques developed during the workshop available in online repositories for the broader research community. A long-term goal of this workshop is to form sustainable interdisciplinary networks between researchers from development, clinical, and computational backgrounds as these areas of expertise are all needed to advance our understanding of the development of mental health.

项目总结/摘要 翻译后摘要：该项目将资助为期五天的沉浸式研讨会，将准备初级和高级 研究人员都喜欢利用ABCD数据进行大脑发育的纵向研究， 与心理健康的关系。研讨会将重点培训研究人员， 神经影像学方法，纵向建模方法和临床心理健康结果， 理论与应用并重。研讨会将由教学，辅导， 圆桌会议、分组会议和开放式黑客组件。研讨会将立即 通过小组项目（“开放式黑客”）应用他们在研讨会期间学到的知识， 应用纵向方法将大脑发育与心理健康措施联系起来， ABCD数据集。本次研讨会的主要成果将是研究人员离开的答案， 对纵向ABCD数据进行分析，加深对理论的理解 建模方法以及高维数据的分析考虑。这将 包括关于适用于临床结果稳健方法的培训， 计算和临床研究人员在职业生涯各个阶段之间的合作。另一 这次讲习班的主要目的是促进各国努力使受过高级培训的人才库多样化， 生物医学研究人员通过招募不同的与会者群体， 与社会不平等有关的障碍，包括经济负担、养育子女或残疾， 在研讨会期间创造一个安全和温馨的环境。这件事的直接目标是 研讨会是为与会者获得以下能力：a）分析和 特定于大规模、开放、纵向数据集的统计考虑; B）如何进行 可重复的研究，重点是神经影像学数据的二次分析; c） 纵向建模背后的理论和使用开源软件应用这些模型的能力; d）设计和执行具有临床意义结果的调查; e）识别社会和 影响精神病理学发展的环境因素;以及f）与 来自不同背景的研究人员，并建立跨学科的团队。的传播目标 拟议讲习班包括成立工作组，发表论文，并在 会议，以及使所有的代码或分析技术在研讨会上开发 可在更广泛的研究社区的在线存储库中获得。长期目标是 讲习班的目的是在发展研究人员之间建立可持续的跨学科网络， 临床和计算背景，因为这些专业领域都需要推进我们的 了解心理健康的发展。