Training in Advanced Statistical Methods in Neuroimaging and Genetics

神经影像和遗传学高级统计方法培训

• 批准号: 10200172
• 负责人: ROBERT C. WELSH
• 金额: $ 25.34万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10200172
• 关键词: Address; Adolescent; Age; Alzheimer' s Disease; Attention; Award; Back; Bayesian Analysis; Behavioral Research; Big Data; Biological Markers; Biomedical Research; Brain; Brain Diseases; Budgets; Classification; Clinic; Clinical; Clinical Research; Collaborations; Communities; Complex; Computers; Continuing Education; Data; Data Collection; Data Set; Disease; Educational process of instructing; Effectiveness; Enrollment; Ensure; Equation; Expenditure; Explosion; Faculty; Feedback; Funding; Genetic; Genomics; Geography; Grant; Growth; Home; Human; Image; Infrastructure; Institutes; Institution; Internet; Investigation; Knowledge; Learning; Linear Models; Measures; Meta-Analysis; Methods; Minority; Mission; Modeling; Monitor; Multimodal Imaging; National Institute of Biomedical Imaging and Bioengineering; National Institute of Drug Abuse; National Institute of Mental Health; National Institute of Neurological Disorders and Stroke; National Institute on Aging; Participant; Positioning Attribute; Process; Protocols documentation; Research; Research Personnel; Research Project Grants; Resources; Science; Scientist; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; Student Selections; Students; Techniques; Time; Training; Travel; Underrepresented Minority; United States; United States National Institutes of Health; Woman; Work; base; biobank; biomarker development; cognitive development; cohort; connectome; data acquisition; design; educational atmosphere; experience; follow-up; genetic analysis; genetic predictors; hands-on learning; imaging genetics; innovation; instructor; lecturer; lectures; meetings; multimodality; nervous system disorder; neuroimaging; patient population; polygenic risk score; predictive modeling; programs; rapid growth; response; skills; success; terabyte; theories

Abstract This program will design, implement, and establish a course to teach advanced statistical methods in neuroimaging and genetics. Over the past decade the National Institutes of Health has greatly increased funding of grants that utilized advanced neuroimaging methods, genetic methods, and advanced statistical methods. While introductory courses are offered, there is, however, no course offered in the United States that provides an intensive, hands-on learning opportunity to better prepare biomedical and clinical researchers in advanced methods. Given that the combined budgets for grants that utilize these advanced analysis techniques from the National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), National Institute on Aging (NIA), National Institute on Drug Abuse (NIDA), and National Institute of Biomedical Imaging and Bioengineering (NIBIB (five Institutes that greatly utilize neuroimaging and genetics), have grown 5-fold from ~250 million USD to ~1250 million USD, there is a great need to provide an educational opportunity to ensure the workforce is well positioned to carry out important work that has been identified by these and other institutes. This program will create the course, “Training in Advanced Statistical Methods in Neuroimaging and Genetics”. It will bring together world-class scientists and educators in a two-week intensive format to provide theoretical lectures paired with hands-on computer tutorials. This program will teach 20 students in the first year, 25 students in years 2 and 3, and then 30 students in years 4 and 5. These students will be accepted from all across the United States, with attention to a diverse student cohort, ensuring enrollment of women and underrepresented minorities. Additionally, the project will distribute Travel Awards based on financial need. The beginning of the course will be a full-day primer on basic statistical methods and analytic infrastructure. Over two weeks, students will learn and put into practice methods such as: hierarchical statistical models, Bayesian statistics, network science, functional and structural connectomics, disease driven degeneration of the brain, and methods for analysis of genetics data such as polygenic risk scoring and structural equation modeling. The course will conclude with lectures and labs on multi-modal analysis, including multi-modal imaging analysis, and imaging-genetics analysis, along with classification methods for biomarker development. To help students apply the acquired knowledge and skills to their independent research projects back at their home institutes, we will supplement the course with a continuing education portion. For the 6 months following the formal two-week course, the students will have access to the theory lecturers on a monthly basis in a structured learning environment. Additionally, during that 6-month follow-up period, they will also have near-real-time access regarding technical implementation questions through the Slack messaging and collaboration platform. This continued education portion will greatly increase success utilizing their new practical skills in their own research.

抽象的 该计划将设计，实施和建立一门课程，以教授高级统计方法 神经影像学和遗传学。在过去的十年中，美国国立卫生研究院大大增加了资金 利用先进的神经影像学方法，遗传方法和先进统计方法的赠款。 在提供课程的同时，美国没有提供任何课程 一个密集的动手学习机会，可以更好地准备生物医学和临床研究人员 方法。鉴于利用这些高级分析技术的赠款的组合预算 国家心理健康研究所（NIMH）国家神经系统疾病与中风研究所（NINDS）， 国家老化研究所（NIA），美国国家药物滥用研究所（NIDA）和国家生物医学研究所 成像和生物工程（Nibib（五个利用神经影像学和遗传学的机构）已经成长 5倍从约2.5亿美元到约1.25亿美元，非常需要提供教育机会 为了确保劳动力能够很好地执行这些和其他的重要工作 机构。该计划将创建课程：“神经影像学高级统计方法的培训 遗传学”。它将以两周的密集格式将世界一流的科学家和教育者汇集在一起 理论讲座与动手计算机教程配对。该课程将在第一年教20名学生， 25年和3年级的25名学生，然后在4年和5年中30名学生。这些学生将被所有人接受 在整个美国，关注一个潜水员的学生队列，以确保妇女入学和 代表性不足的少数民族。此外，该项目将根据财务需求分配旅行奖。这 课程的开始将是基本统计方法和分析基础结构的全日入门。超过 两个星期，学生将学习并投入实践方法，例如：等级统计模型，贝叶斯 统计，网络科学，功能和结构连接学，疾病推动大脑变性，以及 分析遗传学数据的方法，例如多基因风险评分和结构方程建模。 该课程将包含有关多模式分析的讲座和实验室，包括多模式成像分析， 和成像基因分析，以及用于生物标志物开发的分类方法。帮助学生 将获得的知识和技能应用于他们的独立研究项目，以便在他们的家庭研究所中，我们 将通过继续教育的部分补充课程。正式两周后的6个月后的6个月 当然，学生将每月在结构化学习中每月访问理论讲师 环境。此外，在那六个月的随访期间，他们还将访问几乎是实时的 关于通过Slack Messaging和协作平台进行技术实施问题。这 持续的教育部分将在自己的研究中利用其新的实践技能大大提高成功。