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.

摘要 该计划将设计、实施和建立一门课程，教授高级统计方法在 神经成像和遗传学。在过去的十年里，国家卫生研究院大大增加了资金 使用先进的神经成像方法、遗传学方法和先进的统计方法的赠款。 虽然提供入门课程，但在美国没有提供提供 一个密集的实践学习机会，更好地为高级生物医学和临床研究人员做准备 方法：研究方法。鉴于使用这些先进分析技术的赠款的综合预算来自 国家神经疾病和中风研究所(NINDS)，国家精神卫生研究所(NIMH)， 国家老龄研究所(NIA)、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(主要利用神经成像和遗传学的五个研究所))已经增长 5倍于约2.5亿美元至约1.25亿美元，非常需要提供教育机会 确保员工队伍处于有利地位，能够执行这些和其他部门确定的重要工作 研究所。本课程将开设“神经影像高级统计方法培训”。 遗传学“。它将以为期两周的密集形式聚集世界一流的科学家和教育工作者，提供 理论讲座与实际操作的计算机教程相结合。这个项目将在第一年教授20名学生， 25名二年级和三年级学生，然后是30名四年级和五年级学生。这些学生将被所有学生录取 在全美范围内，关注多样化的学生群体，确保女性和 代表不足的少数族裔。此外，该项目还将根据经济需要发放旅行奖励。这个 课程的开始将是一个关于基本统计方法和分析基础设施的全天入门课程。完毕 两周后，学生将学习并实践诸如：分层统计模型、贝叶斯方法 统计学、网络科学、功能和结构连接学、疾病导致的大脑退化，以及 遗传学数据的分析方法，如多基因风险评分和结构方程模型。 本课程将以多模式分析的讲座和实验结束，包括多模式成像分析， 以及成像遗传学分析，以及生物标记物开发的分类方法。帮助学生 将所获得的知识和技能应用于他们在国内研究所的独立研究项目中，我们 将以继续教育部分作为课程的补充。在正式两周后的6个月内 课程中，学生将有机会在有组织的学习中每月接触到理论讲师 环境。此外，在这6个月的跟踪期内，他们还可以近乎实时地访问 关于Slack报文传送和协作平台的技术实施问题。这 继续教育部分将极大地提高他们在自己的研究中利用新的实用技能的成功。