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

基本信息

批准号：
10012613
负责人：
ROBERT C. WELSH
金额：
$ 26.98万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10012613
关键词：
Address Adolescent Age Alzheimer&apos 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 environment 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

项目摘要

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，教授高级统计方法在过去的十年中，美国国立卫生研究院大大增加了神经影像学和遗传学的资金投入。利用先进的神经影像方法、遗传学方法和先进的统计方法的赠款。虽然提供入门课程，但美国没有提供提供以下内容的课程：密集的实践学习机会，可以更好地为高级生物医学和临床研究人员做好准备鉴于利用这些先进分析技术的赠款的合并预算。国家神经疾病和中风研究所 (NINDS)、国家心理健康研究所 (NIMH)、国家老龄化研究所 (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（五个充分利用神经成像和遗传学的研究所），已发展壮大从约 2.5 亿美元增至约 12.5 亿美元 5 倍，非常需要提供教育机会确保员工队伍能够很好地开展这些和其他方面已确定的重要工作该计划将创建“神经影像学高级统计方法培训”课程。它将汇集世界一流的科学家和教育工作者，以为期两周的密集形式提供课程。该项目第一年将教授 20 名学生，包括理论讲座和计算机实践教程。第二年和第三年有 25 名学生，第四年和第五年有 30 名学生。这些学生将被所有学校录取在美国各地，关注多元化的学生群体，确保女性和女性的入学此外，该项目将根据经济需要分配旅行奖励。课程的开始将是关于基本统计方法和分析基础设施的全天入门课程。两周，学生将学习并付诸实践方法，例如：分层统计模型、贝叶斯统计学、网络科学、功能和结构连接组学、疾病驱动的大脑退化，以及遗传学数据分析方法，例如多基因风险评分和结构方程建模。该课程将以多模态分析的讲座和实验室结束，包括多模态成像分析、和成像遗传学分析，以及生物标志物开发的分类方法来帮助学生。将获得的知识和技能应用到他们家乡研究所的独立研究项目中，我们在正式两周后的 6 个月内，将通过继续教育部分来补充课程。课程中，学生每月将有机会与理论讲师进行结构化学习此外，在 6 个月的随访期间，他们还将获得近乎实时的访问。通过 Slack 消息传递和协作平台解决技术实施问题。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践技能的成功率。