Promoting Collaborative Research on Human Connectome Analysis for Substance Use Disorders

促进药物使用障碍人类连接组分析的合作研究

• 批准号: 10738580
• 负责人: Minjeong Kim
• 金额: $ 21.61万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10738580
• 关键词: Adolescent; Affect; Biomedical Research; Biometry; Brain; Brain Diseases; Brain imaging; Collection; Complex; Computer software; Data; Data Analyses; Data Collection; Data Science; Data Scientist; Data Set; Dedications; Development; Developmental Disabilities; Education; Education Projects; Educational workshop; Environment; Faculty; Goals; Hour; Human; Individual; Knowledge; Life Cycle Stages; Longevity; Machine Learning; Magnetic Resonance Imaging; Mathematics; Medical; Medical Students; Mental Health; Mentors; Methodology; Nervous System Physiology; Neurologic Dysfunctions; Neurosciences; Neurosciences Research; North Carolina; Online Systems; Outcome; Outreach Research; Pathway Analysis; Play; Postdoctoral Fellow; Process; Psychiatry; Psychology; Recording of previous events; Reproducibility; Research; Research Methodology; Research Personnel; Role; Statistical Data Interpretation; Structure; Student recruitment; Students; Substance Use Disorder; System; Technology; Time; Training; United States; Universities; Variant; Visualization; analytical tool; clinical application; cognitive development; computational platform; computer science; computerized tools; connectome; connectome data; design; education planning; forest; graduate student; improved; insight; lectures; lifestyle factors; longitudinal dataset; medical schools; member; multidisciplinary; network architecture; neuroimaging; neuropsychiatric disorder; next generation; novel; operation; outreach; phenomenological models; programs; prospective; reconstruction; research faculty; response; skills; statistics; student training; substance misuse; substance use; success; tool; undergraduate student; user-friendly; web site

Project Summary/Abstract Brain connectivity plays a fundamental role in neurologic function and dysfunction, and can directly impact substance use or be alerted by inappropriate uses of substances. However, there is a limited understanding of the bidirectional relationship between brain connectomics and substance use, e.g., are there connections of the brain that predispose an individual to substance use disorders (SUD), and how SUD impacts the brain and its development. Improving this understanding is of critical importance in obtaining mechanistic insights into factors underlying substance misuse and neuropsychiatric disorders. With the availability of large-scale and longitudinal data sets such as ABCD, we are now at the golden time to significantly advance our understanding of the causal or association relationship between SUD and brain connectivity. However, we are facing both computational and theoretical challenges in brain network data analysis, considering the complexity and scale of the brain imaging data. It is critical to train the next-generation neuroscience data scientists with sufficient knowledge to correctly do a full life cycle of data science (LCDS) for brain connectomes analysis. Here, a full LCDS includes steps to collect data for the best brain connectome analysis, reliably and robustly extract brain connectomes, and rigorously analyze variations in the data. The proposed educational plan aims at (i) developing easy-to-use computational tools for connectome reconstruction, visualization, and statistical analysis and training students and young investigators to use these tools; and (ii) enhancing rigorous and reproducible statistical analysis of brain network data through short courses, summer camps, and workshops. The success of the project relies on the unique brain imaging and machine learning expertise of the PIs (Drs. Wu and Zhang) and their collaborative relationships with experts in biostatistics, mental health, computer science, and psychology research faculty in the Department of Psychiatry (PSYCH), the Department of Statistics & Operation Research (STOR), the Department of Biostatistics (BIOS), the Department of Computer Science (CS), the Department of Psychology (PSY), UNC Neuroscience Center (UNCNC), and the Carolina Institute of Developmental Disabilities (CIDD) at the University of North Carolina (UNC) at Chapel Hill, and other departments in Duke University, Wake Forest University, Wake Forest School of Medicine, and UNC at Greensboro.

项目摘要/摘要 大脑连接性在神经功能和功能障碍中起着基本作用，可以直接影响 物质使用或通过不适当使用物质来提醒。但是，对 大脑连接组和物质使用之间的双向关系，例如，是否存在连接 使人易用物质使用障碍的大脑（SUD），以及SUD如何影响大脑及其ITS 发展。提高这种理解对于获得机械洞察力至关重要 潜在的药物滥用和神经精神疾病。随着大型和纵向的可用性 诸如ABCD之类的数据集，我们现在正处于黄金时间，以大大提高我们对因果关系的理解 或SUD与大脑连通性之间的关联关系。 但是，我们在大脑网络数据分析中面临计算和理论挑战， 考虑大脑成像数据的复杂性和规模。训练下一代至关重要 具有足够知识的神经科学数据科学家，可以正确地做数据科学的完整生命周期（LCD） 脑连接组分析。在这里，完整的LCD包括收集最佳大脑连接数据数据的步骤 分析可靠，可靠地提取大脑连接，并严格分析数据中的变化。这 拟议的教育计划旨在（i）开发用于连接组重建的易于使用的计算工具， 可视化，统计分析和培训学生和年轻研究人员使用这些工具； （ii） 通过短期课程，夏季，增强大脑网络数据的严格且可重复的统计分析 营地和讲习班。该项目的成功依赖于独特的大脑成像和机器学习 PIS的专业知识（Wu和Zhang博士）及其与生物统计学专家的合作关系 精神病学系（心理）的健康，计算机科学和心理学研究学院， 统计与运营研究部（Stor），生物统计局（BIOS），部门 计算机科学（CS），心理学系（PSY），UNC神经科学中心（UNCNC）和 卡罗来纳州北卡罗来纳大学（UNC）教堂山的卡罗来纳州发展障碍研究所（CIDD） 以及杜克大学，维克森林大学，维克森林医学院和UNC的其他系 在格林斯伯勒。