Promoting Collaborative Research on Human Connectome Analysis for Substance Use Disorders

项目总结/摘要 脑连接在神经功能和功能障碍中起着基础性作用，并且可以直接影响 物质使用或被警告物质的不适当使用。然而，人们对 大脑连接组学和物质使用之间的双向关系，例如，是否有连接的 大脑使个体易患物质使用障碍（SUD），以及SUD如何影响大脑及其 发展提高这一认识是至关重要的，在获得机械的见解因素 潜在的药物滥用和神经精神疾病随着大规模和纵向的可用性 数据集，如ABCD，我们现在正处于黄金时间，以显着推进我们的因果关系的理解， 或SUD与脑连接之间的关联关系。 然而，我们在大脑网络数据分析中面临着计算和理论上的挑战， 考虑到脑成像数据的复杂性和规模。培养下一代至关重要 神经科学数据科学家有足够的知识来正确地做数据科学的完整生命周期（LCDS）， 脑连接体分析在这里，一个完整的LCDS包括收集最佳大脑连接体数据的步骤 分析，可靠和鲁棒地提取大脑连接体，并严格分析数据中的变化。的 拟议的教育计划旨在（i）开发易于使用的连接体重建计算工具， 可视化和统计分析，并培训学生和年轻的调查人员使用这些工具;以及（ii） 通过短期课程加强大脑网络数据的严格和可重复的统计分析，夏季 营地和车间。该项目的成功依赖于独特的大脑成像和机器学习 主要研究者（吴博士和张博士）的专业知识及其与生物统计学、心理学和医学领域专家的合作关系。 健康，计算机科学和心理学研究系精神病学（PSYCH）， 统计与运筹学系（STOR），生物统计学系（BIOS）， 计算机科学系（CS），心理学系（PSY），神经科学中心（UNCNC）和 位于查佩尔山的北卡罗来纳州大学的卡罗莱纳发育障碍研究所， 以及杜克大学、维克森林大学、维克森林医学院和麻省理工学院的其他院系 在格林斯伯勒。

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.