Training in Complex Systems and Data Science Approaches Applied to the Neurobiology of Drug Use

这个项目的目的是培训博士后和博士后学者复杂系统的应用。 和数据科学方法，研究药物滥用的神经生物学，具有双重策略：(1)培训 具备数据科学、应用数学、计算机科学和复杂系统专业知识的应聘者 将他们的技能应用于成瘾的神经科学；以及(2)从 神经科学、精神病学、心理学和遗传学在大数据方法的理论和应用中， 包括网络分析、机器学习算法和贝叶斯统计模型。实习生将是 跨学科和学术阶段配对，进行研究协作和互惠辅导，以促进 提高每个学员在新领域的熟练程度。每个实习生还将接受双重指导，他们的 辅导员是他们伴侣的主要导师。在其五年的期限内，该计划将提供三个 为五对博士后和博士后研究人员中的每一对提供多年的资助，初始队列为四人(两人 对)，以及在中间三年每年都有更多的对进入。核心课程将包括：(1) 在佛蒙特大学设立的复杂系统和数据科学研究生证书；(2)课程 神经科学、心理学和成瘾方面的工作，包括侧重于发展人类主题的课程 研究技能；以及(3)专门课程，旨在直接和有效地弥合 核心学科。学员还将参加两周一次的日记俱乐部和每月一次的研讨会，由资深学生领导。 参与该计划，以进一步支持获得多学科的研究技能。 该计划的主要目标是培养准备应用最先进的分析工具的研究人员 了解药物滥用的神经生物学。重点将是描述成瘾的神经底物。 以及其他共病的精神变态，总是着眼于临床应用。在最近的增长中 解决神经、遗传和环境问题的大样本、多模式数据集的数量和质量 成瘾的基础使现在是这样一个训练计划的好时机。密歇根州立大学的研究人员是理想的 适合于提供这种培训，因为成瘾研究、认知神经科学、 复杂的系统和数据科学，以及指导教师可以访问特殊的数据集 非常适合使用大数据方法进行审讯。拥有连贯的领域知识，并与 复杂系统的高级方法，学员将开发分析管道：(1)结合 纵向和多模式数据集的复杂聚合，包括各种神经成像模式， 遗传信息、调查和临床数据；(2)利用超级计算和现代机器的力量 学习算法以超越线性和单变量影响；以及(3)解决即时问题 药物滥用的临床重要性，可为确定危险因素、治疗和 干预战略和政策决策。

The purpose of this program is to train pre- and post-doctoral scholars in the application of complex systems and data science approaches to the neurobiology of substance abuse, with the dual strategies of: (1) training candidates with expertise in data science, applied mathematics, computer science, and complex systems to apply their skills to the neuroscience of addiction; and (2) training candidates pursuing addiction research from neuroscience, psychiatry, psychology and genetics in the theory and application of Big Data methods, including network analysis, machine learning algorithms, and Bayesian statistical models. Trainees will be paired across disciplines, and academic stages, for research collaboration and reciprocal tutoring to facilitate the development of proficiency in each trainee's new field. Each trainee will also be dual mentored, their secondary mentor being their partner's primary. Over its five-year duration, the program will provide three years of funding for each of five pairs of pre- and post-doctoral researchers, with an initial cohort of four (two pairs), and additional pairs entering in each of the middle three years. The core curriculum will incorporate: (1) the established complex systems and data science graduate certificate at the University of Vermont; (2) course work in neuroscience, psychology and addiction, including classes focused on developing human subjects research skills; as well as (3) specialized courses designed to directly and effectively bridge the gap between the core disciplines. Trainees will also attend a biweekly journal club and monthly seminar, led by senior participants in the program, to further support the acquisition of multidisciplinary research skills. The overarching aim of the program is to produce researchers poised to apply state-of-the-art analytic tools to understand the neurobiology of drug abuse. The focus will be characterizing the neural substrates of addiction and other comorbid psychopathologies, always with an eye toward clinical application. Recent increases in the quantity and quality of large-sample, multi-modal datasets that address the neural, genetic and environmental substrates of addiction make this a propitious time for such a training program. Researchers at UVM are ideally suited to provide this training as there exist close links between addiction research, cognitive neuroscience, complex systems and data science, and the mentoring faculty have access to exceptional datasets that are ideal for interrogation with Big Data methods. Armed with coherent domain knowledge and practiced with advanced methods for complex systems, trainees will develop analysis pipelines that: (1) incorporate sophisticated aggregation of longitudinal and multi-modal datasets, including various neuroimaging modalities, genetic information, survey and clinical data; (2) harness the power of supercomputing and modern machine learning algorithms to step beyond linear and univariate effects; and (3) address questions of immediate clinical importance to substance abuse that can inform the determination of risk factors, treatment and intervention strategy, and policy decisions.