CAREER: Towards the Next Generation of Data-Driven Computational Brain Analytics

老自治领大学被授予早期教师职业发展补助金，以支持水旺吉博士的研究，从而更好地了解大脑。大脑是一个极其复杂的系统，因此对大脑数据的分析也是一个同样巨大的挑战。人类大脑包含数十亿个神经元和数万亿个突触（连接）;它们中的每一个在基本的生物化学，功能和动力学方面都是独一无二的。大脑也是一个跨不同空间尺度组织的多层次系统，从基因、突触和细胞到电路、大脑区域和系统。今天，脑科学正在经历快速的变化，并有望在不久的将来取得重大进展。最近的技术创新使科学家能够以更快的速度和分辨率捕获基因表达模式，连接和神经元活动。这产生了大量的数据，这些数据捕捉了不同组织层次的大脑活动。为了应对分析这些新数据的核心挑战，该项目将开发一类高效，综合，多维，预测和相关的技术，并使用它们来分析大规模，高分辨率和多模态的大脑数据集。具体来说，该项目将开发分析工具，从遗传转录谱中预测细胞分辨率的全脑连接体（“接线图”）。这种分析将阐明从基因到连接性并最终到功能的信息通路。该项目还将通过执行多维网络相关分析来整合其他大脑维度。此外，该项目将解决基因表达，细胞类型和大脑结构之间的关系。该项目的成功将是一种新的高效、强大的分析方法，这种方法足够灵活，可以用于整合、建模和挖掘当前和未来的大脑数据。该项目的结果将对多个学科产生直接和强烈的影响，包括大脑数据分析和计算神经科学、生物图像信息学和大数据分析。未来的长期目标是揭示正常和受损大脑功能之间的基本差异。对大脑数据分析的统一处理将很容易转化为培训下一代计算生物学家的新课程。该项目的多学科性质为将其组成部分纳入现有课程提供了独特的机会。脑科学已被证明是激发K-12学生科学兴趣的宝贵资源。该项目的组成部分将被整合到现有的高中学生实习计划，从而激励未来的科学学生。将特别鼓励代表性不足的学生参与整个项目。该项目的成果将以同行审查出版物、开放源码软件、教程、研讨会和讲习班的形式传播。所有研究结果、出版物、软件和数据都将在项目网站上公开发布：http://compbio.cs.odu.edu/CAREER/

Old Dominion University is awarded an Early Faculty Career Development grant to support Dr Shuiwang Ji in research leading to a better understanding of the brain. The brain is an enormously complex system, and the analysis of brain data is thus an equally enormous challenge. Human brains contain billions of neurons and trillions of synapses (junctions); and each of them is unique in their basic biochemistry, functions, and dynamics. The brain is also a multi-level system organized across different spatial scales, ranging from genes, synapses, and cells to circuits, brain regions, and systems. Today, brain science is experiencing rapid changes and is expected to achieve major advances in the near future. Recent technological innovations are enabling scientists to capture the gene expression patterns, connectivity, and neuronal activities at increasing speed and resolution. This is generating a deluge of data that capture the brain activities at different levels of organization. To attack the central challenges of analyzing these new data, this project will develop a class of efficient, integrative, multidimensional, predictive, and correlative techniques and use them to analyze large-scale, high-resolution, and multi-modality sets of brain data. Specifically, this project will develop analytics tools to predict the cellular-resolution, brain-wide connectome ("wiring diagram") from genetic transcriptional profiles. This analysis will elucidate the information pathway from genes to connectivity and ultimately, to function. This project will also integrate other brain dimensions by performing multidimensional network correlative analytics. In addition, this project will address the relationship between gene expression, cell types, and brain structures. The success of this project will be a new class of efficient, robust analytics methods that are flexible enough to be adapted for integrating, modeling, and mining current and future brain data.The results of this project will have an immediate and strong impact on multiple disciplines, including brain data analytics and computational neuroscience, biological image informatics, and big data analytics. A future long term goal is to uncover basic underlying differences between normal and impaired brain functions. The unified treatment of brain data analytics will be readily transformable into new courses for training next-generation computational biologists. The multidisciplinary nature of this project provides unique opportunities for integrating its components into existing curricula. Brain science has been shown to be a valuable resource for inspiring scientific interests in K-12 students. Components of the project will be integrated into an existing high-school student internship program, thereby inspiring future science students. Underrepresented students will be especially encouraged to participate throughout the project. The results of this project will be disseminated in the form of peer-reviewed publications, open-source software, tutorials, seminars, and workshops. All findings, publications, software, and data will be made publicly available at the project website: http://compbio.cs.odu.edu/CAREER/