CAREER: Modeling the structure and dynamics of neuronal circuits in the Drosophila larvae using image analytics

职业：使用图像分析对果蝇幼虫神经元回路的结构和动态进行建模

• 批准号: 1252597
• 负责人: Gavriil Tsechpenakis
• 金额: $ 57.25万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1252597&HistoricalAwards=false
• 关键词: CAREER; Modeling; structure; dynamics; neuronal

The ability to adjust dynamically to attain stability in the face of widely ranging internal changes and external insults is a feature observed commonly in natural systems. The human brain, for example, has an amazing capacity to functionally recover from strokes that caused damages to local neuronal circuitries. Despite their scientific and social values, little is known about the principles of such highly adaptive systems. However, recent advances in imaging and computational technologies are practically ripe for visualizing and processing the small insect brain in its entirety, down to the level of individual synaptic connectivity. The objective of this project is the image-based computational modeling of how synaptic connectivity is established in vivo during brain development, a major question in neuroscience today. Using imagery acquired with state-of-the-art imaging techniques (Brainbow and MARCM) at two Drosophila neuroscience laboratories, the goal is to estimate and pattern the complete morphology, connectivity properties and structure dynamics of single neurons and neuronal circuits in the Drosophila larvae. This research effort aims at setting the principle for large-scale studies of more complex brains at single-cell resolution, and modeling adaptive responses of neuronal circuits to changes such as aging, disease and injury. The overreaching hypothesis of this research is that the brain is a highly adaptive system defined by specific structure and dynamics, as a whole and at the single cell level. Although this is a fundamental hypothesis, it has been difficult to test using live animals, quantitatively through numerical modeling, or even qualitatively through observation. By bringing together leading-edge imaging technologies and computationally intensive image analytics, this project initiates the pursuit of what makes the brain function as a whole throughout life and continuously adapt to various changes such as aging, disease, drug treatment, and injury. The research plan includes training a number of graduate, undergraduate and K12 students, integrating cutting-edge research in Computer Science and Computational Neuroscience with education. All findings, methods, developed algorithms (open source), publications, and data will be publicly available at the project website: http://neurovision.cs.iupui.edu/

在面对广泛的内部变化和外部侮辱时，动态调整以获得稳定的能力是自然系统中常见的一个特征。例如，人类的大脑具有惊人的能力，可以从中风中恢复功能，因为中风对局部神经元电路造成了损害。尽管它们具有科学和社会价值，但人们对这种高度适应性系统的原理知之甚少。然而，成像和计算技术的最新进展实际上已经成熟，可以完整地可视化和处理小昆虫的大脑，直到单个突触连接的水平。这个项目的目标是基于图像的计算模型，研究在脑发育过程中突触连接是如何在体内建立的，这是当今神经科学中的一个主要问题。利用两个果蝇神经科学实验室最先进的成像技术(Brain弓和MARCM)获得的图像，目标是估计和描绘果蝇幼虫单个神经元和神经元回路的完整形态、连通性和结构动力学。这项研究旨在为在单细胞分辨率下对更复杂的大脑进行大规模研究设定原则，并对神经元电路对衰老、疾病和损伤等变化的适应性反应进行建模。这项研究的过分假设是，大脑是一个高度适应性的系统，由特定的结构和动力学定义，作为一个整体，在单个细胞水平上。尽管这是一个基本的假设，但它很难用活的动物进行检验，无论是通过数值建模进行定量检验，还是通过观察进行定性检验。通过将尖端成像技术和计算密集型图像分析结合在一起，该项目发起了对如何使大脑在整个生命周期中发挥整体功能并不断适应各种变化(如衰老、疾病、药物治疗和损伤)的追求。研究计划包括培养一批研究生、本科生和K12学生，将计算机科学和计算神经科学的前沿研究与教育相结合。所有发现、方法、开发的算法(开源)、出版物和数据都将在项目网站上公开提供：http://neurovision.cs.iupui.edu/