US-Israel Collaboration: Collaborative Research: New Tools for Extracting Neuronal Phenotypes from a Volumetric Set of Cerebral Cortex Images

连接组学的一个主要限制是很少有工具可以将连接组图像转换为可挖掘的数据库。该项目的研究目标是开发一套工具，从大脑的物理结构中提取基本的结构参数，并以非常高的分辨率（纳米尺度）成像。pi将通过自动化方法确定神经元、突触及其连接模式的大小和形状。使用他们的工具，pi将分析这些详细和变化的数据集，以找到其中的关键模式。他们相信，这种自动化的方法是理解控制大脑皮层神经回路形成的规律和规则的必要条件，迄今为止，这些规律和规则只在非常小的样本空间中进行了研究。大脑皮层可能是哺乳动物生物学中最不为人所知的部分。迄今为止还没有进行过如此大规模的神经元表型空间研究：该数据集将包含数十万个体细胞和10亿个突触，允许pi搜索只能用先前研究中使用的工具猜测的模式。了解大脑皮层网络中存在的首要组织原则对于理解大脑如何正常工作以及它们如何在疾病中出错至关重要。此外，连接组学研究正在世界各地大量不同的实验室开始，重点关注广泛的物种和大脑的部分。这些工具应该直接适用于这些工作。pi是由四个实验室组成的联合体，在计算机科学（Shavit）、系统生物学（Alon）、图像处理（Pfister）和神经生物学（Lichtman）方面具有互补的专业领域。他们共同构建了一套从连接组图像中提取重要参数的方法。这些方法包括神经元几何提取、网络结构、基序检测和原型模式分析。这些方法基于两个软件平台：用于生成连接体图的MapRecurse平台和用于在这些图中挖掘模式的Pareto Inference Engine。pi将在含有数万个细胞和10亿个突触的哺乳动物大脑皮层上测试这些技术，目的是提取神经回路的特性，这些特性很难或不可能通过任何其他方式获得。本提案的工作将对神经科学产生重大影响。它直接说明了白宫大脑计划的核心目标。它将为神经科学家提供许多强大而新颖的工具，以了解构成大脑功能基础的细胞和回路。它还应该对机器学习和神经形态计算的发展方法产生影响。美国-以色列两国科学基金会（BSF）正在资助一个伙伴项目。

A major limitation in connectomics is that there are few tools to transform connectomic images into a minable database. The research aim of this project is to develop a suite of tools that extract essential structural parameters from the brain's physical structure that was imaged at very high (nanometer scale) resolution. The PIs will determine, by using automated methods, the sizes and shapes of neurons, synapses and their connectivity patterns. Using their tools, the PIs will analyze this detailed and varied dataset to find the key patterns within it. It is their belief that such automated methods are a requirement to comprehend the regularities and rules that govern the formation of neural circuits in the cerebral cortex, which to date have only been studied on very small sample spaces. The cerebral cortex remains perhaps the least understood aspect of mammalian biology. No studyof this magnitude of the neuronal phenotype space has ever been conducted: the dataset will contain hundreds of thousands of somata and a billion synapses, allowing the PIs to search for patterns that could only be guessed at with the tools used in prior research. Knowing what overarching organizational principles exist in a cerebral cortical network is crucial for understanding how brains work normally and how they may go awry in disease. Moreover, connectomic studies are beginning in a large number of different laboratories throughout the world focused on a wide range of species and parts of the brain. These tools should have direct applicability to many of these endeavors.The PIs are a consortium of four laboratories with complementary areas of expertise in computer science (Shavit), systems biology (Alon), image processing (Pfister) and neurobiology (Lichtman). Together they are building a stacked set of methods that extract important parameters from connectomic images. These methods include neuron geometry extraction, network structure, motif detection, and archetypical pattern analysis. These approaches are based on two software platforms:the MapRecurse platform for generating connectome graphs and the Pareto Inference Engine for mining patterns within such graphs. The PIs will test these techniques on an a volume of mammalian cerebral cortex containing tens of thousands of cells and a billion synapses, with the aim of extracting the properties of neural circuits that would be difficult or impossible to obtain any other way. The work in this proposal will have significant impact on neuroscience. It speaks directly to the central goals of the White House BRAIN Initiative. It will provide neuroscientists with anumber of powerful and novel tools to understand the cells and circuits that underlie brain function. It should also be influential in developing approaches in machine learning and neuromorphic computing. A companion project is being funded by the US-Israel Binational Science Foundation (BSF).