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，从以非常高（纳米级）分辨率成像的大脑物理结构中提取基本结构参数。PI将通过使用自动化方法来确定神经元、突触及其连接模式的大小和形状。PI将使用他们的工具分析这个详细而多样的数据集，以找到其中的关键模式。他们相信，这种自动化方法是理解大脑皮层神经回路形成的基本原理和规则的必要条件，迄今为止，这些方法只在非常小的样本空间上进行过研究。大脑皮层可能是哺乳动物生物学中最不为人所知的部分。从来没有进行过如此规模的神经元表型空间的研究：数据集将包含数十万个胞体和十亿个突触，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哪些总体组织原则，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的大量不同实验室开始，重点是广泛的物种和大脑部分。这些工具应该直接适用于许多这些努力。PI是一个由四个实验室组成的联盟，它们在计算机科学（Sundert），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知识。他们正在共同构建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、模体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平台和用于在此类图中挖掘模式的Pareto推理引擎。PI将在含有数万个细胞和十亿个突触的哺乳动物大脑皮层上测试这些技术，目的是提取难以或不可能通过任何其他方式获得的神经回路的特性。这项工作将对神经科学产生重大影响。它直接谈到了白宫大脑倡议的中心目标。它将为神经科学家提供许多强大而新颖的工具，以了解大脑功能的细胞和电路。它也应该对机器学习和神经形态计算的发展方法产生影响。 一个配套项目正在由美国-以色列两国科学基金会（BSF）资助。

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).