ABI Innovation: Modeling the Drosophila Brain with Single-neuron Resolution using Computer Vision Methods

该项目的资金用于模拟神经元的形态以及它们如何使用计算机视觉方法在中枢神经系统（CNS）中发育和形成突触。通过利用果蝇运动神经元的形态定型和无与伦比的单细胞分辨率，该项目将产生果蝇CNS中的第一个运动神经元图谱，作为案例研究和整个大脑建模的一步。第一个完整的大脑图谱是C。优雅它的优点是分辨率低至单个视觉识别神经元的水平，迄今为止还没有受到挑战。由于单个神经元的连接性最终决定了大脑的功能，因此在其他更复杂的模型生物中创建这一水平的神经地图对于促进我们对大脑发育的理解至关重要。该项目的直接目标是在单神经元水平上创建健康果蝇大脑中运动神经元的定义群体的神经地图。它旨在成为任何想要在体内“导航”模型大脑的人的资源。 为实现这一目标，该项目包括以下任务。(a)利用基于图像的运动神经元自动分类对单个神经元形态进行建模。为了满足这一目标，一个新的框架将开发三维分区的神经元？形态上的隔间，即索马（细胞体），轴突和树突。(b)从分割的体积中对神经元形态进行分类。该方法包括一个新的可能性半监督聚类框架，强大到足以区分不同的神经元形态，并处理类内变异性和类间相似性。(c)通过推断单个神经元建模的知识，识别和追踪果蝇中枢神经系统中部分形态特异性运动神经元，并构建原型运动回路。所有的发现、方法、开发的算法（开源代码）、出版物和数据将通过该项目的网站进行传播，一旦它们可用并准备好向公众开放：http://web.mac.com/gavriil/Gavriil_Tsechpenakis/neurovision。这项工作立即扩展到（a）完成模型运动回路，包括单个果蝇中枢神经系统半节内所有（约400个）手动识别的运动神经元;（B）识别更多类型的神经元，以扩大这项工作的发现并对大脑中更多类型的神经元电路进行建模。长期目标是创建一个完整的大脑模型，以便全面分析突变后的大脑发育，并评估药物治疗，疾病或衰老和压力导致的大脑连接模式的变化。

The project is funded to model the morphology of neurons and how they develop and form synapses in the Central Nervous System (CNS) using computer vision approaches. By exploiting the morphological stereotypes and unparalleled single-cell resolution of Drosophila motor neurons, this project will yield the first motor neuron map in the Drosophila CNS, serving as a case study and a step towards modeling the entire brain. The first complete map of a whole brain is that of C. elegans. Its virtue, unchallenged thus far, is resolution down to the level of single visually identified neurons. Because the connectivity of individual neurons ultimately determines brain function, the creation of neural maps at this level in other more complex model organisms is critical for advancing our understanding of brain development. The immediate goal of this project is to create a neural map of a defined population of motor neurons in a healthy Drosophila brain at the single-neuron level. It is intended to be a resource for anyone who wants to "navigate" a model brain in vivo. To achieve this goal, this project includes the following tasks. (a) Modeling the individual neuron morphology with automated image-based motor neuron classification. To meet this objective, a new framework will be developed for three-dimensional partitioning of the neurons? morphological compartments, namely soma (cell body), axon and dendrites. (b) Classifying the neuron morphology from the partitioned volumes. The approach consists of a novel possibilistic semi-supervised clustering framework, robust enough to discriminate between different neuron morphologies, and to handle the intra-class variability and inter-class similarities. (c) Identifying and tracing morphology-specific motor neurons in part of the Drosophila CNS by inferring knowledge of individual neuron modeling, and constructing a prototype motor circuit.All findings, methods, developed algorithms (open source codes), publications and data will be disseminated through the project's website, as soon as they become available and ready for the public: http://web.mac.com/gavriil/Gavriil_Tsechpenakis/neurovision. This work has immediate extensions to (a) the completion of a model motor circuit, by including all (approximately 400) manually identified motor neurons within a single Drosophila CNS hemisegment; (b) the identification of more types of neurons, for scaling up the findings of this work and modeling more types of neuron circuitries in the brain. The long-term goal is to create a complete model brain that will allow for comprehensive analysis of brain development after mutation, and the assessment of changes in brain connectivity patterns resulting from drug treatments, disease, or aging and stress.