Relating structure and function in synapse-level wiring diagrams

突触级接线图中的结构和功能相关

• 批准号: 10006999
• 负责人: Ashok Litwin-Kumar
• 金额: $ 117.61万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10006999
• 关键词: Address; Adult; Animal Model; BRAIN initiative; Behavior; Brain; Calcium; Categories; Cells; Cerebellum; Data; Data Analyses; Data Set; Development; Drosophila genus; Drosophila melanogaster; Electron Microscopy; Experimental Models; Future; Generations; Goals; Graph; Image; Individual; Insecta; Learning; Link; Location; Methods; Modality; Modeling; Modernization; Mushroom Bodies; Nature; Nervous system structure; Neurons; Neurosciences; Organism; Output; Property; Research Personnel; Sensory; Source; Stimulus; Structure; Synapses; System; Techniques; Uncertainty; Weight; brain tissue; brain volume; cell type; classical conditioning; connectome; connectome data; data modeling; dopaminergic neuron; high dimensionality; insight; interest; large datasets; microscopic imaging; multiple datasets; network models; neural circuit; neural model; neural network; reconstruction; recurrent neural network; relating to nervous system; statistics; technique development; tool

Project summary: Modern electron-microscopy (EM) imaging and analysis methods now permit the comprehensive reconstruction of all neurons and synapses in large volumes of brain tissue or the entire brains of individual organisms. However, relating this structure to function is difficult. The rapidly increasing scale of these datasets requires the develop- ment of new quantitative techniques to address this challenge. This proposal describes a combined data analysis and modeling approach that is informed by large-scale EM datasets collected by our experimental collaborators. The methods we will develop extend the state of the art by incorporating multiple sources of information about neuronal connectivity and function to explain structure in EM wiring diagrams. They also leverage recent advances in recurrent neural network optimization to use this structure to constrain models of neural dynamics. Our aim is both to develop general and scalable techniques to be used on the latest generation of datasets, as well as apply these techniques to specific scientific questions about the organization of the Drosophila mushroom body, which is a primary target of current reconstruction efforts. Specific aims of the project include a number of subgoals, starting with the development of techniques to determine the organizing principles of neuronal wiring given a connectivity graph defined by an EM dataset. Unlike standard methods, we aim to leverage multiple modalities of information; for instance, connectivity, cell types, functional data, spatial location, and synaptic weights, to perform this inference. Next, we will perform an analysis of the mushroom body of the adult Drosophila melanogaster brain, a center for associative learning in insects. This analysis will both inform the development of our methods and also address fundamental scientific questions about the nature of stimulus representations in mushroom body Kenyon cells and the circuitry involved in learning. Strong parallels between the organization of the mushroom body and the mammalian cerebellum suggest that these efforts will lead to generalizable insights. Finally, we will integrate structural information with modeling of neural dynamics. We will characterize to what extent structure can be used to build well-constrained models, validating our approaches on datasets that involve characterization of connectivity through EM and recording of neural activity through calcium imaging. The proposed methods will be of interest for researchers working across many model organisms for which EM reconstruction efforts have been completed or are currently underway. We expect that the methods will provide a template for integrating structural information into modeling efforts across these varied systems.

项目概要： 现代电子显微镜（EM）成像和分析方法现在允许全面重建 所有神经元和突触在大体积的脑组织或整个大脑的个体生物。然而，在这方面， 将这种结构与功能联系起来是困难的。这些数据集的规模迅速增加，需要开发- 新的量化技术来应对这一挑战。该提案描述了组合数据分析 和建模方法，是由我们的实验合作者收集的大规模EM数据集通知。 我们将开发的方法通过整合有关以下方面的多种信息来源来扩展最新技术水平： 神经元的连接性和功能来解释EM接线图中的结构。他们还利用最近的进展 在递归神经网络优化中使用这种结构来约束神经动力学模型。我们的目标是 既要开发用于最新一代数据集的通用且可扩展的技术，又要应用 这些技术的具体科学问题的组织的果蝇蘑菇体， 是当前重建工作的主要目标。 该项目的具体目标包括若干分目标，首先是开发技术， 由EM数据集定义的连接图给出神经元布线的组织原则。不像标准 方法，我们的目标是利用多种形式的信息;例如，连接，细胞类型，功能 数据、空间位置和突触权重来执行该推断。接下来，我们将分析 黑腹果蝇（Drosophila melanogaster）大脑的蘑菇体，是昆虫的联想学习中心。这 分析将为我们的方法的发展提供信息，并解决以下基本科学问题： 蘑菇体凯尼恩细胞中刺激表征的性质和参与学习的电路。 蘑菇体的组织结构与哺乳动物小脑的相似之处表明， 这些努力将产生可推广的见解。最后，我们将把结构信息与 神经动力学我们将描述结构在多大程度上可以用来构建约束良好的模型， 验证我们在数据集上的方法，这些数据集涉及通过EM和记录 通过钙成像观察神经活动 所提出的方法将是感兴趣的研究人员在许多模式生物的工作，EM 重建工作已经完成或正在进行。我们希望这些方法能提供一个 将结构信息集成到这些不同系统的建模工作中的模板。