Integrative labeling, imaging, and reconstruction tools for high-throughput inhibitory microconnectivity analysis in the mouse brain

用于小鼠大脑高通量抑制性微连接分析的集成标记、成像和重建工具

• 批准号: 10025817
• 负责人: Dawen Cai
• 金额: $ 314.41万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-20 至 2024-08-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10025817
• 关键词: 3-Dimensional; Achievement; Algorithms; Anatomy; Auditory; Auditory area; Axon; Brain; Brain region; Cells; Collaborations; Cre driver; Data; Data Set; Degenerative Disorder; Disease; Electron Microscopy; Feedback; Foundations; Functional Imaging; Future; Genetic; Goals; Gold; Heterogeneity; Human; Image; Individual; Interneurons; Knowledge; Label; Light; Machine Learning; Maps; Modeling; Molecular; Morphology; Mus; Neurites; Neurobiology; Neurons; Neurosciences; Optics; Output; Process; Property; Protocols documentation; Resolution; Resources; Running; Sampling; Solid; Spatial Distribution; Speed; Stains; Synapses; Techniques; Technology; Thalamic structure; Thick; Three-Dimensional Image; To specify; Training; Visual; Visual Cortex; anatomic imaging; autism spectrum disorder; base; cloud based; computerized tools; deep learning; developmental disease; hippocampal pyramidal neuron; image reconstruction; improved; learning network; light microscopy; machine learning algorithm; microscopic imaging; millimeter; neural circuit; neural network; novel; prevent; reconstruction; relating to nervous system; tool; tool development

Abstract Neural circuits composed of interconnected neurons with distinct properties lay the physical foundation of any brain function. Identifying connections between individual neurons is central to understand how information is processed and propagated in the brain. While emerging high throughput light microscopy technologies are highly promising in allowing whole brain scale imaging at the single cell level, optical resolution limitation prevents their use in differentiating densely labeled neuronal processes in the same brain. In addition, computational tools for automatically extracting morphological information from intermingled neurons with high accuracy are still lacking. Our team will concurrently develop novel genetic tools for neuronal labeling, super-resolution imaging, and automated neuronal tracing for high-throughput circuit reconstruction. We will apply these tools to obtain densely reconstructed inhibitory microcircuits in the mouse cortex. These tools will be readily applicable for studying other long-standing questions in neuroscience and the resources generated by this project will be useful for future computational tool development.

抽象的 由具有不同特性的互连神经元组成的神经回路为物理基础奠定了 任何大脑功能。识别单个神经元之间的联系是了解如何 信息在大脑中进行处理和传播。同时出现高吞吐光显微镜 技术在允许单个单元格的整个脑尺度成像方面非常有前途，光学 分辨率限制阻止了它们在相同的密集标记的神经元过程中的使用 脑。此外，用于自动从中提取形态信息的计算工具 仍然缺乏高精度的神经元。我们的团队将同时发展新颖的遗传 用于高通量的神经元标记，超分辨率成像和自动神经跟踪的工具 电路重建。我们将应用这些工具以获得密集重建的抑制性微电路 鼠标皮层。这些工具将很容易适用于研究其他长期存在的问题 神经科学和该项目产生的资源将对未来的计算工具有用 发展。

项目成果

nGauge: Integrated and Extensible Neuron Morphology Analysis in Python.

• DOI: 10.1007/s12021-022-09573-8
• 发表时间: 2022-07
• 期刊: Neuroinformatics
• 影响因子: 3