The Nanoscale Connectome of the Cochlear Nucleus

耳蜗核的纳米级连接组

基本信息

批准号：
10606628
负责人：
Mark H Ellisman
金额：
$ 62.06万
依托单位：
UNIVERSITY OF SOUTH FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10606628
关键词：
3-Dimensional Acoustic Nerve Acoustics Adult Architecture Auditory Auditory system Automobile Driving Axon Biophysics Birds Brain Stem Brain region CNS processing Catalogs Cell model Cells Cellular Structures Censuses Classification Cochlea Cochlear nucleus Communities Data Development Electron Microscopy Experimental Designs Face Frequencies Future Gene Expression Genetic Goals Graph Hearing Hearing problem Human Image Knowledge Label Learning Link Location Mammals Maps Methods Modeling Molecular Molecular Profiling Morphology Mus Names Nerve Nerve Fibers Neurons Neurosciences Noise-Induced Hearing Loss Octopus Pathology Pattern Peripheral Phenotype Process Public Health Research Resolution Sampling Scanning Electron Microscopy Sensory Shapes Skeleton Specific qualifier value Standardization Structure Structure/Function Nuclei Synapses System Techniques Testing Therapeutic Tissues Work afferent nerve auditory processing automated segmentation biophysical model cell type cellular targeting connectome experimental study fluorescence imaging granule cell health goals human error in silico microscopic imaging molecular phenotype molecular scale nanoscale nerve supply neural neural circuit neural model novel parallel processing prevent programs recombinase sensory system sound supervised learning tool transcription factor unsupervised learning virtual reality

项目摘要

The cochlear nucleus is the gateway for central nervous system processing of auditory information in mammals. It has been proposed that parallel processing channels are set up in the CN, and these form the basis for further computation at higher stations of the auditory system. Despite decades of study, enumeration of CN cell types is incomplete and CN circuitry is described only superficially. In neuroscience generally, classification and naming of neurons has relied primarily upon qualitative approaches based upon human observational capabilities. We have implemented and in some cases developed novel high-throughput and unbiased techniques for labeling, segmenting and classifying neurons in 3D, generated from large-scale electron microscopy image volumes. We propose to deliver a nanoscale map, or connectome, of the mouse CN with enumerated and localized cell types and their synaptic connections. This effort is unbiased because all neurons will be sampled. To achieve this goal, we bring together four parallel modes of tissue analysis for neuron classification: morphology, connectivity, molecular identity and function. We propose that connectivity analysis will define long-proposed parallel processing circuits that will be tested functionally using realistic biophysical models of identified cell types. Notably, the cochlear nucleus contains both amorphous and layered organizations of cells, which serve as templates for all other brain regions. By investigating the fundamental structure of this sensory center, we will establish principles of neural computation and methods for structural and functional phenotyping that will apply to other brain regions regardless of their particular neural architecture.

耳蜗核是中枢神经系统处理听觉信息的门户哺乳动物。已经提出，在CN中设置了并行处理通道，这些形式在听觉系统的高级站点进行进一步计算的基础。尽管学习了数十年， CN细胞类型的枚举不完整，CN电路仅在表面上描述。在神经科学通常，神经元的分类和命名主要依赖于定性基于人类观察能力的方法。我们已经实施了，在某些情况下开发了用于标记，分割和分类的新型高通量和公正技术 3D中的神经元，由大型电子显微镜图像体积产生。我们建议提供一个鼠标CN的纳米级图或连接组，具有枚举和局部细胞类型及其突触连接。这种努力是公正的，因为所有神经元将被取样。为了实现这一目标，我们汇集了四种用于神经元分类的组织分析模式：形态，连通性，分子身份和功能。我们建议连通性分析将定义长期提供的平行加工电路将使用已识别的细胞类型的现实生物物理模型在功能上进行测试。值得一提所有其他大脑区域的模板。通过研究这个感官中心的基本结构，我们将建立神经计算的原理以及用于结构和功能表型的方法无论其特定的神经结构如何，都适用于其他大脑区域。