Long-range neuronal projections: circuit blueprint or stochastic targeting? Rigorous classification of brain-wide axonal reconstructions

远程神经元投射：电路蓝图还是随机目标？

• 批准号: 10360723
• 负责人: GIORGIO A ASCOLI
• 金额: $ 128.71万
• 依托单位: GEORGE MASON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10360723
• 关键词: 3-Dimensional; Address; Affect; Anatomy; Animal Model; Animals; Architecture; Area; Axon; BRAIN initiative; Bar Codes; Behavior; Bilateral; Brain; Brain region; Cells; Characteristics; Classification; Code; Cognitive; Collection; Communities; Complex; Computer Analysis; Contralateral; Data; Data Set; Development; Disease; Electrophysiology (science); Elements; Ensure; Foundations; Functional disorder; Future; Gap Junctions; Glutamates; Goals; Human; Impairment; In Vitro; Individual; Interneurons; Investigation; Ipsilateral; Knowledge; Label; Learning; Least-Squares Analysis; Length; Location; Maps; Memory; Methodology; Methods; Modeling; Molecular; Morphology; Multiplexed Analysis of Projections by Sequencing; Mus; Neocortex; Nervous system structure; Neural Network Simulation; Neuraxis; Neurons; Neurosciences; Nuclear; Nucleic Acids; Output; Pathologic; Pattern; Performance; Physiological; Population Sizes; Preparation; Property; Public Health; Research; Resolution; Resources; Role; Sample Size; Schizophrenia; Seminal; Slice; Source; Specificity; Speed; Synapses; Synaptic plasticity; System; Techniques; Testing; Time; Variant; algorithm development; analysis pipeline; artificial neural network; autism spectrum disorder; base; cloud based; cloud platform; combinatorial; comparative; deep learning; design; digital; forgetting; imaging genetics; innovation; insight; knowledge of results; laser capture microdissection; microscopic imaging; neural circuit; neural network; neuronal cell body; novel; open source; operation; programs; reconstruction; relating to nervous system; resilience; supercomputer; transmission process

ABSTRACT (PROJECT SUMMARY) The classification of neurons in the mammalian brain has long been a focus of intensive investigation in neuroscience. Neurons are widely recognized as the fundamental computational elements of the nervous system, and the broad diversity of their morphological, physiological, and molecular properties may provide crucial insights into their function and involvement in disease. Long-range axonal projections, in particular, are the quintessential determinants of network connectivity, providing a key nexus between cellular organization and circuit architecture. Converging technological breakthroughs in microscopic imaging, genetic labeling, and algorithmic development have only recently enabled the high-throughput collection of large-scale, whole-brain axonal reconstructions under the BRAIN initiative. Using a principled statistical strategy, the proposed project leverages such information-rich resources to rigorously identify, from each brain region, all “projection neuron types” with objectively distinct patterns of anatomical targeting. This application will thus directly test the seminal hypothesis that the axonal trajectories of individual neurons follow specific coordination plans as opposed to aiming randomly within the constraints of regional connections. While this data-driven classification necessarily depends on the existing digital tracings, we will deploy our full analysis workflow as an automated pipeline on public cloud servers, allowing not only free community access, but also continuous refinement of the resulting knowledge as more datasets become available. Moreover, our approach allows the quantitative estimation of the population size of every separate neuron class as well as its unique distribution of path distances from the soma to each target, defining the basic temporal dynamics of information transmission. We will also determine if different projection neuron types vary in their dendritic morphology, providing an important clue as to whether input processing is specifically tuned for the intended outputs. Furthermore, we will extend this innovative methodology by applying it to complementary datasets obtained by stochastic nucleic acid barcoding, laser capture microdissection, and sequencing, yielding far greater sample sizes in exchange for lower anatomical resolution. Last but not least, we will model the discovered axonal projection patterns into a novel artificial neural network design (“projectron nets”) to systematically explore their possible selective advantages in learning and memory robustness and performance. Achieving these goals will thus quantify, for the first time, the relevant single-neuron motifs to outline the functional blueprint of the mammalian central nervous system and related impairments for the long-lasting benefit of public health.

摘要（项目总结） 哺乳动物脑中神经元的分类长期以来一直是研究的焦点。 神经科学神经元被广泛认为是神经系统的基本计算元件， 系统，其形态，生理和分子特性的广泛多样性可以提供 对它们的功能和与疾病的关系有重要的认识。特别是长距离轴突投射， 网络连接的典型决定因素，提供了细胞组织之间的关键联系， 和电路架构。在显微成像、基因标记和 算法的发展最近才使大规模的高通量收集，全脑 在BRAIN计划下进行轴突重建。采用原则性统计战略，拟议项目 利用这些信息丰富的资源，从每个大脑区域严格识别所有“投射神经元”， 类型”，具有客观不同的解剖靶向模式。因此，此应用程序将直接测试 开创性的假设，即单个神经元的轴突轨迹遵循特定的协调计划， 而不是在区域联系的限制下随意瞄准。虽然这种数据驱动的分类 这必然取决于现有的数字跟踪，我们将部署我们的完整分析工作流程作为一个自动化的 管道上的公共云服务器，不仅允许免费的社区访问，而且还不断完善， 随着更多的数据集变得可用，所产生的知识。此外，我们的方法允许定量 估计每个单独的神经元类的种群大小以及其唯一的路径分布 从索马到每个目标的距离，定义了信息传输的基本时间动态。我们 还将确定不同投射神经元类型的树突形态是否不同， 输入处理是否针对预期输出进行了专门调整。此外，我们将扩展 这种创新的方法，通过将其应用于互补的数据集获得的随机核酸 条形码，激光捕获显微切割和测序，产生更大的样本量，以换取 较低的解剖分辨率。最后但并非最不重要的是，我们将把发现的轴突投射模式建模成一个 新的人工神经网络设计（“投影网络”），系统地探索其可能的选择性 在学习和记忆鲁棒性和性能方面的优势。因此，实现这些目标将量化， 第一次，相关的单神经元基序勾勒出哺乳动物中枢的功能蓝图 神经系统和相关损伤，以长期造福公众健康。