Accelerating discovery of the human foveal microconnectome with deep learning

项目摘要 人类视网膜是中枢神经系统（CNS）中最复杂的微电路之一，并且是一个模型， CNS神经退行性疾病的微连接组学技术进步的独特优势。 中央视网膜或中央凹介导高敏锐度视力，驱动大脑一半的活动，并且是一个关键位点 流行性致盲疾病。中央凹较小（<1 mm），可接近，与CNS疾病诊断相关 通过先进的细胞水平的临床成像。完整的中央凹微连接体包括两个不同的 神经回路，创造平行的视觉通路以及复杂的微连接， 神经外胚层起源的细胞类型、视网膜色素上皮（RPE）和Müller神经胶质。我们集团 开创了器官捐献者眼睛的极短恢复时间，以创造精美保存的视网膜组织。 体积适合于第一次microconnectomic分析的深入研究人类中枢神经系统结构。 这项提案的目标是通过改进和增加一个新的基因组来加速人类中央凹微连接体的形成。 非常成功和专业支持的软件平台，Dragonfly by Object Research Systems (ORS)，是实施深度学习方法以自动分割复杂 结构我们与ORS的合作将针对深度学习（DL）模型的开发，以及 注释和校对工具，将有广泛的适用性神经科学microconnectomics。在 我们的初步研究发现，RPE细胞产生了非常密集的神经样突起， 感光细胞和中央凹Müller神经胶质细胞同样与中央凹有特殊而复杂的关系。 微型电路此外，单个中央凹锥体光感受器对数十个平行视觉信号具有突触前作用。 极其复杂的电路为了进一步了解这些复杂的微连接体，ORS将 使用新开发的卷积神经网络增强快速自动分割， 快速注释、校对、数据可视化和定量分析工具。在目标1和2中，我们将 开发人类RPE细胞-神经元微连接体和Müller细胞的完整深度学习模型- 神经元微连接体，这将改变我们对这些细胞的关键作用的理解， 类型在中央凹功能和疾病中起作用。在目标3中，我们将开发一个多神经元的深度学习模型。 细胞类型和微连接体的平行视觉路径的形式，颜色和运动视觉。主要 其结果将是一个强大的、广泛使用的、专业支持的、基于DL的平台的转型， 广泛应用于神经科学微连接组学，通过免费许可证免费用于学术研究。的 ORS-Dragonfly平台将加速复杂CNS电路和冲击系统的微连接组学 神经科学、人类神经病理生理学和细胞水平临床成像的解释。这项建议 结合了神经生物学、视觉科学、临床眼科学和连接组学方面的专业知识和创新， DL软件开发与应用。

Project Summary The human retina is one of the most complex microcircuits of the central nervous system (CNS) and is a model of CNS neurodegenerative disease with unique advantages for microconnectomics technology advancement. The central retina or fovea mediates high acuity vision, drives activity in half of the brain, and is a critical locus for prevalent blinding disease. The fovea is small (<1 mm), accessible, and relevant to CNS disease diagnosis through advanced cellular-level clinical imaging. The full foveal microconnectome comprises both the diverse neural circuits that create parallel visual pathways as well as complex microconnectivity with two specialized cell types of neuroectodermal origin, the retinal pigment epithelium (RPE) and the Müller glia. Our group has pioneered ultra-short recovery times of eyes from organ donors, to create exquisitely preserved retinal tissue volumes suitable for the first microconnectomic analysis of an intensively investigated human CNS structure. The goal of this proposal is to accelerate the human foveal microconnectome by refining and augmenting a highly successful and professionally supported software platform, Dragonfly by Object Research Systems (ORS), an industry leader in implementation of deep learning methods for auto-segmentation of complex structure. Our collaboration with ORS will target development of deep learning (DL) models as well as annotation and proofreading tools that will have broad applicability to neuroscience microconnectomics. In preliminary studies we discovered that RPE cells give rise to extremely dense neural-like projections to photoreceptor cells and that foveal Müller glia similarly have a specialized and complex relationship to foveal microcircuits. Moreover, single foveal cone photoreceptors were presynaptic to dozens of parallel visual circuits of extreme complexity. To advance understanding of these complex microconnectomes ORS will augment fast auto-segmentation using newly developed convolutional neural networks and refine sophisticated tools for rapid annotation, proofreading, data visualization, and quantitative analysis. In Aims 1 and 2 we will develop complete deep learning models of the human RPE cell-neuronal microconnectome and the Müller cell- neuronal microconnectome respectively that will transform our understanding of the critical roles these cell types play in foveal function and disease. In Aim 3 we will develop a deep learning model of the multiple neural cell types and microconnectome of parallel visual pathways for form, color, and motion vision. The major outcome will be the transformation of a powerful, widely used, professionally supported, DL-based platform for broad application to neuroscience microconnectomics, free for academic research via a no-cost license. The ORS-Dragonfly platform will accelerate microconnectomics of complex CNS circuitry and impact systems neuroscience, human neuro-pathophysiology, and interpretation of cellular-level clinical imaging. This proposal combines expertise and innovation in neurobiology, vision science, clinical ophthalmology and connectomics, with DL software development and application.