Accelerating discovery of the human foveal microconnectome with deep learning

通过深度学习加速人类中心凹微连接组的发现

• 批准号: 10411154
• 负责人: DENNIS MICHAEL DACEY
• 金额: $ 109.99万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10411154
• 关键词: Address; Adopted; Age related macular degeneration; Aging; Amaze; Apical; Artificial Intelligence; Brain; Brain Diseases; Cells; Cellular Morphology; Central Nervous System Diseases; Clinical; Collaborations; Color; Color Visions; Complex; Computer software; Cone; Cytoplasm; Data; Development; Diabetic Retinopathy; Diagnostic Imaging; Dietary Component; Disease; Electron Microscopy; Eye; Floor; Form Perception; Functional disorder; Future; Goals; Human; Image; Industry; Ion Channel; Lead; Licensing; Link; Machine Learning; Manuals; Measures; Mediating; Membrane; Methods; Modeling; Motion; Muller' s cell; Mus; Neocortex; Nervous system structure; Neuraxis; Neurobiology; Neurodegenerative Disorders; Neuroglia; Neurons; Neurophysiology - biologic function; Neurosciences; Ophthalmology; Organ Donor; Organelles; Outcome; Output; Photoreceptors; Pigments; Play; Process; Recovery; Research; Retina; Retinal Cone; Retinal Diseases; Rod; Role; Short Interspersed Nucleotide Elements; Signal Transduction; Software Tools; Spatial Distribution; Structure; Structure of retinal pigment epithelium; Supporting Cell; Surface; Synapses; System; Techniques; Technology; Testing; Time; Tissues; Vision; Visual; Visual Pathways; Xanthophylls; automated segmentation; base; cell type; clinical diagnostics; clinical imaging; convolutional neural network; cost; data visualization; deep learning; deep learning model; density; disease diagnosis; fly; fovea centralis; innovation; learning strategy; microscopic imaging; nanoscale; neural circuit; novel; postsynaptic; preservation; presynaptic; rapid technique; reconstruction; relating to nervous system; retinal imaging; retinal neuron; software development; systems research; tool; vision science

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.

项目摘要 人类视网膜是中枢神经系统(CNS)最复杂的微电路之一，是一种 对中枢神经系统退行性疾病具有独特优势的微连接技术的进步。 中央视网膜或中央凹调节高视力，驱动半个大脑的活动，是一个关键部位。 针对流行的失明疾病。中心凹很小(1毫米)，容易接近，与中枢神经系统疾病的诊断有关 通过先进的细胞级临床成像。完整的中心凹微连接包括不同的 神经回路创建平行的视觉通路以及复杂的微连接，与两个专门的 神经外胚层起源的细胞类型，视网膜色素上皮(RPE)和Müler胶质细胞。我们的团队已经 首创从器官捐赠者那里恢复眼睛的超短时间，创造出保存完好的视网膜组织 体积适合于对深入研究的人类中枢神经系统结构进行第一次微连接分析。 这项提议的目标是通过提炼和增强一种 高度成功和专业支持的软件平台，蜻蜓by Object Research Systems (ORS)，在实施深度学习方法以实现复杂的自动分割方面的行业领导者 结构。我们与ORS的合作将致力于深度学习(DL)模型的开发以及 对神经科学微连接学有广泛适用性的注释和校对工具。在……里面 初步研究发现，RPE细胞会产生非常密集的神经样投射， 光感受器细胞和中心凹Müler胶质细胞与中心凹同样具有特殊而复杂的关系 微电路。此外，单个中心凹视锥感光感受器与数十个平行视觉感受器发生突触前反应。 极其复杂的电路。为了促进对这些复杂的微连接的理解，ORS将 使用最新开发的卷积神经网络增强快速自动分割并精炼复杂 用于快速注释、校对、数据可视化和定量分析的工具。在目标1和2中，我们将 建立完整的人视网膜色素上皮细胞-神经元微连接组和Müler细胞的深度学习模型 这将改变我们对这些细胞的关键作用的理解 类型在中心凹功能和疾病中起作用。在目标3中，我们将建立多个神经网络的深度学习模型 形态、颜色和运动视觉的平行视觉通路的细胞类型和微连接。少校 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 广泛应用于神经科学微连接学，通过免费许可证免费进行学术研究。这个 ORS-蜻蜓平台将加速复杂CNS电路和撞击系统的微连接 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、临床眼科和连接学的专业知识和创新， 具备数字图书馆软件的开发和应用。