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 人视网膜是中枢神经系统（CNS）最复杂的微电路之一，是一个模型 中枢神经系统神经退行性疾病具有独特的微连接学技术进步的优势。 中央视网膜或中央凹介导高敏锐的视觉，在一半的大脑中驱动活动，是一个关键的基因座 用于普遍的盲目疾病。 Fovea很小（<1 mm），可访问且与CNS疾病诊断有关 通过晚期细胞级临床成像。完整的凹起的微核心均包含潜水员 神经回路产生平行的视觉途径以及具有两个专业的复杂微连接性 神经外科起源的细胞类型，视网膜色素上皮（RPE）和Müller神经胶质。我们的小组有 从器官捐赠者的开拓性超短缺的恢复时间，以创建精确保存的永久性组织 适用于对人类中枢神经系统结构进行深入研究的首次微连接分析的体积。 该提案的目的是通过完善和增强A加速人类中央凹 非常成功且受专业支持的软件平台，对象研究系统的蜻蜓 （ORS），实施深度学习方法的行业领导者自动分割复杂 结构。我们与ORS的合作将针对深度学习（DL）模型的发展以及 注释和校对工具将对神经科学微连接学具有广泛的适用性。在 初步研究，我们发现RPE细胞引起了非常密集的神经样项目 感光细胞和Fovealmüller胶质类似地与Foveal具有专业且复杂的关系 微电路。此外，单一凹锥光感受体是数十个平行视觉的突触前的 极端复杂性的电路。提高对这些复杂的微社会切除术的理解将 使用新开发的卷积神经网络增强快速自动细分，并精致 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 开发人类RPE细胞神经元微角核和müller细胞的完整深度学习模型 - 神经元微角色分别将改变我们对这些细胞的关键作用的理解 类型在动脉功能和疾病中发挥作用。在AIM 3中，我们将开发出多重中性的深度学习模型 平行视觉途径的细胞类型和微连接组，形成，颜色和运动视觉。专业 结果将是一个强大，广泛使用，专业支持，基于DL的平台的转变 广泛应用于神经科学微问题，免费用于通过无成本许可进行学术研究。这 ORS-Dragonfly平台将加速复杂CNS电路和冲击系统的微共同体学 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。这个建议 结合神经生物学，视觉科学，临床眼科和连接组学方面的专业知识和创新， 使用DL软件开发和应用。