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

基本信息

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
项目类别：
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&apos 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

项目摘要

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.

项目概要人类视网膜是中枢神经系统（CNS）最复杂的微电路之一，是一个模型中枢神经系统退行性疾病的研究对于微连接组学技术的进步具有独特的优势。中央视网膜或中央凹介导高敏锐度视力，驱动一半大脑的活动，是一个关键部位对于常见的致盲疾病，中央凹很小（<1毫米），易于接近，并且与中枢神经系统疾病诊断相关。通过先进的细胞水平临床成像，完整的中心凹微连接组包括不同的部分。神经回路创建并行的视觉通路以及复杂的微连接与两个专门的我们小组有神经外胚层来源的细胞类型，视网膜色素上皮（RPE）和米勒神经胶质细胞。开创了器官捐献者眼睛超短恢复时间的先河，创造出保存完好的视网膜组织适合对深入研究的人类中枢神经系统结构进行首次微连接组分析的体积。该提案的目标是通过细化和增强人类中心凹微连接组来加速人类中心凹微连接组的发展。非常成功且专业支持的软件平台，Object Research Systems 的 Dragonfly (ORS)，实施自动分割复杂性的深度学习方法的行业领导者我们与 ORS 的合作将针对深度学习 (DL) 模型以及开发。注释和校对工具将广泛适用于神经科学微连接组学。初步研究我们发现 RPE 细胞产生极其密集的神经样投射感光细胞和中央凹米勒神经胶质细胞同样与中央凹有特殊而复杂的关系此外，单个中心凹锥光感受器突触前有数十个平行视觉。为了加深对这些复杂微连接体的理解，ORS 将使用新开发的卷积神经网络增强快速自动分割并完善复杂的在目标 1 和 2 中，我们将提供用于快速注释、校对、数据可视化和定量分析的工具。开发人类 RPE 细胞神经元微连接组和 Müller 细胞的完整深度学习模型每个神经微连接组都将改变我们对这些细胞关键作用的理解在目标 3 中，我们将开发多神经元的深度学习模型。形状、颜色和运动视觉的平行视觉通路的细胞类型和微连接组。结果将是一个强大的、广泛使用的、专业支持的、基于深度学习的平台的转变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。 ORS-Dragonfly 平台将加速复杂中枢神经系统电路和影响系统的微连接组学神经科学、人类神经病理生理学以及细胞水平临床成像的解释。结合了神经生物学、视觉科学、临床眼科和连接组学方面的专业知识和创新，深度学习软件开发和应用。