Image-based modeling of functional connectivity in neural networks at single-cell resolution

单细胞分辨率神经网络功能连接的基于图像的建模

• 批准号: 10266100
• 负责人: Jungsik Noh
• 金额: $ 12.69万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-21 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266100
• 关键词: 3-Dimensional; Accessory Olfactory Bulbs; Active Learning; Afferent Neurons; Aggressive behavior; Algorithms; Animals; Architecture; Automobile Driving; Behavior; Biological; Blinking; Brain; Calcium; Cells; Cellular biology; Code; Collaborations; Complex; Computing Methodologies; Cues; Data; Data Set; Development; Economics; Environment; Etiology; Event; Goals; Graph; Image; Imaging technology; Individual; Informatics; Interdisciplinary Study; Laboratory Study; Learning; Light; Link; Logic; Manuals; Mentors; Methods; Microscopy; Modeling; Molecular; Monitor; Mus; Nervous system structure; Neurobiology; Neurons; Neurophysiology - biologic function; Neurosciences; Neurosciences Research; Olfactory Pathways; Optics; Organism; Output; Partner in relationship; Pathway Analysis; Pattern; Pheromone; Play; Population; Procedures; Process; Property; Randomized; Reproducibility; Research; Research Personnel; Resolution; Role; Science; Stimulus; Techniques; Technology; Testing; Time; Time Series Analysis; Tissues; Training; Translating; Urine; Work; base; calcium indicator; career; combinatorial; design; expectation; experimental study; fluorescence imaging; high dimensionality; image processing; improved; in vivo; information processing; interest; markov model; mathematical methods; microscopic imaging; multidisciplinary; nervous system disorder; neural circuit; neural network; neuroimaging; novel strategies; programs; prototype; relating to nervous system; reproductive; response; sex; signal processing; social; statistics; tool; vomeronasal organ

Project Summary/Abstract Calcium fluorescence imaging has opened unprecedented opportunities to investigate how neurons are wired in circuits that plastically process information in the brain. Recent advances in microscopy and genetically encoded calcium indicators allow us to record in real time the transient rises of intracellular Ca2+ for a large population of neurons during their electrical activity. However, little is known about mechanisms of information processing in neural circuits at the single neuron level. Even though cutting-edge technologies are capable of optically probing thousands of neurons firing in relation to stimulation or behavior output, we are still unable to track the propagation of the neuron firing events. The key barrier to progress is the lack of computational technologies in image and signal processing for the calcium imaging data. A common but unresolved obstacle to collect calcium activities of neurons from acquired images is deformation of live tissues during imaging. The goal of the project for image processing is to develop an algorithm to automatically extract accurate traces of single-neuron activity from deforming 3D calcium images. A new approach under development generates a dynamic region-of-interest for each jittering and blinking neuron by iteratively learning neuronal identities from local images of firing neurons. As a next step, the goal for signal processing is to develop statistical inference frameworks that can assess the evidence of information flows from external stimuli to sensory neurons, and between interconnected neurons. The responsiveness of neurons upon stimulation will be statistically determined based on an autoregressive hidden Markov model. We will identify causal hierarchy among neuronal activities using Granger-causality inference, in order to reconstruct the functional connectivity networks for large-scale neuronal populations. Subsequent graph theoretical quantification of the connectivity networks at the single-neuron level will enable us to differentiate wiring architectures of neural circuits under different molecular conditions. The long-term career goal of the candidate, Dr. Noh, is to establish an independent research program specialized in image-based stochastic modeling of dynamic nervous systems by translating his expertise in statistics and time series analysis. The training objective of this proposal is to allow Dr. Noh to make a unique contribution to computational methods for complex neuroimaging data and its dynamics, and to train Dr. Noh to gain the ability to conduct hypothesis-driven research for neuroscience by himself. The proposed training is guided by Gaudenz Danuser and Julian Meeks, who are leaders in the fields of computational cell biology and neurobiology, respectively. Being engaged in diverse environment of informatics/experiments and neurobiology, Dr. Noh will immerse himself into neuroscience, acquire experiential learning of neuroimaging experiments, and gain expertise in multidisciplinary team science. The completion of this proposal will enable Dr. Noh not only to establish his groundwork for research in neuroimaging, but also to play leading roles in multidisciplinary research.

项目摘要/摘要 钙荧光成像为研究神经元是如何连接的提供了前所未有的机会 大脑中处理信息的可塑性回路。显微技术和遗传编码技术的最新进展 钙指示剂使我们能够实时记录一大群人细胞内钙离子的瞬时上升 神经元在它们的电活动中。然而，人们对信息处理机制知之甚少。 单个神经元水平的神经回路。即使尖端技术能够通过光学探测 数以千计的神经元放电与刺激或行为输出有关，我们仍然无法追踪 神经元放电事件的传播。取得进展的关键障碍是缺乏计算技术 钙离子成像数据的图像和信号处理。收集钙的一个常见但尚未解决的障碍 从获取的图像中获得的神经元的活动是成像过程中活组织的变形。该项目的目标是 用于图像处理的是开发一种算法来自动提取单个神经元活动的准确痕迹 使3D钙质图像变形。正在开发的一种新方法产生了一个动态的感兴趣区域 通过反复地从放电神经元的局部图像中学习神经元的身份，来识别每个抖动和闪烁的神经元。 下一步，信号处理的目标是开发统计推理框架，该框架可以评估 证据表明，信息从外部刺激流向感觉神经元，并在相互连接的神经元之间流动。 神经元对刺激的反应性将基于自回归的统计方法来确定 隐马尔可夫模型。我们将使用格兰杰因果关系来确定神经元活动之间的因果等级 推断，以重建大规模神经元群体的功能连接网络。 随后在单个神经元水平上对连接网络的图论量化将使我们能够 以区分不同分子条件下神经回路的布线结构。 候选人卢博士的长期职业目标是建立一个专门的独立研究项目 在动态神经系统的基于图像的随机建模方面，他将其在统计学和 时间序列分析。这项建议的培训目标是让卢博士为 复杂神经影像数据的计算方法及其动力学，并训练卢博士获得 独自为神经科学进行假说驱动的研究。拟议的培训由Gaudenz指导 丹尼瑟和朱利安·米克斯是计算细胞生物学和神经生物学领域的领导者， 分别进行了分析。在信息学/实验和神经生物学的不同环境中，卢博士将 沉浸在神经科学中，获得神经成像实验的体验式学习，并获得 多学科团队科学方面的专业知识。这项建议的完成将使卢博士不仅能够 他不仅为神经成像研究奠定了基础，还在多学科研究中发挥了主导作用。