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: 10689050
• 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/10689050
• 关键词: 3-Dimensional; Accessory Olfactory Bulbs; Active Learning; Afferent Neurons; Aggressive behavior; Algorithms; Animals; Architecture; Automobile Driving; Behavior; Blinking; Brain; Calcium; Cells; Cellular biology; Code; Collaborations; Communication; Compensation; Complex; Computing Methodologies; Cues; Data; Data Set; Development; Early Diagnosis; 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; 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; Reproducibility; Research; Research Personnel; Resolution; Role; Science; Stimulus; Techniques; Technology; Testing; Time; Time Series Analysis; Tissues; Training; Translating; Urine; Work; automated segmentation; calcium indicator; career; combinatorial; design; expectation; experimental study; fluorescence imaging; high dimensionality; image processing; improved; in vivo; information processing; interest; markov model; mathematical methods; multidisciplinary; nervous system disorder; neural; neural circuit; neural network; neuroimaging; novel strategies; programs; prototype; reproductive; 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.

项目总结/摘要 钙荧光成像为研究神经元如何连接提供了前所未有的机会。 大脑中可塑性处理信息的回路。显微镜和遗传编码的最新进展 钙指示剂使我们能够在真实的时间内记录细胞内Ca 2+的瞬时升高， 神经元的电活动。然而，人们对大脑中的信息处理机制知之甚少， 在单个神经元水平上的神经回路。即使尖端技术能够光学探测 成千上万的神经元与刺激或行为输出有关，我们仍然无法追踪这些神经元的活动。 神经元放电事件的传播。进步的关键障碍是缺乏计算技术， 用于钙成像数据的图像和信号处理。一种常见但尚未解决的钙收集障碍 来自所获取图像的神经元活动是成像期间活组织的变形。该项目的目标 是开发一种算法来自动提取单神经元活动的准确痕迹 使三维钙图像变形。正在开发的一种新方法产生了一个动态的感兴趣区域 通过从激发神经元的局部图像迭代地学习神经元身份，来针对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断框架， 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模型统计学地确定。 隐马尔可夫模型我们将使用Granger因果关系识别神经元活动之间的因果层次 推理，以重建大规模神经元群体的功能连接网络。 随后在单神经元水平上对连通性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。 候选人诺博士的长期职业目标是建立一个独立的研究计划， 在基于图像的动态神经系统的随机建模中， 时间序列分析本提案的培训目标是让Noh博士为以下方面做出独特的贡献： 复杂神经成像数据及其动力学的计算方法，并训练Noh博士获得 进行神经科学的假设驱动研究。建议的培训由Gaudenz指导 Danuser和Julian Meeks是计算细胞生物学和神经生物学领域的领导者， 分别从事信息学/实验和神经生物学的多样化环境，Noh博士将 沉浸在神经科学中，获得神经成像实验的经验学习，并获得 多学科团队科学的专业知识。这一提案的完成将使诺博士不仅能够 为神经影像学研究奠定基础，同时在多学科研究中发挥主导作用。