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: 10054899
• 负责人: Jungsik Noh
• 金额: $ 12.84万
• 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/10054899
• 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 钙荧光成像已经为研究神经元如何连接开放了前所未有的机会 塑料处理大脑信息的电路。显微镜和遗传编码的最新进展 钙指标使我们能够实时记录大量细胞内Ca2+的瞬态上升 神经元在其电活动过程中。但是，关于信息处理的机制知之甚少 单个神经元水平的神经回路。即使尖端技术能够光学探测 与刺激或行为输出有关的成千上万的神经元发射，我们仍然无法跟踪 神经元射击事件的传播。进步的关键障碍是缺乏计算技术 钙成像数据的图像和信号处理。收集钙的常见但尚未解决的障碍 从获得图像中的神经元的活性是成像过程中活组织的变形。项目的目标 用于图像处理是开发一种算法，以自动提取单神经活动的精确痕迹 从变形3D钙图像。开发的新方法产生了动态的利益 通过迭代地学习射击神经元图像的神经元身份，每个抖动和眨眼的神经元。 下一步，信号处理的目标是开发可以评估的统计推理框架 信息的证据从外部刺激到感觉神经元以及互连神经元之间。 神经元在刺激时的反应能力将根据自回旋的统计确定 隐藏的马尔可夫模型。我们将使用Granger-Causality确定神经元活动之间的因果关系 推断，为了重建大规模神经元种群的功能连接网络。 随后的图理论量化单神经元级别的连接网络将使我们能够 在不同的分子条件下区分神经回路的接线体系结构。 候选人NOH博士的长期职业目标是建立一个独立的研究计划专业 在基于图像的动态神经系统的随机建模中，通过翻译其统计和 时间序列分析。该建议的培训目标是允许NOH博士为 用于复杂神经影像数据及其动态的计算方法，并训练NOH博士以获得能力 对自己进行假设驱动的神经科学研究。拟议的培训由高丹兹指导 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。 Noh博士将参与信息学/实验和神经生物学的各种环境 将自己浸入神经科学中，获得神经影像学实验的体验式学习，并获得 多学科团队科学方面的专业知识。该提案的完成将使NOH博士不仅能够 建立他的神经影像学研究的基础，同时在多学科研究中发挥主导作用。