Models and Methods for Calcium Imaging Data with Application to the Allen Brain Observatory

应用于艾伦脑天文台的钙成像数据模型和方法

• 批准号: 9789279
• 负责人: Michael Buice
• 金额: $ 34.44万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789279
• 关键词: Algorithms; Animals; Behavior; Biological; Biophysics; Brain; Calcium; Characteristics; Collaborations; Complex; Computer software; Consensus; Data; Data Analyses; Data Set; Dependence; Development; Disease; Electrophysiology (science); Evaluation; Exhibits; Exposure to; Fluorescence; Future; Goals; Heterogeneity; Image; Imaging technology; Institutes; Lead; Measures; Methods; Modeling; Mus; Neurons; Neurosciences; Noise; Population; Probability; Process; Pythons; Research Personnel; Resolution; Sampling; Science; Statistical Methods; Statistical Models; Surveys; Techniques; Technology; Time; Training; Translating; Visual Cortex; cell type; in vivo calcium imaging; insight; interest; method development; neural correlate; neural model; open source; relating to nervous system; repository; response; rho; software development; tool; two-photon; visual stimulus

PROJECT SUMMARY. New advances in calcium imaging make it possible to survey the brains of behaving animals at single-neuron resolution, thereby promising to transform the field of neuroscience. However, existing statistical models and methods are inadequate for this complex and noisy data. This proposal involves developing statistical models and methods for the analysis of calcium imaging data. Aim 1 involves deconvolving a neuron's fluorescence trace in order to infer its underlying spike times. A number of authors have considered a simple auto-regressive model for the effect of a neuron's spike on calcium dynamics, which leads naturally to a non-convex optimization problem previously thought to be computationally intractable. A scalable online algorithm will be developed for solving this non-convex optimization problem, leading to drastic improvements over competing approaches. This approach will be extended to perform spike deconvolution while allowing for the effect of a neuron's spike on calcium dynamics to take a completely non-parametric form. Existing approaches for quantifying the association between a neuron's activity and covariates of interest assume that it is governed by a single model, which applies across all trials. However, this assumption appears not to hold for calcium imaging data, which is characterized by a huge amount of heterogeneity in a single neuron's activity (and association with covariates) across trials. Aim 2 involves developing a mixture model for the association between a neuron's activity and covariates of interest, which can adequately capture real-world heterogeneity across trials. Researchers typically fit a separate model for each neuron in order to quantify the association between that neu- ron's activity and the covariates of interest. Aim 3 involves “borrowing strength” across a population of ρ neurons, by assuming that each neuron in the population follows one of L response models, where L << ρ. The neurons associ- ated with a given response model can be viewed as a “functional cell type”; thus, this approach will lead not only to the identification of functional cell types, but also to more accurate estimation of the model that governs each neuron's firing rate, and a more refined understanding of neural dynamics. Finally, Aim 4 involves the development of high-quality open source software implementing the models and methods developed in this proposal, as well as plans for the careful evaluation of these tools by two end-users: a theorist and an experimentalist. The models and methods developed in this proposal are motivated by, and will be applied to, data from the Allen Brain Observatory, a large-scale publicly-available repository of calcium imaging data from the visual cortex of mice that were exposed to five types of visual stimuli. The investigators will create high-quality publicly-available software that implements the models and methods developed in this proposal. All tools (models, methods, and software) developed in this proposal will be evaluated in collaboration with end-users.

项目摘要。钙成像的新进展使研究行为动物的大脑成为可能 单神经元分辨率，从而有望改变神经科学领域。然而，现有的统计模型 并且方法不足以应对这种复杂且嘈杂的数据。该提案涉及开发统计模型和 钙成像数据分析方法。 目标 1 涉及对神经元的荧光轨迹进行反卷积，以推断其潜在的尖峰时间。一些 作者考虑了一个简单的自回归模型来描述神经元尖峰对钙动力学的影响，该模型 自然地导致了以前被认为在计算上难以解决的非凸优化问题。可扩展的 将开发在线算法来解决这个非凸优化问题，从而带来巨大的改进 超过竞争的方法。这种方法将被扩展以执行尖峰反卷积，同时允许 神经元尖峰对钙动力学的影响采取完全非参数形式。 量化神经元活动与感兴趣的协变量之间关联的现有方法假设 它由适用于所有试验的单一模型控制。然而，这个假设似乎并不成立 对于钙成像数据，其特点是单个神经元的活动存在巨大的异质性（并且 与协变量的关联）。目标 2 涉及开发一个混合模型来描述 神经元的活动和感兴趣的协变量，可以充分捕获试验中真实世界的异质性。 研究人员通常为每个神经元建立一个单独的模型，以量化该神经元之间的关联。 ron 的活动和感兴趣的协变量。目标 3 涉及在 ρ 神经元群体中“借用力量”，通过 假设群体中的每个神经元遵循 L 个响应模型之一，其中 L << ρ。神经元关联 具有给定反应模型的细胞可以被视为“功能细胞类型”；因此，这种方法不仅会导致 功能细胞类型的识别，而且还可以更准确地估计控制每个神经元的模型 放电率，以及对神经动力学的更精细的理解。 最后，目标 4 涉及开发实现模型和方法的高质量开源软件 该提案中制定了这些工具，以及由两个最终用户仔细评估这些工具的计划：理论家和 一个实验主义者。 本提案中开发的模型和方法受到艾伦研究中心数据的启发，并将应用于该数据。 Brain Observatory，一个大规模公开的小鼠视觉皮层钙成像数据存储库， 暴露于五种视觉刺激。研究人员将创建高质量的公开软件 实施本提案中开发的模型和方法。开发的所有工具（模型、方法和软件） 在本提案中，将与最终用户合作进行评估。