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

项目总结。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，从而有望改变神经科学领域。然而，现有的统计模型 而对于这种复杂且有噪声的数据，方法是不够的。这项建议涉及开发统计模型和 方法对钙质影像资料进行分析。 目标1涉及对神经元的荧光轨迹进行去卷积，以推断其潜在的尖峰时间。一批 作者考虑了一个简单的自回归模型，用于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处理的非凸优化问题。一个可扩展的 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 关于相互竞争的方法。这种方法将被扩展以执行尖峰去卷积，同时允许 神经元尖峰对钙动力学的影响采取完全非参数的形式。 现有的量化神经元活动和感兴趣的协变量之间的关联的方法假设 它由一个单一的模式管理，适用于所有试验。然而，这一假设似乎不成立。 对于钙成像数据，其特征是单个神经元的活动具有巨大的异质性(和 与协变量的关联)。目标2涉及开发一种混合模型，用于在 神经元的活动和感兴趣的协变量，这可以充分捕捉真实世界的异质性跨越试验。 研究人员通常为每个神经元拟合一个单独的模型，以便量化神经元间的联系 罗恩的活动和感兴趣的协变量。目标3涉及通过以下方式在一组ρ神经元中“借力” 假设种群中的每个神经元遵循一种L反应模型，其中L&lt；ρ。相关的神经元- 与给定的响应模型相关联可以被视为一种“功能细胞类型”；因此，这种方法不仅会导致 识别功能细胞类型，但也更准确地估计管理每个神经元的模型 射击率，以及对神经动力学更精细的理解。 最后，目标4涉及实现模型和方法的高质量开源软件的开发 以及由两个最终用户仔细评估这些工具的计划：一名理论家和一名 一个实验者。 本提案中开发的模型和方法以艾伦的数据为基础，并将应用于这些数据。 脑观察站，一个大规模的公开可用的小鼠视觉皮质钙成像数据的储存库， 暴露在五种类型的视觉刺激下。调查人员将创建高质量的公开可用的软件 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) 将与最终用户合作对本提案中的项目进行评估。

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.