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

基本信息

  • 批准号:
    10000915
  • 负责人:
  • 金额:
    $ 35.68万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-09-20 至 2023-06-30
  • 项目状态:
    已结题

项目摘要

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。现有的统计模型 并且方法不足以处理这种复杂和噪声数据。这项建议涉及建立统计模型, 用于分析钙成像数据的方法。 目标1涉及去卷积神经元的荧光轨迹,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,用于神经元尖峰对钙动力学的影响, 自然地导致先前被认为在计算上难以处理的非凸优化问题。一个可扩展 在线算法将被开发用于解决这个非凸优化问题,从而大大改善 竞争的方法。这种方法将被扩展到执行尖峰反卷积,同时允许 神经元的尖峰对钙动力学的影响采取完全非参数形式。 用于量化神经元的活动与感兴趣的协变量之间的关联的现有方法假设 它是由一个单一的模式,适用于所有试验。然而,这一假设似乎并不成立。 对于钙成像数据,其特征在于单个神经元活动中的大量异质性(以及 与协变量的相关性)。目标2涉及开发一个混合模型,用于 神经元的活动和感兴趣的协变量,这可以充分捕获跨试验的真实世界异质性。 研究人员通常为每个神经元拟合一个单独的模型,以量化该神经元之间的关联。 罗恩的活动和相关的协变量。目标3涉及在ρ神经元群体中“借用力量”, 假设群体中的每个神经元遵循L个响应模型之一,其中L <<p。神经元联合- 与给定的响应模型相关联的细胞可以被视为“功能细胞类型”;因此,这种方法不仅会导致 识别功能细胞类型,而且还可以更准确地估计管理每个神经元的模型 发射率,以及对神经动力学更精细的理解。 最后,目标4涉及开发实现模型和方法的高质量开源软件 本建议中提出的,以及两个最终用户仔细评估这些工具的计划:一个理论家和 一个实验家 本提案中开发的模型和方法的动机是来自艾伦公司的数据,并将应用于这些数据。 Brain Observatory是一个大规模的公开的钙成像数据库,来自小鼠的视觉皮层, 被暴露在五种视觉刺激下。研究人员将创建高质量的公开软件, 实施本提案中提出的模型和方法。开发的所有工具(模型、方法和软件) 将与最终用户合作进行评估。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Michael Buice其他文献

Michael Buice的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Michael Buice', 18)}}的其他基金

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
  • 财政年份:
    2018
  • 资助金额:
    $ 35.68万
  • 项目类别:

相似海外基金

CAREER: Next-generation of Wirelessly Powered Implantable Neuromodulation and Electrophysiological Recording System for Long-term Behavior Study of Freely-Moving Animals
职业:下一代无线供电植入式神经调节和电生理记录系统,用于自由移动动物的长期行为研究
  • 批准号:
    2309413
  • 财政年份:
    2022
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Continuing Grant
Developing remote monitoring system of aquatic animals' behavior and ecology to reform ecosystem conservation
开发水生动物行为和生态远程监测系统改革生态系统保护
  • 批准号:
    22K18432
  • 财政年份:
    2022
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Grant-in-Aid for Challenging Research (Pioneering)
OCE-PRF: Cliff Hangers: Investigating Effects of a Submarine Canyon on the Distribution and Behavior of Midwater Animals and their Predators
OCE-PRF:悬崖吊架:调查海底峡谷对中层水域动物及其捕食者的分布和行为的影响
  • 批准号:
    2126537
  • 财政年份:
    2021
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Standard Grant
CAREER: Next-generation of Wirelessly Powered Implantable Neuromodulation and Electrophysiological Recording System for Long-term Behavior Study of Freely-Moving Animals
职业:下一代无线供电植入式神经调节和电生理记录系统,用于自由移动动物的长期行为研究
  • 批准号:
    1943990
  • 财政年份:
    2020
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Continuing Grant
Study on factors that increase or decrease the vigilance behavior of wild animals: the effect of species differences and visual stimuli
野生动物警觉行为增减因素研究:物种差异和视觉刺激的影响
  • 批准号:
    20K06353
  • 财政年份:
    2020
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Neural circuit underlying flexible behavior in animals
动物灵活行为的神经回路
  • 批准号:
    19H01769
  • 财政年份:
    2019
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Analysis of adaptive mechanisms in chemical localization behavior of animals by using novel devices to intervene in sensory and motor functions
使用新型装置干预感觉和运动功能来分析动物化学定位行为的适应性机制
  • 批准号:
    19H02104
  • 财政年份:
    2019
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Life Cost Strategy for Wild Animals Using Wearable Behavior Recording Devices and Telomere Measurement
使用可穿戴行为记录设备和端粒测量的野生动物生命成本策略
  • 批准号:
    18K14788
  • 财政年份:
    2018
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Modeling and application of energy-efficient behavior in calling animals
动物呼叫节能行为建模及应用
  • 批准号:
    18K18005
  • 财政年份:
    2018
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Cooperative behavior of non-human animals focusing on reward sharing -comparison between rodents and birds-
注重奖励分享的非人类动物的合作行为-啮齿类动物与鸟类的比较-
  • 批准号:
    18K12020
  • 财政年份:
    2018
  • 资助金额:
    $ 35.68万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了