Next-Generation Calcium Imaging Analysis Methods

下一代钙成像分析方法

• 批准号: 9357586
• 负责人: Liam M Paninski
• 金额: $ 36万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9357586
• 关键词: Address; BRAIN initiative; Behavioral; Big Data; Brain; Calcium; Cells; Communities; Community Developments; Computing Methodologies; Data; Data Collection; Data Set; Dendrites; Development; Dimensions; Dreams; Drowning; Event; Future; Genetic; Goals; Hour; Image; Image Analysis; Light; Literature; Location; Manuals; Measures; Methods; Microscopy; Modeling; Modernization; Motion; Motivation; Mus; Neurons; Neurosciences; Optics; Output; Performance; Recovery; Resolution; Shapes; Signal Transduction; Somatosensory Cortex; Statistical Models; Stimulus; Structure; Techniques; Technology; Time; Trees; Zebrafish; analytical tool; experimental study; extracellular; high resolution imaging; imaging modality; improved; microscopic imaging; multi-electrode arrays; next generation; novel; novel strategies; prototype; relating to nervous system; scale up; spatiotemporal; temporal measurement; terabyte; tool

Project Summary Calcium imaging methods allow us to record the simultaneous activity of many neurons with single-cell resolution; these methods are therefore a critical enabling tool for the BRAIN initiative and in neuroscience more broadly. These experiments produce enormous 2D or 3D video datasets – in some cases with data rates measured in terabytes/hour - and the analysis of this “big data” currently represents a major bottleneck on scientific progress in this field. This project develops powerful new analysis methods for eliminating this bottleneck, opening up new scientific questions and applications that can be attacked with these new tools. The methods under development simultaneously identify the locations of the imaged neurons, resolve spatially overlapping neuronal shapes, and provide denoised estimates of the activity of each neuron, with minimal manual parameter tuning. The new methods quantitatively and qualitatively improve upon the state of the art in both simulated data and in a wide variety of real data applications, leading to the recovery of useful signals from many more neurons than otherwise possible. At the same time, the methods are computationally scalable and modular, enabling a healthy user and development community. Finally, the methods are extensible: they are founded on well-defined probabilistic modeling and convex optimization principles, enabling a range of extensions to address important new scientific problems. Specific aims of the project include a number of critical subprojects focused on: first, scaling up these methods to handle very large data sets, as computationally efficiently as possible, to enable closed-loop, real-time experiments; and second, strengthening the methods to obtain statistically optimal solutions, in order to extract as much information from the data as possible, with the highest possible spatiotemporal resolution, enabling the development of novel integrated computational imaging methods. In parallel, this project will develop extensions of these methods to handle different data types: spatially blurred data, or data formed via some more complicated linear imaging transformation (e.g., from light-field cameras); imaging data in which we can constrain and improve our results by exploiting simultaneously-recorded stimulus or behavioral information; and finally, imaging data recorded simultaneously with high-temporal-resolution multielectrode electrical data, in order to combine the strengths of these two data types. The proposed analytical tools will be widely used in the neuroscience community, and will have a strong influence on fundamental approaches to understanding neuroscience data; furthermore, the project will inform experimental paradigms and drive future data collection.

项目概要 钙成像方法使我们能够用单细胞记录许多神经元的同时活动 解决;因此，这些方法是 BRAIN 计划和神经科学领域的重要支持工具 更广泛地说。这些实验产生了大量的 2D 或 3D 视频数据集——在某些情况下具有数据速率 以 TB/小时为单位测量 - 这种“大数据”的分析目前是一个主要瓶颈 该领域的科学进展。该项目开发了强大的新分析方法来消除这种情况 瓶颈，开辟了新的科学问题和应用程序，可以用这些新工具来解决。 正在开发的方法同时识别成像神经元的位置，解析空间 重叠的神经元形状，并提供每个神经元活动的去噪估计，以最小的 手动参数调整。新方法在数量和质量上改进了现有技术 在模拟数据和各种实际数据应用中，导致有用信号的恢复 来自比其他方式更多的神经元。同时，该方法通过计算 可扩展和模块化，支持健康的用户和开发社区。最后，方法是 可扩展：它们建立在明确定义的概率建模和凸优化原理的基础上， 实现一系列扩展以解决重要的新科学问题。 该项目的具体目标包括一些关键子项目，重点关注：首先，扩大这些方法的规模 尽可能高效地处理非常大的数据集，以实现闭环、实时 实验；其次，加强获得统计最优解的方法，以提取 尽可能多的数据信息，以及尽可能高的时空分辨率，使得 新型集成计算成像方法的发展。与此同时，该项目将开发 这些方法的扩展可以处理不同的数据类型：空间模糊的数据，或通过某些方法形成的数据 更复杂的线性成像变换（例如，来自光场相机）；图像数据，我们可以 通过利用同时记录的刺激或行为信息来限制和改善我们的结果； 最后，与高时间分辨率多电极电数据同时记录的成像数据， 为了结合这两种数据类型的优点。 所提出的分析工具将广泛应用于神经科学界，并将具有强大的 对理解神经科学数据的基本方法的影响；此外，该项目将告知 实验范式并推动未来的数据收集。