Next-Generation Calcium Imaging Analysis Methods

项目摘要 钙离子成像方法使我们能够用单个细胞记录多个神经元的同时活动， 因此，这些方法是BRAIN倡议和神经科学的关键使能工具 更广泛地说。这些实验产生了巨大的2D或3D视频数据集-在某些情况下具有数据速率 以TB/小时为单位进行测量，而对这种“大数据”的分析目前是 这一领域的科学进步。该项目开发了强大的新的分析方法，以消除这一点。 瓶颈，开辟了新的科学问题和应用，可以用这些新工具来攻击。 正在开发的方法同时识别成像神经元的位置，空间分辨 重叠的神经元形状，并提供每个神经元活动的去噪估计， 手动参数调整。这些新方法在定量和定性上都比现有技术有所改进 在模拟数据和各种真实的数据应用中，导致有用信号的恢复 比其他方式更多的神经元。同时，该方法在计算上是 可扩展和模块化，实现健康的用户和开发社区。最后，这些方法是 可扩展性：它们建立在定义良好的概率建模和凸优化原理上， 使一系列扩展能够解决重要的新科学问题。 该项目的具体目标包括一些关键的分项目，重点是： 处理非常大的数据集，尽可能有效地计算，以实现闭环，实时 实验;第二，加强获得统计最优解的方法，以提取 尽可能多的数据信息，尽可能高的时空分辨率， 新的综合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，以处理不同的数据类型：空间模糊的数据，或通过一些 更复杂的线性成像变换（例如，从光场相机）;成像数据，我们可以 通过利用错误记录的刺激或行为信息来限制和改进我们的结果; 以及最后，与高时间分辨率多电极电数据同时记录的成像数据， 以便联合收割机结合这两种数据类型的优点。 所提出的分析工具将广泛用于神经科学界，并将具有很强的 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 实验范例和驱动未来的数据收集。

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.