Mapping brain activity at scale with cluster computing
Mapping brain activity at scale with cluster computing
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DOI：
10.1038/nmeth.3041
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发表时间：
2014-09-01
期刊：
影响因子：
48
通讯作者：
Ahrens, Misha B.
中科院分区：
文献类型：
--
作者：
Freeman, Jeremy;Vladimirov, Nikita;Ahrens, Misha B.
了解大脑功能需要监测和解释行为过程中大型神经元网络的活动。记录技术的进步大大增加了神经数据的大小和复杂性。分析这些数据将成为神经科学的根本瓶颈。我们提出了一个名为Thunder的分析工具库，该工具构建在开源Apache Spark平台上，用于大规模分布式计算。该库实现了各种单变量和多变量分析，具有模块化的可扩展结构，非常适合交互式探索和分析开发。我们展示了这些分析如何在大规模神经数据中找到结构，包括来自虚构行为的斑马鱼幼虫的全脑光片成像数据和来自行为小鼠的双光子成像数据。这些分析将神经元的反应与感官输入和行为联系起来，在几分钟或更短的时间内运行，可以在私有集群或云中使用。因此，我们的开源框架有望将大脑活动映射工作转化为生物学见解。
Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.