Enhancing automated, reproducible analysis workflows and data curation for extracellular neural recordings with SpikeInterface

使用 SpikeInterface 增强细胞外神经记录的自动化、可重复分析工作流程和数据管理

• 批准号: BB/X01861X/1
• 负责人: Matthias Hennig
• 金额: $ 95.27万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX01861X%2F1
• 关键词: Enhancing; automated; reproducible; analysis; workflows

How our brains give rise to cognition, thought and behaviour is one of the great unanswered questions in science today. Answering this question will impact fields ranging from medicine to artificial intelligence. The last decade has brought major innovations in technologies to precisely record the activity of large numbers of neurons in the brain. These advances allow, for the first time, the systematic study of the role of interactions between neurons in neural circuits and between brain areas at a large scale. However, the size and complexity of the resulting data sets is considerable and requires new approaches and methods for their analysis and interpretation. As in other research fields such as physics and genomics, the availability of new data has led to a community effort to tackle data analysis. Numerous algorithms and software tools are available to researchers, yet their effective use in neuroscience labs still requires technical expertise that is not always easily available. Moreover, labs typically implement hand-crafted analysis workflows that are not portable and may be difficult to maintain as the original developers move on. This, in turn, makes it difficult to integrate new developments and can limit reproducibility as results may depend on specific but undocumented elements of analysis workflows. To address this, we developed SpikeInterface, a software framework to unify access to data and major existing algorithms and tools for data processing and analysis. This successful open-source project has been used to support analysis in data-intensive research studies and has received numerous contributions from the research community. Here we propose to build on SpikeInterface to address two major open problems: 1) How to automate data curation; and 2) How to abstract workflows so they can be reproducible and designable without specialist programming expertise. Data curation is an essential part of analysis workflows as existing algorithms, for instance to isolate the activity of single neurons, are imperfect. Usually this is a time-consuming, manual process that severely limits the data volume a lab can realistically work with. To improve throughput and reproducibility, we will develop novel machine-learning approaches to automate data curation. To improve reproducibility and accessibility of these and the many other methods in SpikeInterface, we will add functionality to handle abstract representations of workflows. This will allow the full provenance of analysis workflows to be easily documented, and will enable data analysis to be created and run without the need to write program code. This, in turn, will be the basis for a web browser-based user interface to design, test and execute workflows. With flexible data access and pipelining SpikeInterface already implements, this will allow de-centralised solutions, for instance an analysis may be run locally, on a remote machine, or on a cloud-based service. Furthermore, the project team will support the research community in adoption and use of these tools, and will continue to maintain and improve the SpikeInterface software. Together this effort will make cutting-edge analysis methods available to the thousands of neuroscience labs now adopting large scale recording technologies, it will enable collaborative analysis of large data sets, and simplify sharing and re-use of valuable data sets for further discovery.

我们的大脑是如何产生认知、思想和行为的，这是当今科学界尚未回答的重大问题之一。解决这个问题将影响从医学到人工智能的各个领域。在过去的十年里，技术上的重大创新带来了精确记录大脑中大量神经元活动的技术。这些进展首次允许系统地研究神经回路中神经元之间以及大脑区域之间的相互作用。然而，由此产生的数据集的规模和复杂性是相当大的，需要新的方式和方法来进行分析和解释。与物理学和基因组学等其他研究领域一样，新数据的可用性导致社区努力解决数据分析问题。研究人员可以使用许多算法和软件工具，但在神经科学实验室中有效使用它们仍然需要技术专业知识，而这些知识并不总是容易获得的。此外，实验室通常实施手工制作的分析工作流程，这些工作流程不具有可移植性，并且随着原始开发人员的迁移可能难以维护。这反过来又使得难以集成新的开发，并且可能限制可重复性，因为结果可能取决于分析工作流程中特定但未记录的元素。为了解决这个问题，我们开发了SpikeInterface，这是一个软件框架，可以统一访问数据以及用于数据处理和分析的主要现有算法和工具。这个成功的开源项目已被用于支持数据密集型研究的分析，并收到了来自研究界的许多贡献。在这里，我们建议构建SpikeInterface来解决两个主要的开放问题：1）如何自动化数据管理; 2）如何抽象工作流，以便在没有专业编程知识的情况下可以重复和设计。数据管理是分析工作流的重要组成部分，因为现有的算法（例如隔离单个神经元的活动）是不完善的。通常，这是一个耗时的手动过程，严重限制了实验室实际工作的数据量。为了提高吞吐量和可重复性，我们将开发新的机器学习方法来自动化数据管理。为了提高这些方法和SpikeInterface中许多其他方法的可重复性和可访问性，我们将添加处理工作流抽象表示的功能。这将使分析工作流程的完整出处易于记录，并使数据分析能够在无需编写程序代码的情况下创建和运行。反过来，这将成为基于Web浏览器的用户界面的基础，以设计，测试和执行工作流程。SpikeInterface已经实现了灵活的数据访问和流水线，这将允许分散的解决方案，例如，分析可以在本地、远程机器或基于云的服务上运行。此外，项目小组将支持研究界采用和使用这些工具，并将继续维护和改进SpikeInterface软件。这一努力将使目前采用大规模记录技术的数千个神经科学实验室能够使用尖端的分析方法，它将使大型数据集的协作分析成为可能，并简化有价值数据集的共享和重复使用，以进一步发现。