美国北卡罗来纳大学教堂山分校Andrea Giovannucci团队近期取得重要工作进展，他们研究开发了FIOLA工具，这是一种荧光成像在线分析的加速管道。相关研究成果2023年9月7日在线发表于《自然—方法学》杂志上。
据介绍，光学显微镜方法，如钙和电压成像，能够使用光快速读出大型神经元群体的活动。然而，在线算法缺乏相应的进步，减缓了在实验期间或实验后不久检索神经活动信息的进展。这种差距不仅阻碍了实时闭环实验的执行，而且阻碍了高通量成像模式的快速实验-分析-理论转换。以与指示剂动力学和成像模式兼容的速度从荧光成像帧中可靠地提取神经活动是一个挑战。
因此，研究人员开发了FIOLA工具，这是一种荧光成像在线分析框架，可以从钙和电压成像影片中提取神经元活动，其速度比最先进的方法快一个数量级。FIOLA利用针对GPU和CPU上的并行处理而优化的算法。研究人员在模拟和真实的钙和电压成像数据集上展示了FIOLA的可靠和可扩展性能。
最后，研究人员提出了一个在线实验场景，为设置FIOLA参数提供指导，并强调这一方法的权衡。
附：英文原文
Title: FIOLA: an accelerated pipeline for fluorescence imaging online analysis
Author: Cai, Changjia, Dong, Cynthia, Friedrich, Johannes, Rozsa, Marton, Pnevmatikakis, Eftychios A., Giovannucci, Andrea
Issue&Volume: 2023-09-07
Abstract: Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment–analysis–theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach. FIOLA is a pipeline for processing calcium or voltage imaging data. Its advantages include the fast speed and online processing.
DOI: 10.1038/s41592-023-01964-2
Source: https://www.nature.com/articles/s41592-023-01964-2
Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex
本期文章：《自然—方法学》：Online/在线发表
