University of Bordeaux, INSERM, Neurocentre Magendie, U1215, F-33000, Bordeaux, France.
UCL, Sainsbury Wellcome Centre, London, UK.
Sci Rep. 2023 Oct 2;13(1):16562. doi: 10.1038/s41598-023-43565-1.
Despite the popularity of fiber photometry (FP), its integration with operant behavior paradigms is progressing slowly. This can be attributed to the complex protocols in operant behavior - resulting in a combination of diverse non-predictable behavioral responses and scheduled events, thereby complicating data analysis. To overcome this, we developed Pyfiber, an open-source python library which facilitates the merge of FP with operant behavior by relating changes in fluorescent signals within a neuronal population to behavioral responses and events. Pyfiber helps to 1. Extract events and responses that occur in operant behavior, 2. Extract and process the FP signals, 3. Select events of interest and align them to the corresponding FP signals, 4. Apply appropriate signal normalization and analysis according to the type of events, 5. Run analysis on multiple individuals and sessions, 6. Collect results in an easily readable format. Pyfiber is suitable for use with many different fluorescent sensors and operant behavior protocols. It was developed using Doric lenses FP systems and Imetronic behavioral systems, but it possesses the capability to process data from alternative systems. This work sets a solid foundation for analyzing the relationship between different dimensions of complex behavioral paradigms with fluorescent signals from brain regions of interest.
尽管光纤光度测定法(FP)很受欢迎,但它与操作性行为范式的整合进展缓慢。这可以归因于操作性行为中的复杂协议-导致各种不可预测的行为反应和预定事件的组合,从而使数据分析变得复杂。为了克服这一问题,我们开发了 Pyfiber,这是一个开源的 Python 库,通过将神经元群体中荧光信号的变化与行为反应和事件相关联,促进了 FP 与操作性行为的融合。Pyfiber 有助于:1. 提取操作性行为中发生的事件和反应,2. 提取和处理 FP 信号,3. 选择感兴趣的事件并将其与相应的 FP 信号对齐,4. 根据事件类型应用适当的信号归一化和分析,5. 对多个个体和会话进行分析,6. 以易于阅读的格式收集结果。Pyfiber 适用于许多不同的荧光传感器和操作性行为协议。它是使用 Doric 透镜 FP 系统和 Imetronic 行为系统开发的,但它具有处理来自替代系统的数据的能力。这项工作为分析不同维度的复杂行为范式与来自感兴趣脑区的荧光信号之间的关系奠定了坚实的基础。