Athanassi Anna, François Amaury, Bourinet Emmanuel, Thevenet Marc, Mandairon Nathalie
CNRS, UMR 5292, INSERM, U1028, Lyon Neuroscience Research Center, Neuroplasticity and Neuropathology of Olfactory Perception Team, University of Lyon, Lyon, France.
CNRS, UMR5203, INSERM, Institut of Functional Genomics, University of Montpellier, Montpellier, France.
Front Neurosci. 2025 Jul 10;19:1601127. doi: 10.3389/fnins.2025.1601127. eCollection 2025.
Fiber photometry provides crucial insights into cell population activity underlying behavior. While numerous open-source data analysis tools exist, few offer an automated workflow that streamlines the analysis of fiber photometry data alongside behavioral measurements, by enabling more intuitive and facilitated navigation within data files. We developed here a workflow starting from the intracerebral implantation of optical fibers in mice, to the analysis of fiber photometry signals, aligned with recorded behavioral data. This tool is particularly valuable for studying unpredictable exploratory behaviors, as it allows efficient and rapid revisiting of fiber photometry signals aligned to spontaneous behavioral changes. Our approach allows ease of data analysis and exploration using custom algorithms and scripts that extract and process both fiber photometry and behavioral data, without relying on predefined event markers. We validated our method by assessing calcium activity and dopaminergic dynamics in the olfactory tubercle in response to spontaneous investigation of attractive and non-attractive odorants in freely moving adult C57BL/ 6J mice. Using jRGECO1a and dLight1.2 biosensors, we revealed distinct dopamine responses to attractive versus unattractive odorants while calcium transmission remained similar. Overall, our method significantly enhances the accessibility and efficiency of data analysis, allowing for rapid retrieval and exploration of key behavioral time points. Its adaptability makes it suitable for a wide range of spontaneous behaviors, paradigms, and sensory modalities, facilitating deeper insights into complex neural dynamics.
纤维光度法为理解行为背后的细胞群体活动提供了至关重要的见解。虽然存在众多开源数据分析工具，但很少有工具能提供一个自动化工作流程，通过在数据文件中实现更直观、便捷的导航，将纤维光度法数据的分析与行为测量结合起来进行简化。我们在此开发了一个工作流程，从在小鼠脑内植入光纤开始，到分析与记录的行为数据对齐的纤维光度法信号。该工具对于研究不可预测的探索行为特别有价值，因为它允许高效、快速地重新审视与自发行为变化对齐的纤维光度法信号。我们的方法使用自定义算法和脚本轻松地进行数据分析和探索，这些算法和脚本可以提取和处理纤维光度法和行为数据，而无需依赖预定义的事件标记。我们通过评估成年C57BL/6J自由活动小鼠在自发探究有吸引力和无吸引力气味剂时嗅结节中的钙活性和多巴胺能动态，验证了我们的方法。使用jRGECO1a和dLight1.2生物传感器，我们发现对有吸引力和无吸引力气味剂的多巴胺反应不同，而钙传递保持相似。总体而言，我们的方法显著提高了数据分析的可及性和效率，允许快速检索和探索关键行为时间点。其适应性使其适用于广泛的自发行为、范式和感觉模态，有助于更深入地洞察复杂的神经动力学。
