Optimized workflow for behavior-coupled fiber photometry experiment: improved data navigation and accessibility
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1601127/full |
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| Summary: | 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. |
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| ISSN: | 1662-453X |