Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be challenging for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is designed to operate across computing platforms and can accept data from a variety of FP data acquisition systems. The program presents users with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs are produced that can be easily exported for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.

Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.

Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be a challenge for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is provided as a Jupyter notebook, a well-commented interactive development environment (IDE) designed to operate across platforms. GuPPy presents the user with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs produced by GuPPy can be exported into various image formats for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.