RT Journal Article
SR Electronic
T1 FiPhoPHA - A fiber photometry python package for post-hoc analysis
JF eneuro
JO eNeuro
FD Society for Neuroscience
SP ENEURO.0221-25.2025
DO 10.1523/ENEURO.0221-25.2025
A1 Drakopoulos, Vasilios
A1 Reichenbach, Alex
A1 Stark, Romana
A1 Foldi, Claire J.
A1 Jean-Richard-dit-Bressel, Philip
A1 Andrews, Zane B.
YR 2025
UL http://www.eneuro.org/content/early/2025/07/28/ENEURO.0221-25.2025.abstract
AB Fiber photometry is a neuroscience technique that can continuously monitor in vivo fluorescence to assess population neural activity or neuropeptide/transmitter release in freely behaving animals. Despite the widespread adoption of this technique, methods to statistically analyse data in an unbiased, objective, and easily adopted manner are lacking. Various pipelines for data analysis exist, but they are often system-specific, only for pre-processing data, and/or lack usability. Current post hoc statistical approaches involve inadvertently biased user-defined time-binned averages or area under the curve analysis. To date, no post-hoc user-friendly tool with few assumptions for a standardised unbiased analysis exists, yet such a tool would improve reproducibility and statistical reliability for all users. Hence, we have developed a user-friendly post hoc statistical analysis package in Python that is easily downloaded and applied to data from any fiber photometry system. This Fiber Photometry Post Hoc Analysis (FiPhoPHA) package incorporates a variety of tools, a downsampler, bootstrapped confidence intervals (CIs) for analyzing peri-event signals between groups and compared to baseline, and permutation tests for comparing peri-event signals across comparison periods. We also include the ability to quickly and efficiently sort the data into mean time bins, if desired. This provides an open-source, user-friendly python package for unbiased and standardised post-hoc statistical analysis to improve reproducibility using data from any fiber photometry system.Significance Statement Despite the widespread adoption of in vivo photometry for neuroscience research, methods to statistically analyse data in an unbiased, objective, and easily adopted manner are lacking. Various pipelines for data analysis exist, but they are often system-specific, only for pre-processing data, and/or lack usability. Current post hoc statistical approaches involve inadvertently biased user-defined time-binned averages or area under the curve analysis. Here, we have developed a standardised post hoc statistical analysis package in Python that is easily downloaded and applied to data from any fiber photometry system. This provides an open-source, user-friendly python package for unbiased and standardised post-hoc statistical analysis to improve reproducibility using data from any fiber photometry system.