FiPhoPHA-A Fiber Photometry Python Package for Post Hoc Analysis.

Abstract

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 analyze data in an unbiased, objective, and easily adopted manner are lacking. Various pipelines for data analysis exist, but they are often system specific, are only for preprocessing 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 standardized 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 with 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 standardized post hoc statistical analysis to improve reproducibility using data from any fiber photometry system.