FiPhA: An Open-Source Platform for Fiber Photometry Analysis

by Matthew F. Bridge, Leslie R. Wilson, Sambit Panda, Korey D. Stevanovic, Ayland C. Letsinger, Sandra McBride, and Jesse D. Cushman
in bioRxiv on July, 2023


Significance: Fiber photometry is a widely used technique in modern behavioral neuroscience, employing genetically encoded fluorescent sensors to monitor neural activity and neurotransmitter release in awake-behaving animals, However, analyzing photometry data can be both laborious and time-consuming.

Aim: We propose the FiPhA (Fiber Photometry Analysis) app, which is a general-purpose fiber photometry analysis application. The goal is to develop a pipeline suitable for a wide range of photometry approaches, including spectrally resolved, camera-based, and lock-in demodulation.

Approach: FiPhA was developed using the R Shiny framework and offers interactive visualization, quality control, and batch processing functionalities in a user-friendly interface.

Results: This application simplifies and streamlines the analysis process, thereby reducing labor and time requirements. It offers interactive visualizations, event-triggered average processing, powerful tools for filtering behavioral events and quality control features.

Conclusions: FiPhA is a valuable tool for behavioral neuroscientists working with discrete, event-based fiber photometry data. It addresses the challenges associated with analyzing and investigating such data, offering a robust and user-friendly solution without the complexity of having to hand-design custom analysis pipelines. This application thus helps standardize an approach to fiber photometry analysis.


  title = {{{FiPhA}}: {{An Open-Source Platform}} for {{Fiber Photometry Analysis}}},
  shorttitle = {{{FiPhA}}},
  author = {Bridge, Matthew F. and Wilson, Leslie R. and Panda, Sambit and Stevanovic, Korey D. and Letsinger, Ayland C. and McBride, Sandra and Cushman, Jesse D.},
  year = {2023},
  month = jul,
  publisher = {{bioRxiv}},
  doi = {10.1101/2023.07.21.550098},
  archiveprefix = {bioRxiv},
  copyright = {\textcopyright{} 2023, Posted by Cold Spring Harbor Laboratory. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license},
  langid = {english}