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PASTa: An Open-Source Analysis and Signal Processing Toolbox for Fiber Photometry Data.

Rachel M Donka1, Maxine K Loh1, Vaibhav R Konanur2

  • 1Department of Psychology, University of Illinois Chicago, Chicago, Illinois.

Current Protocols
|July 3, 2025
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Summary
This summary is machine-generated.

We developed PASTa, a user-friendly MATLAB toolbox for analyzing fiber photometry data. This tool simplifies neural signal processing and transient event detection for researchers.

Keywords:
fiber photometryin vivoopen‐sourcesoftwaretoolbox

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Area of Science:

  • Neuroscience
  • Bioengineering
  • Computational Biology

Background:

  • Fiber photometry is crucial for real-time neural activity recording in behaving subjects.
  • Existing analysis tools for fiber photometry data are often inflexible, inconsistent, and difficult for new users.
  • Recent sensor advancements create challenges in signal and control stream fluorescence interpretation.

Purpose of the Study:

  • To introduce PASTa (Photometry Analysis and Signal Processing Toolbox), an open-source MATLAB-based solution.
  • To provide a comprehensive and user-friendly pipeline for fiber photometry data analysis.
  • To improve the reliability of neural signal processing and transient event detection.

Main Methods:

  • Developed PASTa, an open-source MATLAB toolbox with a full analysis pipeline.
  • Implemented customizable parameters for signal processing and transient event detection.
  • Annotated code for readability, accessibility, and adaptability for novice users.

Main Results:

  • PASta offers a standardized protocol for fiber photometry data processing.
  • The toolbox enables reliable detection and characterization of neural transient events.
  • PASta accommodates diverse experimental designs and sensor types.

Conclusions:

  • PASta provides a customizable and accessible platform for fiber photometry analysis.
  • The toolbox addresses limitations of existing methods, enhancing data interpretation.
  • Future updates will integrate novel signal processing methods for broader applicability.