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We developed an affordable, open-source fiber photometry system for recording neural activity. This system offers improved signal quality and is adaptable for diverse neuroscience research applications.

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Area of Science:

  • Neuroscience
  • Biotechnology
  • Open-source hardware and software development

Background:

  • Fiber photometry is a key technique for measuring bulk neural activity by detecting fluorescence changes in indicator proteins like GCaMP via optical fibers.
  • Existing commercial systems can be costly and lack flexibility for diverse research needs.

Purpose of the Study:

  • To present a novel, low-cost, open-source hardware and software system for fiber photometry data acquisition.
  • To provide researchers with an accessible and adaptable tool for neural activity monitoring.

Main Methods:

  • Developed a compact data acquisition board based on the Micropython microcontroller.
  • Created a cross-platform graphical user interface (GUI) for system control and signal visualization.
  • Implemented time-division multiplexed illumination for independent readout of fluorescence signals from multiple excitation wavelengths using a single photoreceiver.

Main Results:

  • The system successfully acquires analog and digital signals and controls external LEDs.
  • Time-division multiplexed illumination demonstrated superior signal-to-noise ratio compared to sinusoidal modulation at equivalent light intensities.
  • The pyPhotometry system is significantly more cost-effective than comparable commercial solutions.

Conclusions:

  • The presented open-source fiber photometry system offers a cost-effective and adaptable solution for neural activity recording.
  • Its design facilitates broad adoption and customization across various neuroscience research fields.
  • This tool democratizes access to advanced neural recording technologies.