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An integrated workflow for long-term fiber photometry analysis.

Farina Pourmir1, Jordan N Cook2, Samantha O Sweck2

  • 1Department of Biology, Texas A&M University, College Station, TX, USA.

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Summary

This study introduces new software for analyzing long-term fiber photometry data, improving reproducibility and interpretability. The tool offers a structured workflow for neural dynamics analysis across extended recording periods.

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Area of Science:

  • Neuroscience
  • Computational Biology

Background:

  • Long-term fiber photometry allows neural dynamics measurement over extended periods (hours to days).
  • Existing analytical tools are often inadequate for long-term recordings, posing challenges for reproducibility.

Purpose of the Study:

  • To present a novel software environment for the analysis of long-term fiber photometry data.
  • To establish a structured, revisitable workflow addressing analytical and reproducibility challenges.

Main Methods:

  • Developed a software environment with a structured workflow for data analysis.
  • Separated signal correction from downstream event reanalysis for iterative refinement.
  • Enabled inspection of recordings at multiday and session-level scales.

Main Results:

  • Correction choices significantly impact the corrected neural signal.
  • Post-run reanalysis can alter event-detection outcomes.
  • The software preserves both tonic and phasic neural outputs.

Conclusions:

  • The software provides a practical workflow for interpretable, revisitable, and reproducible long-term photometry analysis.
  • Facilitates robust investigation of neural dynamics over extended durations.