An integrated workflow for long-term fiber photometry analysis.

Authors

Farina Pourmir, Jordan N Cook, Samantha O Sweck, Jeff R Jones

Published in

eNeuro. Sep 24, 2026. Epub Sep 24, 2026.

Abstract

Long-term fiber photometry enables measurement of neural dynamics across hours to days, but these recordings create analytical and reproducibility challenges that are not well addressed by tools developed for short, stimulus-locked experiments. Here we present a software environment for long-term photometry analysis organized around a structured guided workflow for configuring, reviewing, executing, and inspecting analyses. The workflow makes consequential analysis choices visible by allowing correction strategies and event-detection settings to be previewed and configured for individual regions of interest before analysis. It supports both intermittent and continuous recordings and provides complementary views of corrected ΔF/F, detected-event summaries, and slow-signal structure across session-level and multiday timescales. We illustrate how correction choice can substantially alter the resulting ΔF/F under challenging signal-reference conditions. We also demonstrate long-term analysis of recordings obtained with calcium, acetylcholine, and dopamine sensors in male mice. Together, these capabilities provide a practical framework for reproducible analysis of long-term fiber photometry recordings.Significance statement Most fiber photometry analysis tools were developed for short, event-driven experiments rather than for recordings collected over hours, days, or even weeks. Long-term datasets can contain hundreds or thousands of recording intervals, making exhaustive trace-by-trace inspection impractical and increasing the importance of consistent approaches to signal correction, event detection, and temporal organization. We present a guided software workflow that makes these analysis choices visible and reviewable while organizing long-duration recordings into interpretable session-level and multiday outputs.

PMID:
42785985
Bibliographic data and abstract were imported from PubMed on 25 Sep 2026.

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