ArticleeNeuro2026
An Integrated Workflow for Long-Term Fiber Photometry Analysis.
Article in eNeuro, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
Funding
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 Δ
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.