ArticleNeurophotonics2025
Obtaining artifact-corrected signals in fiber photometry via isosbestic signals, robust regression, and dF/F calculations.
Article in Neurophotonics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Perceptual training selectively strengthens top-down signaling to sensory cortex.bioRxiv : the preprint server for biology · 2026Article
- Nucleus accumbens medial shell D1 and D2 medium spiny neurons signal the valence of conditioned stimuli in a sex-dependent manner.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Disinhibition of ventral tegmental area during initial punishment learning causes enduring punishment insensitivity.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Review
- Development of a genetically encoded melanocortin sensor for high sensitivity in vivo imaging.Molecular metabolism · 2025Article
- PASTa: An Open-Source Analysis and Signal Processing Toolbox for Fiber Photometry Data.Current protocols · 2025Article
- Obtaining artifact-corrected signals in fiber photometry via isosbestic signals, robust regression, and dF/F calculations.Neurophotonics · 2025Article
- Fiber photometry in neuroscience research: principles, applications, and future directions.Pharmacological reports : PR · 2024Review
- Event boundaries drive norepinephrine release and distinctive neural representations of space in the rodent hippocampus.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: Fiber photometry is a powerful tool for neuroscience. However, measured biosensor signals are contaminated by various artifacts (photobleaching and movement-related noise) that undermine analysis and interpretation. Currently, no universal pipeline exists to deal with these artifacts. Aim: We aim to evaluate approaches for obtaining artifact-corrected neural dynamic signals from fiber photometry data and provide recommendations for photometry analysis pipelines. Approach: Using simulated and real photometry data, we tested the effects of three key analytical decisions: choice of regression for fitting isosbestic control signals onto experimental signals [ordinary least squares (OLS) versus iteratively reweighted least squares (IRLS)], low-pass filtering, and dF/F versus dF calculations. Results: IRLS surpassed OLS regression for fitting isosbestic control signals to experimental signals. We also demonstrate the efficacy of low-pass filtering signals and baseline normalization via dF/F calculations. Conclusions: We conclude that artifact-correcting experimental signals via low-pass filter, IRLS regression, and dF/F calculations is a superior approach to commonly used alternatives. We suggest these as a new standard for preprocessing signals across photometry analysis pipelines.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.