ArticleNeurophotonics2024
FiPhA: an open-source platform for fiber photometry analysis.
Article in Neurophotonics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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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Who cites it
8 citing papers in PubMed.
- Methods for measuring neural activity during voluntary wheel running.Journal of neuroscience methods · 2026Article
- Attenuation of social preference and alteration of cortical neurons following adolescent cerebellar nuclei perturbation.Molecular psychiatry · 2026Article
- Lateral hypothalamic melanin-concentrating hormone neuron dynamics in rats during sensory stimulation and sugar sweetened alcoholic cocktail drinking.bioRxiv : the preprint server for biology · 2026Article
- Calcium Imaging in Vivo: How to Correctly Select and Apply Fiber Optic Photometric Indicators.Organogenesis · 2025Review
- Article
- PASTa: An Open-Source Analysis and Signal Processing Toolbox for Fiber Photometry Data.Current protocols · 2025Article
- Fiber photometry in neuroscience research: principles, applications, and future directions.Pharmacological reports : PR · 2024Review
- Special Section Guest Editorial: Open-source neurophotonic tools for neuroscience.Neurophotonics · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Significance: Fiber photometry (FP) is a widely used technique in modern behavioral neuroscience, employing genetically encoded fluorescent sensors to monitor neural activity and neurotransmitter release in awake-behaving animals. However, analyzing photometry data can be both laborious and time-consuming. Aim: We propose the fiber photometry analysis (FiPhA) app, which is a general-purpose FP analysis application. The goal is to develop a pipeline suitable for a wide range of photometry approaches, including spectrally resolved, camera-based, and lock-in demodulation. Approach: FiPhA was developed using the R Shiny framework and offers interactive visualization, quality control, and batch processing functionalities in a user-friendly interface. Results: This application simplifies and streamlines the analysis process, thereby reducing labor and time requirements. It offers interactive visualizations, event-triggered average processing, powerful tools for filtering behavioral events, and quality control features. Conclusions: FiPhA is a valuable tool for behavioral neuroscientists working with discrete, event-based FP data. It addresses the challenges associated with analyzing and investigating such data, offering a robust and user-friendly solution without the complexity of having to hand-design custom analysis pipelines. This application thus helps standardize an approach to FP analysis.
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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.